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We will start with number system and then move towards algebra. After which we will study coordinate geometry. We will also study concepts of trigonometry and mensuration. Lastly, we will study Statistics and Probability.

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These solutions are arranged chapterwise and exercise wise as. You can use these solutions to develop your skills and knowledge. What is circumference of a circle? The total length of boundary of a circle is called circumference of a circle.

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The additional models estimated for each outcome and shown in Tables 3 and 4 offer other key findings. In Model 1, we used the full samples for the two cohorts but did not include any controls that capture characteristics of children or their parents or the early education practices in which families engage.

Model 2 partitions the data into schools and classes, or clusters, so that the subjects in the clusters are more similar to one another than to those in other groups.

Under this adjustment, the gaps shrink substantially, by between 15 and 25 percent across the skills, and the regression fit improves significantly see increased adjusted R-squared, i. This clustering takes into account school segregation, that is, that children are not randomly distributed but tend to concentrate in schools or classrooms with children of the same race, social class, etc.

Clustered estimates provide a comparison of the skills gaps of peer students�those in the same schools and classrooms�rather than a comparison across schools. We next examine the contribution of the certain variables of interest to SES-based performance gaps. We approach this in two ways. First, we examine the changes in the gaps Tables 3 and 4, Models 3 and 4 and the overall reduction in the gaps that results from controlling for children and their family characteristics, early literacy practices, and parental expectations of educational achievement Table 5.

Second, we assess the influence of select early educational practices on performance and how that influence has changed over time by looking at the associations between these inputs and performance Table 6.

Models 3 and 4 in Tables 3 and 4 use the samples that result from removing observations without full information for the controls of interest. Model 3 also includes the interactions between the early education variables with time.

Including covariates changes the estimates of SES-based skills gaps in various ways. First, the gaps between the top- and bottom-SES quintiles shrink, showing that SES-based gaps are partially explained by the variation in the controls which is not visible in the tables. In reading, the change in the gap between and diminishes and becomes statistically insignificant in the last model the relative gap increases by 0.

Gaps between high- and low-SES children in cognitive and noncognitive skills after adjustments are made are shown in Figure B. As mentioned above, the fact that the skills gaps decrease after controls are taken into consideration affirms that SES-based gaps are due in part to variation in the controls among high- versus low-SES children. This trend can be seen in Table 5, which, as noted above, shows the overall reduction in gaps that results from controlling for child and family characteristics, early literacy practices, and parental expectations of educational achievement.

With respect to cognitive skills, the gaps shrink by 46 percent and 53 percent, respectively, after the inclusion of the covariates. About half of the gaps are thus due to other factors that are associated both with SES status and with the outcomes themselves. The reduction in the gaps for noncognitive skills varies from 28 percent approaches to learning as reported by teachers to 74 percent approaches to learning as reported by parents.

For self-control as reported by teachers, the reduction is 51 percent versus 35 percent when reported by parents. While the gaps hold after the inclusion of controls across outcomes, gaps in are less sensitive to the inclusion of the covariates than they were in This trend can also be seen in Table 5. In any event, the resistance of gaps to these controls should worry researchers and policymakers.

The waning influence of these controls makes it harder to understand what drives SES gaps. It also suggests that the gaps may be growing more intractable or, at least are less easily narrowed via the enactment of known policy interventions.

For , the estimated coefficients are 0. However, while center-based pre-K continues to reduce self-control as reported by teachers in , the effect is less negative in the 0. We find no independent effect of center-based prekindergarten schooling i. The number of books children have at home likewise supports their skills at the beginning of kindergarten. Indeed, this factor is positively associated with all outcomes but self-control reported by parents.

The coefficients are very small, of about 0. The associations with cognitive skills, especially with reading, are strong and statistically significant�0. For noncognitive skills, the relationships are strong for those assessed by parents, though they shrink by about half over time: self-control is 0.

On the other hand, the index that measures other enrichment activities that parents do with their children a composite of how frequently parents and children play games, do sports, build things, work on puzzles, do arts and crafts, and do chores shows significant correlations with all of the skills, but they may be either positively correlated or negatively correlated, depending on the skill.

For cognitive skills, the associations are statistically significant and negative, though stronger and somewhat more meaningful or more intense with reading achievement But the fact that both the frequency with which parents engage in most of these activities and the importance of this index for parent-assessed skills increased noticeably from to by 0. It also indicates that parents are increasingly acting on this knowledge and that this involvement will continue to grow, albeit potentially with decreasing marginal returns to time and resources invested.

In addition, most of these associations�particularly the cognitive gradients�grow in We thus conduct analyses using several of the main proxies employed to measure socioeconomic status.

The purpose of these analyses is twofold. The first purpose is to test the sensitivity of the estimated relative gaps, and of trends in them, to changes in the measurement of this key predictor of education performance. In other words, if all the indicators are reliable proxies of SES, gaps and trends obtained using the various metrics should be similar.

The second purpose is to increase the comparability of the results of studies addressing trends in education inequalities that use various metrics of social class. This is an important issue; in addition to helping reconcile diverse results found in the literature, these analyses may reveal why patterns differ, and have significant policy implications. Unlike the SES composite measure, two of these measures offer the advantage of being directly comparable over time.

As a limitation, and mainly associated with the information that is available in the raw data, none of these categories can be transformed into a percentile-variable without major transformations. The adjustments to ensure comparability over time are explained in Appendix A. See Reardon and Portilla for an analysis with a transformation of the income variable that offers a proper percentile comparison, based on the methodology developed by Reardon Still, they are variables associated with social class and can be ordered in groups or categories that identify high- and low-social-class statuses.

Thus, with the necessary caution when interpreting and using the findings, we offer this comparison of results as a sensitivity analysis.

For simplicity, Tables 7�9 show only the results from two models: one without covariates Model 1, baseline estimates and one with all covariates Model 4, fully adjusted estimates. We focus on the findings for the baseline relative gaps in and first Figures C�E. The overall patterns found in the results suggest that all social-class gaps are statistically significant and sizable. However, the exact sizes of the gaps vary depending on the social-class indicator used and the outcome being assessed.

In addition to these general findings, we note some more detailed ones. For example, the relative gap is 1. All in all, results seem internally consistent as well as generally consistent with prior results on this topic Reardon and Portilla Changes in the performance gaps in cognitive skills between and by our composite SES measure and books are similar: an increase in the reading gap between children in the top and bottom quintiles of about a tenth of a standard deviation 0.

Meanwhile, income-based gaps for the two cognitive skills�reading and math�decreased by No significant changes occurred for the remaining noncognitive skills. In sum, this sensitivity analysis demonstrates that all of the indicators are reliable proxies of SES for the estimation of early achievement gaps, though absolute gaps may vary slightly depending on the indicator used.

However, the proxies are not equally reliable when we assess trends in the gaps by SES or their drivers. As such, aside from differences in the definitions and procedures used to construct each SES proxy, the proxies should not be treated as fully equivalent. The decomposition conducted here helps clarify the different weights that various components of SES may have in driving changes in gaps by social class.

For example, variation in income across groups over time is associated with decreased performance gaps in the cognitive skills between and , and variation in educational attainment quintiles or categories over time is associated with decreased performance gaps across cohorts in most noncognitive skills. On the other hand, findings that indicate that income inequality is the larger culprit would point to the need for policies that reduce such inequalities.

Future research should consider and look more closely into these questions. The multiple factors and relationships examined in this section can now be examined from a policy perspective. This section of the report draws on a set of case studies published by the Broader, Bolder Approach to Education BBA , a national campaign that advances evidence-based strategies to mitigate the impacts of poverty-related disadvantages on teaching and learning.

We explore the premise that school districts that take a whole-child approach to education and a whole-community approach to delivering it are likely to enjoy larger gains in academic achievement and to narrow their race- and income-based achievement gaps.

Large and growing disparities in the economic well-being of children in America and extensive evidence linking those disparities to widely diverging educational outcomes have prompted action among a growing number of communities and school districts.

Heeding the evidence that out-of-school factors play even larger roles than school-based factors in school performance, these districts are seeking ways to mitigate the poverty-related impediments to effective teaching and learning. These districts have benefited from a substantial body of research on strategies with promise to address core challenges that students and schools face�strategies that have been shown to shrink achievement gaps by narrowing major disparities in opportunity Carter and Welner The first, and perhaps best-documented, of these strategies is high-quality early child care and education, especially when it engages parents early and in meaningful ways.

High-quality early childhood education programs not only narrow achievement gaps at kindergarten entry but also deliver long-term benefits to children, their families, and society as a whole Chaudry et al. While the impact of such comprehensive approaches has not been studied as extensively as the individual components, considerable theoretical and emerging empirical research point to the strong potential of such strategies to boost achievement and narrow gaps Child Trends ; Oakes, Maier, and Daniel ; Weiss i.

This section of the report seeks to add to that knowledge base by sharing qualitative information on how such comprehensive approaches have emerged and grown, what they look like when they are successfully implemented, and what types of outcomes and benefits result and how outcomes vary across diverse communities.

Each of the districts studied has distinct circumstances, and thus distinct reasons for coming to the conclusion, as a community, that it needed to take a comprehensive approach to education. At the same time, demographic trends that are affecting virtually every state�and many, if not most, school districts across the country�have played major roles in that decision in every case.

Across the country, in Vancouver, Washington, the share of children eligible for subsidized school meals rose from 39 percent to over 50 percent in less than a decade, such that, by , in some central-city schools, more than four in five students qualified for subsidized school meals in Weiss b. And in the early s, the Tangelo Park neighborhood in Orlando, Florida�an isolated enclave of 3, residents, almost all low-income and African American�caught the attention of hotelier and philanthropist Harris Rosen, who was looking for a neighborhood in which to invest Alvarez One of the most politically progressive of the districts studied, Montgomery County Public Schools MCPS in Maryland, paved the way for a whole-child approach in the early s when it enacted housing policy that uses mixed-income residential developments to create communities with families of different income levels.

Community schools are known for building partnerships with community agencies and private service providers to meet student and family needs. Austin Independent School District AISD , also in a politically progressive jurisdiction, began its whole-child efforts through parent- and community-organizing in schools. It has since invested in social and emotional learning and in a community schools strategy CASEL At the other end of the spectrum are whole-child approaches in Joplin, Missouri, and Pea Ridge, Arkansas, districts located in more politically conservative southern states.

The Northside Achievement Zone in Minneapolis is funded through a grant from the federal Promise Neighborhoods initiative, enacted by the Obama Administration to help more communities dramatically improve the academic success for low-income children by adopting HCZ-like strategies.

Districts also take different approaches based on density. Cultural offerings to supplement other well-rounded services are also part of the full-service community schools district initiative in Vancouver, Washington. In contrast, Partners for Education, which serves the isolated region surrounding Berea College in Kentucky, was the first rural organization to receive a Promise Neighborhood grant and, thus, is a pioneer in exploring how well the model works outside the urban context Berea College In keeping with their whole-child approaches to education policy and practice, every one of the 12 districts highlighted as a BBA case study has made investments in early childhood care and education, many of them substantial.

Investing in babies by engaging parents can include providing new parents with key information about child development and how to keep children healthy and safe. The districts leverage partnerships to connect parents with a range of school and community resources that support children from birth through kindergarten entry.

Educating and engaging parents early helps prepare children for school both academically and more broadly for healthy development. Those are the twin goals of the Minneapolis Northside Achievement Zone NAZ , where currently only one in four preschoolers in the zone is ready for kindergarten based on standardized tests.

Almost every district studied provides new-parent classes. The Grow and Learn program is a weekly minute literacy-rich program for young children and their parents offered at 12 elementary schools in high-poverty Vancouver neighborhoods. It lays the foundations for school readiness through social and education experiences. In many cases, districts employ a combination of one-on-one and group supports, along the lines of Early Head Start.

In Montgomery County, Maryland, family social workers collaborate with classroom teachers to help them develop Family Partnership Agreements, which are based on the strengths, needs, and personal goals of each family. A social worker�led team follows up by phone and with visits. Almost every state in the country now invests at least minimally in pre-K programs for disadvantaged children, and a growing share of states make these programs widely available.

A few of these districts benefit from high-quality state pre-K programs that serve a large share of children, freeing the districts to invest in other aspects of early childhood enrichment. The Partners for Education initiative based in Berea, Kentucky, leverages the state pre-K program, which serves all three- and four-year olds who are either low-income or have other risk factors.

This enables Partners for Education to use Promise Neighborhood grant funds to place early childhood specialists in pre-K classrooms throughout the four-county region the region is a Promise Neighborhood region, which means that federal funds are available for a variety of education- and health-related investments.

The specialists also provide coaching, professional development, and support for Head Start classrooms, as well as in-home tutoring over the summer. In East Durham, North Carolina, strong state early education programs are supplemented by partner-led low-cost half-day preschool and a summer kindergarten readiness program, and home visits by parent advocates provide a range of supports, such as connections to state pre-K. In Kalamazoo, Michigan, the Pre-Kindergarten Early Education Program PEEP offers half- or full-day pre-K classes in elementary schools for four-year-olds at or below percent of the federal poverty level, per state law, but it adds transportation and meals for those children.

Other districts with less comprehensive state support use federal resources to expand local options. For example, Vancouver draws on both state and federally funded early learning programs to provide pre-K in seven schools, along with district-supported programs for children in Title I schools.

Montgomery County also enhances state and federal programs with district-level investments: it provides the same literacy-rich curriculum in its Head Start classrooms as in district pre-K classrooms. And Montgomery County uses a blend of federal Title I and Head Start dollars to offer full-day Head Start in 18 of the poorest schools, serving children Marietta The Northside Achievement Zone in north Minneapolis uses federal Race to the Top Early Learning Fund money for scholarships for three- and four-year-olds to attend high-quality pre-K, serving children in � and in � Local programs can also fill in where state programs are weak.

Austin, Texas, uses local funds to provide enriching, hands-on full-day programs for the four-year-olds who would otherwise participate in lower-quality half-day state programs. Pea Ridge is another community using local resources to supplant state resources. Featured districts also build on pre-K gains and help narrow school-readiness gaps with such programs as full-day kindergarten. Full-day kindergarten has since expanded to every school in the district Marietta And Vancouver offers Kindergarten Jump Start, a school readiness program, at all 21 elementary schools, and full-day kindergarten; both programs seek to enhance the transition from pre-K into formal schooling.

In addition to the above range of supports for infants, toddlers, and preschoolers and their parents, several of the districts studied by BBA have made additional investments in young children and their families. The Community Storywalk in Clay County, Kentucky, and the Born Learning Trail in Joplin, Missouri, provide opportunities for parents and paid caregivers to learn with their children in a hands-on way through outdoor and physical activities.

The whole-child approaches these communities embrace for children from birth to five years old continue as those children transition to kindergarten and through elementary, middle, and high school.

As these examples illustrate, students continue to benefit from a more comprehensive approach to education and there is an array of strategies school districts can use to deliver that comprehensive approach.

Schools that ensure hands-on learning both in and out of the classroom make the most of this opportunity. Joplin and Pea Ridge students and their teachers enjoy service learning projects that are a core component of the Bright Futures strategy.

In East Durham, partnerships with community agencies and nonprofits enable clubs, field trips to museums, and other enrichment activities.

In most of the districts studied, schools partner with organizations such as the YMCA, Boys and Girls Clubs, Boy Scouts, and Girl Scouts to provide out-of-school enrichment programs that range from organized sports and help with homework to math and book clubs, theater, and robotics.

In addition to boosting student engagement, some focus in particular on academic and college preparatory help, and many also provide snacks or even full meals.

Summer camps in Boston and East Durham and book deliveries and clubs in Pea Ridge and Eastern Kentucky�where online options help bridge long distances in rural areas�keep students reading, engaged, and on track for fall classes. Under City Connects�the whole-child collaboration among Boston College, Boston Public Schools, and community agencies�school coordinators meet at the start of the year with teachers to discuss the particular strengths and needs of each student and develop plans to support teachers with academic and enrichment activities and meet student needs with small-group sessions on healthy eating and dealing with bullies, referrals to mental health providers, and a range of other supports Weiss g.

Two districts have made social and emotional learning a particularly high priority. Austin is one of eight districts working with the Collaborative for Academic, Social, and Emotional Learning CASEL to comprehensively embed social and emotional learning in teacher training, teacher standards, curricula, and metrics for assessing student and school progress CASEL These are complemented by enhanced support for teachers to nurture social and emotional learning in daily classroom practice, by standards-based report cards that track key social and emotional skills, and by constructive disciplinary policies that reengage students and build their soft skills instead of punishing them for infractions.

Several of the districts focus in particular on helping students�many of whom will be the first in their families to go to college�prepare for and make that leap. They offer site-based mentoring from current undergraduates. Middle and high school students in the North Minneapolis Northside Achievement Zone receive similar assistance.

De-tracking, an intentional decision to not separate students who are achieving at different levels into different classrooms or types of courses, which is the norm in Austin and in some Montgomery County high schools, helps ensure that college preparatory classes serve students of all income levels rather than just wealthier, nonminority students.

College readiness is also a high priority for many Bright Futures districts. And in Pea Ridge, specialized high schools such as the Manufacturing and Business Academy and Pea Ridge Academy provide targeted support for students who want to go straight to jobs and careers or need special academic supports. These relationships are key to efforts in large urban districts and remote rural ones.

In Eastern Kentucky, to bridge the long distances between one school and community and another, mentors use Skype to connect with eighth- and ninth-graders in Promise Neighborhood area schools.

Several of the districts studied have established health clinics in some or all of their schools, including Montgomery County, Vancouver, and New York City. In some other districts, such as Austin, school coordinators can arrange for mobile clinics to come to schools. These clinics provide basic preventive care through immunizations and check-ups, along with prescriptions and other care for sick children, physical and mental health screenings, follow-up counseling, mental health care, and even crisis intervention when needed.

Nutrition is another critical factor that affects physical and mental health and thus learning. Food and clothing pantries plus social media outreach in Pea Ridge and Joplin enable counselors and teachers to meet targeted immediate needs so students can focus and learn.

Montgomery County has expanded its breakfast-in-the-classroom program to serve all students in a growing share of schools MCPS Though research has long affirmed the importance of parental engagement, many schools struggle to meaningfully engage parents. The case study districts show how it can be done.

And full-service community schools such as those in Vancouver and New York City specialize in parent outreach and engagement. Community schools in these districts draw on parental input to shape school policies and practices and provide parents with an opportunity to meet one another.

Other targeted supports provide added help for the most vulnerable students and their families. In Vancouver, for example, student advocates conduct home visits to parents of kindergartners and first-graders who are at risk of chronic absenteeism. In these visits, the advocates emphasize the importance of attendance and brainstorm with parents ways to reduce specific barriers to attendance.

Complementary in-school efforts reward strong attendance. High-risk Montgomery County Public Schools students benefit from an unusual, but very effective, system of targeted support.

Providing children from birth through 12th grade and their families with targeted supports both within and outside of school has enabled these communities to make progress toward a range of goals. These districts ensure enrichment for all students, regardless of socioeconomic status.

Finally, in contrast with the national trend in recent decades of rapidly growing achievement gaps between wealthy and poor students, these districts are also narrowing race- and income-based achievement gaps: while all students are gaining ground, those who started off behind tend to see the largest gains. Most of the data presented in this section do not come from experimental studies; with a few exceptions which are noted in the case studies , they rely on nonexperimental comparisons with a similar nontreatment group, such as other low-income children in the district or other high-poverty districts in the state.

However, they are gathered from official district, state, or federal resources in all cases, except for the minority of cases in which such data are not publicly available. Perhaps most importantly, in contrast with many other programs that have reported substantially improved outcomes for very vulnerable groups of students, these programs do not cherry-pick students to get these results.

Establishing more expansive goals and implementing ways to track progress toward those goals also offers timely guidance, given that the Every Student Succeeds Act ESSA asks states, districts, and schools to do just that.

These districts have not only set broader goals, they are demonstrating real progress toward achieving these goals. Because of their success, many now serve as role models for other districts or entire regions, and a few are beginning to influence state policy as well.

Some of the kindergarten readiness efforts described above have translated into improved readiness to learn and, thus, greater odds of success in kindergarten and throughout the K�12 years. In Eastern Kentucky, East Durham, and Minneapolis, children who participated in early learning programs significantly increased their rates of kindergarten readiness across a range of metrics and developmental domains.

A study of Montgomery County Public Schools found much larger gains in reading for children in the full-day Head Start program than for children in the half-day program, with full-day students more than doubling their reading scores over the year and especially pronounced gains for the most vulnerable students: Hispanics and English language learners Marietta While only one of many indicators, rising test scores and narrowing gaps in core academic subjects are an important sign that schools in case study districts have sustained and enhanced early gains.

Increases varied from four points to 15�19 points, with the latter increases occurring in schools with the highest levels of parental engagement Henderson Subsequent rollout of social and emotional learning in district schools some of which were also Alliance schools produced gains in the share of students deemed proficient on the State of Texas Assessment of Academic Readiness STAAR, the next-generation state assessments in the years following that rollout, with students in the first set of schools with social and emotional learning programs scoring higher on state math and reading exams than those in later school cohorts.

The small group of Minneapolis Northside Achievement Zone students who were tested increased their proficiency on the Minnesota Comprehensive Assessments MCA exam, with the share scoring as proficient rising from 14 percent in the � academic year to 22 percent in � Despite serving a much poorer and socially and economically isolated student body than in state schools overall, the Eastern Kentucky schools served by Partners for Education have seen substantially higher increases in test scores: from to , math test scores in the Promise Neighborhood region rose 7.

Specifically, a study found that participants outperformed their peers 97 percent of the time on third-, fourth-, and fifth-grade standardized tests in math and reading, demonstrating a significant long-term positive effect Caspe and Lorenzo Kennedy Increases or lack of decreases in reading scores over the summer months between the end of the school year and the start of the following year can be an especially important indicator of sustainable academic achievement, since low-income students tend to lose substantial ground when they are out of school for the summer.

Case study districts with more mature initiatives and those offering higher or more intensive doses of whole-child interventions are producing particularly large academic gains. Students enrolled in City Connects elementary schools Class 10th Ncert Hindi Solutions Kshitij Mp in Boston score significantly higher on tests of both academic and noncognitive skills in elementary and secondary school, with the highest-risk students, such as English language learners, showing especially large gains.

Scores of City Connects elementary school students on the Stanford Achievement Test version 9 increased between one-fourth and one-half a standard deviation greater than scores of their non�City Connects peers.

Chronic absenteeism depresses achievement, particularly among low-income students. Students attending City Connects high schools in Boston have significantly lower rates of chronic absenteeism than their peers Boston College Center for Optimized Student Support In Joplin, Missouri, attendance rates among high school students increased 3.

At the same time, reportable disciplinary incidents�which keep students out of school and are found to drive at-risk students to disengage�dropped by over 1,, from 3, in to 2, in Every infant and toddler in East Durham whose family participated in the Healthy Families Durham home visiting program is up to date on immunizations; this helps at-risk children avoid missing school due to illness.

This not only improved their health but enabled them to participate in the kinds of extracurricular sports activities that boost student engagement. Because most of the initiatives studied have been in place for less than 10 years, and a few for five or fewer, there is less evidence of their impact on high school graduation and college enrollment. Parent-organizing in Austin helped establish a program to get more low-income and minority middle school students into rigorous science and math programs, enabling them to successfully compete for slots in the prestigious LBJ High School Science Academy.

From the � to the � academic year, the number of Kalamazoo Public School students taking Advanced Placement AP courses more than doubled, with low-income and African American students experiencing the largest absolute gains in participation and Hispanic students experiencing the largest percentage gains.

Black and low-income students roughly quadrupled their participation in such courses; black students and low-income students took AP classes during the � academic year, up from 63 and 53 respectively in � Miller-Adams Over the same period, the number of Hispanic students taking AP courses increased by a magnitude of 10�from just 8 to And in Vancouver, which also made socioeconomic diversity of students in advanced courses a priority, enrollment in AP courses rose by 67 percent overall from � to �, and nearly three times as fast, by almost percent, among low-income students.

By , with the benefit of a community schools strategy, the school was serving more than 1, students and had a graduation rate of 85 percent. At the same time, the cohort dropout rate fell from 6. And in Kalamazoo, incentives to finish high school have proven to be powerful tools for disadvantaged students when combined with mentoring, tutoring, and after-school options.

Moreover, African American girls in Kalamazoo graduate at higher rates than their peers across the state, and 85 percent of those graduates go to college. Initiatives that have had time to mature have made particularly large gains. Hispanic, low-income, and African American students in Montgomery County Public Schools are much more likely than their counterparts across the state to graduate from high school� And from to , a period when the share of students in poverty and the share of minority students rose in the district, overall graduation rates rose 2.

There were much larger gains for Hispanic and black students, whose graduation rates rose respectively by 4. In Vancouver, the four-year graduation rate rose from 64 percent in to almost 80 percent in , and the five-year rate rose from 69 percent in to over 80 percent in The comprehensive, whole-child, whole-community approaches in the featured school districts have built strong school�community partnerships.

Two indicators of the strength of the partnerships are the levels of parent and community engagement. In Joplin, more adults are now serving as mentors and tutors than five years ago.

The support also helps more families connect with stable housing, substantially reducing the number of times that some vulnerable families move. In �, up to Austin families benefited from help with legal, employment, health, and housing issues at the family resource center, which also provides classes for parents, including English language learning classes.

And Montgomery County Public Schools social workers who specialize in early childhood education make an average of home visits, 1, phone contacts, and direct contacts with parents at school or conferences each month. In some cases, engagement enhances school leadership. And over 2, Kentucky parents have undergone training at the Berea Commonwealth Institute for Parent Leadership since its creation in Many of these parents have gone on to join school boards, serve on school councils, and engage in day-to-day educational advocacy.

Aided by federal School Improvement Grant funds, City Connects has operated in Springfield since , expanding from six to 13 schools in its first four years there.

And in both Vancouver and Austin, district leaders have led advocacy efforts to bring community schools to other communities in the region and to support the introduction of state-level legislation to enhance the work. Bright Futures began in Joplin, Missouri, in but is now a national organization. Bright Futures USA has 50 affiliates in eight states, many of which�such as Pea Ridge�are just two or three years old.

The newest affiliate, in Fairbanks, Alaska, has just been made official. In Virginia, Dave Sovine, superintendent of a second-year affiliate, Frederick County Public Schools, is reaching out to several of his counterparts across the region to create the first regional Bright Futures initiative Gizriel If established, this would allow for the kind of cross-district collaboration identified by Bright Futures founder C.

As this report demonstrates, very large social-class-based gaps in academic performance exist and have persisted across the two most recently studied cohorts of students starting kindergarten. The estimated gap between children in the top fifth and the bottom fifth of the SES distribution is over a standard deviation in both reading and math in unadjusted performance gaps are 1.

Another important finding from our study is that gaps were not, on average, sensitive to the set of changes that may have occurred between and gaps across both types of skills are virtually unchanged compared with the prior generation of students�those who entered school in The only cognitive gap that changed substantially was in reading skills, which increased by about a tenth of a standard deviation.

The gaps by SES in mathematics, in approaches to learning as reported by parents, and in self-control as reported by teachers did not change significantly. And relative gaps in approaches to learning as reported by teachers and in self-control as reported by parents shrank between and , by about a tenth of a standard deviation. This means that there is a substantial set of SES-related factors that are not captured by the traditional covariates used in this study but that are important to understanding how and why gaps develop.

Moreover, the capacity for these other factors�child and family characteristics, early education investments, and expectations�to narrow gaps has decreased over time. This suggests that, while such activities as parental time spent with children and center-based pre-K programs cushion the negative consequences of growing up in a low-social-class context, they can do only so much, and that the overall toxicity of lacking resources and supports is increasingly hard to compensate for.

The resistance of gaps to these controls should thus be a matter of real concern for researchers and policymakers. These troubling trends point to critical implications for policy and for our society: clearly, we are failing to provide the foundational experiences and opportunities that all children need to succeed in school and thrive in life. The failure to narrow gaps between and suggests, too, that investments in pre-K programs and other early education and economic supports were insufficient to counter rising rates of poverty and its increasing concentration in neighborhoods where black and Hispanic children tend to live and learn.

But there is also good news. The case study review in the previous section of this report explores district-level strategies to address these gaps, strategies that are being implemented in diverse communities across the country.

The communities studied all employ comprehensive educational approaches that align enriching school strategies with a range of supports for children and their families. Their implementation is often guided by holistic data and, to the extent possible, this report provides a summary, as well, of student outcomes, using both traditional academic measures and a broad range of other measures.

Parents were more likely in than in to read regularly to their children; to sing to them; to play games with them; and to enroll them in center-based pre-K programs. Key principles that span across the case studies include very early interventions and supports, parental engagement and education, pre-K, kindergarten transitions, whole-child approaches to curricula, and wraparound supports that are sustained through the K�12 years.

However, despite the abundance of child development information available to researchers and parents�about the serious impacts of child poverty, about what works to counter those effects, about the importance of the first years of life for children, and about the value of education�our data indicate insufficient policy response at all levels of government.

Pre-K programs have expanded incrementally and unevenly, with both access and quality still wildly disparate across states and overall availability severely insufficient. There is a dearth of home visiting programs and of quality child care Bivens et al. Child poverty has increased see Proctor, Semega, and Kollar for recent trends in child poverty rates. And while a growing number of districts have embraced Broader, Bolder approaches, that number is failing to keep up with high and growing need.

In sum, it is actually positive, and somewhat impressive, that gaps by and large did not grow in the face of steadily increasing income inequality, compounded by the worst economic crisis in many decades EPI , ; Saez But it is disappointing and troubling that new policy investments made in the previous decade were insufficient to make even a dent in these stubborn gaps.

We cannot ensure real opportunities for all our children unless we tackle the severe inequities underlying our findings. And while momentum to enact comprehensive and sustained strategies to close gaps is growing, such strategies are not being implemented nearly as quickly as children need them to be. These data on large, stubborn gaps across both traditional cognitive and noncognitive skills should guide the design of education policies at the federal, state, and local levels; the combined resources and support of government at all three levels are needed if we are to tackle these inequalities effectively.

Looking at these case studies, policymakers can ask: What are the key strategies these communities employed, what main components characterize these strategies, and how did these communities effectively implement the strategies?

The latter set of questions is particularly pertinent to issues of scalability, financing, and sustainability, all of which have posed significant challenges for the districts studied and others like them. Policymakers can further ask: What other sources or examples might we learn from?

Bright Futures affiliates now exist in 50 districts across eight states�and the program continues to grow�offering another set of communities to look to.

Also, new opportunities under the Every Student Succeeds Act ESSA �from funding to expand and align early childhood education programs to broader and more supports-based educator- and school-accountability systems�provide another avenue for exploration and educational improvement. This is already the focus of states and districts across the country�as well as of education policy nonprofits and associations�and is a focus that has the potential to inspire viable larger-scale models Cook-Harvey et al.

We must take action, in particular, in those areas of policy related to early education in which we have seen little or no progress over the past decade. Quality preschool, among the most-agreed-upon strategies to avert and narrow early gaps, continues to be much talked about but far too little invested in and far too infrequently and shoddily implemented. The advantages of preschool have been known for decades, and significant progress has been made in preschool enrollment over that time; however, preschool enrollment stagnated soon after Barnett et al.

Altogether, this report adds to the strong evidentiary base that identifies strategies to reduce the education consequences of economic inequality. It also sheds light on the need to conduct further research on the channels that drive or cushion changes in readiness.

A close follow-up of these trends in the near future and of the measures adopted to really tackle inequities will not only determine what type of society we will be, but will also say a lot about what type of society we actually are.

Her areas of research include analysis of the production of education, returns to education, program evaluation, international comparative education, human development, and cost-effectiveness and cost-benefit analysis in education. Elaine Weiss served as the national coordinator for the Broader, Bolder Approach to Education BBA from to , in which capacity she worked with four co-chairs, a high-level task force, and multiple coalition partners to promote a comprehensive, evidence-based set of policies to allow all children to thrive.

She holds a Ph. We appreciate the feedback we received from our discussant Richard Todd and from the audience. The authors gratefully acknowledge Rob Grunewald and Milagros Nores for their insightful comments and advice on earlier drafts of the paper. Special gratitude is expressed to Sean Reardon, for his advice and thorough guidance on the sensitivity analyses affecting the measurement of the cognitive skills and their implications for our study, and for sharing useful materials to help test our results.

We thank Ben Zipperer and Yilin Pan for their advice on issues associated with multiple imputation of missing data. We are also grateful to Lora Engdahl and Krista Faries for editing this report, and to Margaret Poydock for her work preparing the tables and figures and formatting the report. Finally, we appreciate the assistance of communications staff at the Economic Policy Institute who helped to disseminate the study, especially Dan Crawford, Kayla Blado, and Elizabeth Rose.

NW, Suite , Washington, D. Email: egarcia epi. Notes: SES refers to socioeconomic status. The gap in equals the gap in plus the change in the gap from to For example, the gap in approaches to learning as reported by teachers in is 0.

For statistical significance of these numbers, see Tables 3 and 4, Model 1. Note: SES refers to socioeconomic status. For statistical significance of these numbers, see Tables 3 and 4, Model 4. Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have mothers in the top quintile of the education distribution and low-SES children have mothers in bottom quintile of the education distribution.

For statistical significance of these numbers, see Table 7, Model 1. Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children are in households with incomes in the top quintile of the income distribution and low-SES children are in households with incomes in bottom quintile of the income distribution.

For statistical significance of these numbers, see Table 8, Model 1. Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have a number of books in the home in the top quintile of the books-in-the-home distribution and low-SES children have a number of books in the home in the bottom quintile of the books-in-the-home distribution.

For statistical significance of these numbers, see Table 9, Model 1. Note: Using the full sample. The number of observations is rounded to the nearest multiple of Notes: Models 1 and 2 use the full sample; Models 3 and 4 use the complete cases sample. Robust standard errors are in parentheses. SES refers to socioeconomic status.

Declining values from to indicate that factors such as early literacy activities and other controls were not as effective at shrinking SES-based gaps in as they were in Notes: The robust standard errors are in parentheses.

Notes: Model 1 uses the full sample; Model 4 uses the complete cases sample. Values are in dollars. Early investments in education strongly predict adolescent and adult development Cunha and Heckman ; Heckman ; Heckman and Kautz For instance, students with higher levels of behavioral skills learn more in school than peers whose attitudinal skills are less developed Jennings and DiPrete Conversely, children who fail to acquire this early foundational knowledge may experience some permanent loss of opportunities to achieve to their full potential.

Research by Reardon had found systematic increases in income gaps among generations. Recent studies by Bassok and Latham and Reardon and Portilla , however, show narrower achievement gaps at kindergarten entry between a recent cohort and the previous one, and thus a possible discontinuation or interruption of that trend. Bassok et al. Clustering takes into account the fact that children are not randomly distributed, but tend to be concentrated in schools or classrooms with children of the same race, social class, etc.

These estimates offer an estimate of gaps within schools. See Appendix B for more details. Results available upon request. The specific skills measured may vary between the home and classroom setting.

Parents, on the other hand, may be basing their expectations on family, community, culture, or other factors. The detailed frequency with which parents develop or practice some activities with their children at home and others is available upon request. This literature acknowledges the multiple pathways through which expectations and behaviors influence educational outcomes, as well as the importance of race, social class, and other factors as moderators of such associations Davis-Kean ; Redd et al.

HHS and U. ED Models include all quintiles in their specification. Tables that offer a comparison for all quintiles relative to the first quintile are available upon request. We focus the discussion on the gap between the top and bottom. As a result, sample sizes become smaller see Appendix Table C1.

Analytic samples once missingness is accounted for are called the complete case samples. We tested to see whether the unadjusted gaps estimated above with the full sample remained the same when using the complete case samples.

For Model 1, we found an average difference of 0. For Model 2, the differences were 0. In terms of statistical significance, there are no significant changes in the estimates associated with the gaps, but there are two changes in the statistical significance of the estimates associated with the changes in the gaps by � , and one change in the magnitude of the coefficient. The first change in the statistical significance of the estimates associated with the changes in the gaps by � is the change in the gap in approaches to learning as reported by parents, which is statistically significant when using the restricted sample 0.

Finally, the one change in the magnitude of the coefficient, in this model, is the estimate of the change in the gap in reading, which increases when using the restricted sample from 0. Results are available upon request. These interactions between inputs and time test for whether the influence of inputs in is smaller than, the same as, or larger than the influence of inputs in Also, although only the fully specified results are shown, as noted in Appendix B, these sets of controls are entered parsimoniously in order to determine how sensitive gaps and changes in gaps over time are to the inclusion of family characteristics only, to the added inclusion of family investments, and, finally, to the inclusion of parental expectations for the inclusion of parental expectations, we incorporated interactions of the covariates with time parsimoniously as well.

For all outcomes, and focusing on the models without interactions between covariates and time, we find that all gaps in continuously shrink as we add more controls. For example, in reading, adding family characteristics reduces the gap in by 11 percent, adding investments further reduces it by 15 percent, and adding expectations further reduces it by 9 percent. In math, these changes equal to 16 percent, 13 percent, and 10 percent. For changes in the gap by �, for both reading and math, adding family characteristics and investments shrink the changes in the gaps, but adding expectations slightly increases the estimated coefficients which are statistically significant for reading, but not for math in these models.

These results are not shown in the appendices, but are available upon request. The change in the skills gaps by SES in due to the inclusion of the controls is not directly visible in the tables in this report. The change in the skills gaps by SES in is directly observable in Tables 3 and 4 and is discussed below.

Please note that until this point in the report we have been concerned with SES gaps and not with performance directly though SES gaps are the result of the influence of SES on performance, which leads to differential performance of children by SES and hence to a performance gap. Now the focus is on exploring the independent effect of the covariates of interest on performance.

In this report, because we address whether the education and selected practices affect outcomes, the main effect is measured for the cohort, and we measure how it changed between and Any finding associated with this variable may be interpreted as the association between attending prekindergarten programs, compared with other options, but must be interpreted with caution.

In other words, the child may have attended a high-quality prekindergarten program, which could have been either private or public, or a low-quality one, which would have different impacts. He or she might have been placed in noneducational child care, either private or public, of high or low quality, for few or many hours per day, with very different implications for his or her development Barnett ; Barnett ; Magnuson et al.

For the extensive literature explaining the benefits of pre-K schooling, see Camilli et al. Thus, more detailed information on the characteristics of the nonparental care arrangements type, quality, and quantity would help researchers further disentangle the importance of this variable. This additional information would provide a much clearer picture of the effects of early childhood education on the different educational outcomes.

Because these associations seemed counterintuitive, we tested whether they were sensitive to the composition of the index. We removed one component of the index at a time and created five alternative measures of other enrichment activities that parents do with their children. The results indicate that the negative association between the index and reading is not sensitive to the components of the index the coefficients for the main effect, i.

For math, the associations lose some precision, but retain the negative sign negative association in four out of the five cases minimum coefficient is As a caveat, these components do not reflect whether the activities are undertaken by the child or guided by the adult, the time devoted to them, or how much they involve the use of vocabulary or math concepts.

These results are available upon request. See Appendices C and D for discussions of two other sensitivity analyses, one based on imputation of missing values for the main analysis in this paper, and the other on the utilization of various metrics of the cognitive variables.

Overall, our findings were not sensitive to various multiple imputation tests. In terms of the utilization of different metrics for the cognitive variables, some sensitivity of the point estimates was detected.

With certain activities that are already so provided to high-SES children, there may be little room for doing more for them. For example, there are only 24 hours per day to read to your child, so there is a cap on reading from a cap on time. But perhaps there is still room to improve the influence of reading, if, for example, the way reading is done changes.

Eight of the 12 districts explored in this paper are the subjects of published case studies. Case studies for the other four are in progress and will be published later this year.

When citing information from the published case studies, we cite the specific published study. For the four that are not yet published, we refer to the original sources being used to develop the case studies.

Missing or incomplete cells in the table indicate that data were not available on that aspect of student demographics or other characteristics. In the country as a whole, poverty rates, which had been rising prior to , sped up rapidly during the recession and in its aftermath through � , and minority students mainly Hispanic and Asian grew as a share of the U.

Between and , even with a decline in the proportion of black students, the share of the student body that is minority of black or Hispanic origin increased from The Southern Education Foundation revealed a troubling tipping point in for the first time since such data have been collected, over half of all public school students 51 percent qualified for free or reduced-priced meals i.

Across the South, shares were much higher, with the highest percentage, 71 percent�or nearly three in four students�in Mississippi Southern Education Foundation The federal Early Head Start EHS program includes both a home visiting and a center-based component, with many of the low-income infants and toddlers served benefiting from a combination of the two.

Studies of EHS find improved cognitive, behavioral, and emotional skills for children as well as enhanced parenting behaviors.

According to one important source for data on access to and quality of state pre-K programs, the State of Preschool yearbook produced annually by the National Institute for Early Education Research NIEER at Rutgers University, as of , 42 states and the District of Columbia were funding 57 programs.

Elaine Weiss interview with Joshua Starr, June In recent years, a growing number of reports have emerged that some charter schools�which are technically public schools and often tout their successes in serving disadvantaged students�keep out students unlikely to succeed through complex application processes, fees, parent participation contracts, and other mechanisms, and then further winnow the student body of such students by pushing them out when they struggle academically or behaviorally.

See AIR and Sparks Joplin statistics are from internal data produced for the superintendent at that time that are no longer available.

Attendance Works , a national campaign to reduce chronic absence, points to a range of studies that document and explain the connections between chronic absenteeism, student physical and mental health, and student achievement.

Areas of research include elementary school absenteeism, middle and high school absenteeism, health issues, and state and local data on how these problems play out, among others. Elaine Weiss interview with C.

Huff, June See Appendix D for a discussion of results using other metrics for reading and math achievement. Results are not meaningfully different across metrics, though the point estimates differ slightly. This last feature will be explored in a companion paper to this one, as soon as the necessary information is released by NCES. As Tourangeau et al. We are waiting on the availability of this data to conduct a companion study that allows us to learn whether starting levels of knowledge rose over these years, and what the relative gains were for different demographic groups.

We acknowledge that there are multiple noneducation public policy and economic policy areas to be called upon to address the problems studied in this report, namely, all the ones that ensure other factors that correlate with low-SES are attended, and, obviously, the ones that lead to fewer low-SES children.

These other policies could help ensure that more children grow up in contexts with sufficient resources and healthy surroundings, or would leave fewer children without built-in supports at home that need to be compensated for afterwards. A similar comprehensive approach in terms of policy recommendations was used by Putnam Adamson, Frank, and Linda Darling-Hammond.

Alvarez, Lizette. School Turnaround: A Pocket Guide. Baker, Bruce D. The Stealth Inequities of School Funding. The Center for American Progress. Barbarin, O.

Downer, E. Odom, and D. Barnett, W. Malden, Mass. Bassok, Daphna, Jenna E. Reardon, and Jane Waldfogel. Bassok, Daphna, and Scott Latham.

Berea College. November Achievement Gap in Comparative Perspective. New York: Russell Sage Foundation. Brooks-Gunn, Jeanne, and Lisa Markman. Bivens, Josh. Progressive Redistribution without Guilt. Incomes Grow Fairer and Faster. Economic Policy Institute.

Burris, Carol. Steven Barnett. Five Key Trends in U. Student Performance. Carter, Prudence L. Human intellect and a different approach to computer technologies are necessary. They proposed the use of a supportive information system, which they called a DSS. Note that the more structured and operational control-oriented tasks such as those in cells 1, 2, and 4 of Figure 1. Operational and managerial control decisions are made in all functional areas, especially in finance and production i.

Evaluating credit Preparing budget Laying out plant Scheduling project Designing reward system Categorizing inventory. Building a new plant Planning mergers and acquisitions Planning new products Planning compensation Providing quality assurance Establishing human resources policies Planning inventory.

Planning research and development Developing new technologies Planning social responsibility. Managing finances Monitoring investment portfolio Locating warehouse Monitoring distribution systems. Structured problems, which are encountered repeatedly, have a high level of struc- ture, as their name suggests.

It is therefore possible to abstract, analyze, and classify them into specific categories. For example, a make-or-buy decision is one category.

Other examples of categories are capital budgeting, allocation of resources, distribution, pro- curement, planning, and inventory control decisions. For each category of decision, an easy-to-apply prescribed model and solution approach have been developed, generally as quantitative formulas.

Therefore, it is possible to use a scientific approach for automat- ing portions of managerial decision making. Solutions to many structured problems can be fully automated see Chapters 2 and It is usually necessary to develop customized solutions. However, such solutions may benefit from data and information generated from corporate or external data sources.

Intuition and judgment may play a large role in these types of decisions, as may computerized com- munication and collaboration technologies, as well as cognitive computing Chapter 6 and deep learning Chapter 5. Management science can provide models for the portion of a decision-making problem that is structured.

For the unstructured portion, a DSS can improve the quality of the information on which the decision is based by providing, for example, not only a single solution, but also a range of alternative solutions along with their potential impacts. These capabilities help managers to better understand the nature of problems and, thus, to make better decisions.

It was aimed at decisions that required judgment or at decisions that could not be completely supported by al- gorithms. Not specifically stated but implied in the early definitions was the notion that the system would be computer based, would operate interactively online, and prefer- ably would have graphical output capabilities, now simplified via browsers and mobile devices.

A DSS is typically built to support the solution of a certain problem or to evaluate an op- portunity. Reporting plays a major role in BI; the user gener- ally must identify whether a particular situation warrants attention and then can apply analytical methods.

Again, although models and data access generally through a data warehouse are included in BI, a DSS may have its own databases and is developed to solve a specific problem or set of problems and are therefore called DSS applications. Formally, a DSS is an approach or methodology for supporting decision mak- ing. It uses an interactive, flexible, adaptable computer-based information system CBIS especially developed for supporting the solution to a specific unstructured management problem.

In addition, a DSS includes models and is developed possibly by. It can support all phases of deci- sion making and may include a knowledge component. Finally, a DSS can be used by a single user or can be Web based for use by many people at several locations. The capabilities in Figure 1. Supports decision makers, mainly in semistructured and unstructured situations, by bringing together human judgment and computerized information.

Such problems cannot be solved or cannot be solved conveniently by other computerized systems or through use of standard quantitative methods or tools. Generally, these problems gain structure as the DSS is developed. Even some structured problems have been solved by DSS. Supports all managerial levels, ranging from top executives to line managers.

Supports individuals as well as groups. Less-structured problems often require the. DSS supports virtual teams through collaborative Web tools.

DSS has been developed to support individual and group work as well. The decisions may be made once, several times, or repeatedly. Supports all phases of the decision-making process: intelligence, design, choice, and implementation. Supports a variety of decision-making processes and styles.

Is flexible, so users can add, delete, combine, change, or rearrange basic elements. The decision maker should be reactive, able to confront changing conditions quickly, and able to adapt the DSS to meet these changes. It is also flexible in that it can be readily modified to solve other, similar problems.

Is user-friendly, has strong graphical capabilities, and a natural language interactive human-machine interface can greatly increase the effectiveness of DSS.

Most new DSS applications use Web-based interfaces or mobile platform interfaces. Improves the effectiveness of decision making e. When DSS is deployed, decision making often takes longer, but the decisions are better. Provides complete control by the decision maker over all steps of the decision- making process in solving a problem.

A DSS specifically aims to support, not to replace, the decision maker. Enables end users to develop and modify simple systems by themselves. Larger systems can be built with assistance from IS specialists.

Spreadsheet packages have been utilized in developing simpler systems. Provides models that are generally utilized to analyze decision-making situations. The modeling capability enables experimentation with different strategies under dif- ferent configurations. Provides access to a variety of data sources, formats, and types, including GIS, mul- timedia, and object-oriented data.

Can be employed as a stand-alone tool used by an individual decision maker in one location or distributed throughout an organization and in several organiza- tions along the supply chain.

These key DSS characteristics and capabilities allow decision makers to make bet- ter, more consistent decisions in a timely manner, and they are provided by major DSS components,. A DSS application can be composed of a data management subsystem, a model manage- ment subsystem, a user interface subsystem, and a knowledge-based management sub- system.

We show these in Figure 1. The data management subsystem includes a database that contains relevant data for the situation and is managed by software called the database management system DBMS. DBMS is used as both singular and plural system and systems terms, as are many other acronyms in this text. The data management subsystem can be interconnected with the corporate data warehouse, a repository for corporate relevant decision-making data.

Usually, the data are stored or accessed via a database Web server. The data management subsystem is composed of the following elements:. Many of the BI or descriptive analytics applications derive their strength from the data management side of the subsystems.

Modeling languages for building cus- tom models are also included. This software is often called a model base management system MBMS. This component can be connected to corporate or external storage of models. Model solution methods and management systems are implemented in Web de- velopment systems such as Java to run on application servers.

The model management subsystem of a DSS is composed of the following elements:. Because DSS deals with semistructured or unstructured problems, it is often neces- sary to customize models, using programming tools and languages. Some examples of these are. OLAP software may also be used to work with models in data analysis. Even languages for simulations such as Arena and. For small- and medium-sized DSS or for less complex ones, a spreadsheet e.

We use Excel for several ex- amples in this book. Application Case 1. The user communicates with and commands the DSS through the user interface subsys- tem. The user is considered part of the system. Researchers assert that some of the unique contributions of DSS are derived from the intensive interaction between the computer and the decision maker.

A difficult user interface is one of the major reasons that man- agers do not use computers and quantitative analyses as much as they could, given the availability of these technologies. For locally used DSS, a spreadsheet also provides a familiar user interface. The Web browser has been recognized as an effective DSS GUI because it is flexible, user-friendly, and a gateway to almost all sources of necessary infor- mation and data.

Essentially, Web browsers have led to the development of portals and dashboards, which front end many DSS. Explosive growth in portable devices, including smartphones and tablets, has changed the DSS user interfaces as well. These devices allow either handwritten input or.

Telecommunications network services to educational institutions and government entities are typically pro- vided by a mix of private and public organizations. Many states in the United States have one or more state agencies that are responsible for providing net- work services to schools, colleges, and other state agencies.

One example of such an agency is OneNet in Oklahoma. Usually agencies such as OneNet operate as an enterprise-type fund. This cost recovery should occur through a pricing mechanism that is efficient, simple to implement, and equitable.

This pricing model typically needs to recognize many factors: convergence of voice, data, and video traffic on the same infrastructure; diversity of user base in terms of educational institu- tions and state agencies; diversity of applications in use by state clients from e-mail to videoconferences, IP telephoning, and distance learning; recovery of current costs as well as planning for upgrades and.

These considerations led to the development of a spreadsheet-based model. In addition, the SNAP-DSS not only illustrates the influence of the changes in the pricing factors on each rate card option but also allows the user to analyze various rate card options in different scenarios using dif- ferent parameters. This model has been used by OneNet financial planners to gain insights into their customers and analyze many what-if scenarios of different rate plan options.

Source: Based on J. Chongwatpol and R. Some DSS user interfaces utilize natural language input i. Cell phone inputs through short message service SMS or chatbots are becoming more common for at least some consumer DSS-type applications. Such capabilities are most useful in locating nearby businesses, addresses, or phone numbers, but it can also be used for many other decision support tasks.

Search activities noted in the previous paragraph are also largely accomplished now through apps provided by each search provider. Voice input for these devices and the new smart speakers such as Amazon Echo Alexa and Google Home is common and fairly accurate but not perfect.

When voice input with accompanying speech-recognition software and readily available text-to- speech software is used, verbal instructions with accompanied actions and outputs can be invoked. These are readily available for DSS and are incorporated into the portable devices described earlier. Many of the user interface developments are closely tied to the major new advances in their knowledge-based systems.

The knowledge-based management subsystem can support any of the other subsystems or act as an independent component. Many artificial intelligence methods have been implemented in the current generation of learning systems and are easy to integrate into the other DSS com- ponents. This section has covered the history and progression of Decision Support Systems in brief.

In the next section we discuss evolution of this support to business intelligence, analytics, and data science. What is the difference between a problem and its symptoms? Why is it important to classify a problem? Define implementation. What are structured, unstructured, and semistructured decisions? Provide two exam-.

Define operational control, managerial control, and strategic planning. Provide two. What are the nine cells of the decision framework? Explain what each is for. How can computers provide support for making structured decisions? How can computers provide support for making semistructured and unstructured. The timeline in Figure 1.

During the s, the primary focus of information systems support for decision making focused on providing structured, periodic reports that a manager could use for decision making or ignore them. Businesses began to create routine reports to inform decision makers managers about what had happened in the previous period e.

Although it was useful to know what had happened in the past, managers needed more than this: They needed a variety of reports at different levels of granularity to better understand and address changing needs and challenges of the busi- ness. These were usually called management information systems MIS. Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions.

It is a computer-based support system for management decision makers who deal with semistructured problems.

Note that the term decision support system, like management information system and several other terms in the field of IT, is a content-free expression i. Therefore, there is no universally accepted definition of DSS. During the early days of analytics, data were often obtained from the domain ex- perts using manual processes i. The idea was to do the best with limited resources. Such decision support models were typically called operations research OR.

The problems that were too complex to solve optimally using linear or nonlinear mathematical programming techniques were tackled using heuristic methods such as simulation models.

We will introduce these as prescriptive analytics later in this chapter. In the late s and early s, in addition to the mature OR models that were being used in many industries and government systems, a new and exciting line of mod- els had emerged: rule-based expert systems ESs.

The s saw a significant change in the way organizations captured business- related data. The old practice had been to have multiple disjointed information systems tailored to capture transactional data of different organizational units or functions e. In the s, these systems were integrated as enterprise-level information systems that we now commonly call en- terprise resource planning ERP systems.

The old mostly sequential and nonstandardized data representation schemas were replaced by relational database management RDBM systems. These systems made it possible to improve the capture and storage of data as well as the relationships between organizational data fields while significantly reducing the replication of information.

The need for RDBM and ERP systems emerged when data integrity and consistency became an issue, significantly hindering the effectiveness of business practices. With ERP, all the data from every corner of the enterprise is collected and integrated into a consistent schema so that every part of the organization has access to the single version of the truth when and where needed.

In addition to the emergence of ERP systems, or perhaps because of these systems, business reporting became an on- demand, as-needed business practice. Decision makers could decide when they needed to or wanted to create specialized reports to investigate organizational problems and opportunities. In the s, the need for more versatile reporting led to the development of execu- tive information systems EISs; DSS designed and developed specifically for executives and their decision-making needs.

These systems were designed as graphical dashboards and scorecards so that they could serve as visually appealing displays while focusing on the most important factors for decision makers to keep track of the key performance in- dicators. To make this highly versatile reporting possible while keeping the transactional integrity of the business information systems intact, it was necessary to create a middle data tier known as a DW as a repository to specifically support business reporting and decision making.

In a very short time, most large- to medium-sized businesses adopted data warehousing as their platform for enterprise-wide decision making.

The dashboards and scorecards got their data from a DW, and by doing so, they were not hindering the efficiency of the business transaction systems mostly referred to as ERP systems. As the amount of longitudinal data accumulated in the DWs increased, so did the capabilities of hardware.

Because of the globalized competitive marketplace, decision makers needed current information in a very digestible format to address business problems and to take advantage of market opportunities in a timely manner. Because the data in a DW are up- dated periodically, they do not reflect the latest information. To elevate this information latency problem, DW vendors developed a system to update the data more frequently, which led to the terms real-time data warehousing and, more realistically, right-time data warehousing, which differs from the former by adopting a data-refreshing policy based on the needed freshness of the data items i.

With the increasing volumes and varieties of data, the needs for more storage and more processing power emerged. Although large corporations had the means to tackle this problem, small- to medium-sized companies needed more financially manageable business models. This need led to service-oriented architecture and software and infrastructure-as-a-service ana- lytics business models.

Smaller companies, therefore, gained access to analytics capabili- ties on an as-needed basis and paid only for what they used, as opposed to investing in financially prohibitive hardware and software resources.

In the s, we are seeing yet another paradigm shift in the way that data are captured and used. Largely because of the widespread use of the Internet, new data gen- eration mediums have emerged. Of all the new data sources e. These unstructured data are rich in information content, but analysis of such data sources poses significant challenges to computational systems from both software and hardware perspectives.

Recently, the term Big Data has been coined to highlight the challenges that these new data streams have brought on us. Many advancements in both hardware e. The last few years and the upcoming decade are bringing massive growth in many exciting dimensions. For example, streaming analytics and the sensor technologies have enabled the IoT. Artificial Intelligence is changing the shape of BI by enabling new ways of analyzing images through deep learning, not just traditional visualization of data.

Deep learning and AI are also helping grow voice recognition and speech synthesis, leading to new interfaces in interacting with technologies. Almost half of U. Growth in video interfaces will eventually enable gesture-based interaction with systems.

All of these are being enabled due to massive cloud- based data storage and amazingly fast processing capabilities. And more is yet to come. It is hard to predict what the next decade will bring and what the new analytics-related terms will be. The time between new paradigm shifts in information systems and particularly in analytics has been shrinking, and this trend will continue for the foreseeable future.

Even though analytics is not new, the explosion in its popularity is very new. Thanks to the recent explosion in Big Data, ways to collect and store these data and intuitive software tools, data- driven insights are more accessible to business professionals than ever before. Therefore, in the midst of global competition, there is a huge opportunity to make better managerial decisions by using data and analytics to increase revenue while decreasing costs by building better products, improving customer experience, and catching fraud before it happens, im- proving customer engagement through targeting and customization, and developing entirely.

More and more companies are now preparing their employees with the know-how of business analytics to drive effec- tiveness and efficiency in their day-to-day decision-making processes.

The next section focuses on a framework for BI. Although most people would agree that BI has evolved into analytics and data science, many vendors and researchers still use that term. So the next few paragraphs pay homage to that history by specifically focusing on what has been called BI. Following the next section, we introduce analytics and use that as the label for classifying all related concepts. The decision support concepts presented in Sections 1.

As noted in Section 1. These systems, which were generally called EISs, then began to offer addi- tional visualization, alerts, and performance measurement capabilities. By , the major commercial products and services appeared under the term business intelligence BI.

It is, like DSS, a content-free expression, so it means different things to different people. Part of the confu- sion about BI lies in the flurry of acronyms and buzzwords that are associated with it e.

By analyzing his- torical and current data, situations, and performances, decision makers get valuable insights that enable them to make more informed and better decisions. The process of BI is based on the transformation of data to information, then to decisions, and finally to actions. However, as the history in the previous section points out, the concept is much older; it has its roots in the MIS reporting systems of the s.

During that period, reporting sys- tems were static, were two dimensional, and had no analytical capabilities. In the early s, the concept of EISs emerged. This concept expanded the computerized support to top-level managers and executives.

Some of the capabilities introduced were dynamic mul- tidimensional ad hoc or on-demand reporting, forecasting and prediction, trend analysis, drill-down to details, status access, and critical success factors.

These features appeared in dozens of commercial products until the mids. Then the same capabilities and some new ones appeared under the name BI.

Today, a good BI-based enterprise information system contains all the information that executives need. By , BI systems started to include artificial intelligence ca- pabilities as well as powerful analytical capabilities. Figure 1. It illustrates the evolution of BI as well. The tools shown in Figure 1. The most sophisticated BI products include most of these capabilities; others specialize in only some of them.

A BI system has four major components: a DW, with its source data; business analytics, a collection of tools for manipulating, mining, and analyzing the data in the DW; BPM for monitoring and analyzing performance; and a user interface e. The re- lationship among these components is illustrated in Figure 1. Where did modern approaches to DW and BI come from? What are their roots, and how do those roots affect the way organizations are managing these initiatives today?

The same is true of DW and the BI applications that make these initiatives possible. Source: Based on W. Organizations are being compelled to capture, understand, and harness their data to support decision making to improve business operations. Legislation and regulation e. Moreover, business cycle times are now extremely compressed; faster, more informed, and better decision making is, therefore, a competitive impera- tive.

Managers need the right information at the right time and in the right place. This is the mantra for modern approaches to BI. Organizations have to work smart. Paying careful attention to the management of BI initiatives is a necessary aspect of doing business. It is no surprise, then, that organiza- tions are increasingly championing BI and under its new incarnation as analytics.

BI systems rely on a DW as the information source for creating insight and supporting managerial decisions. A multitude of organizational and external data is captured, trans- formed, and stored in a DW to support timely and accurate decisions through enriched business insight. In simple terms, a DW is a pool of data produced to support decision making; it is also a repository of current and historical data of potential interest to man- agers throughout the organization.

Data are usually structured to be available in a form ready for analytical processing activities i. Whereas a DW is a repository of data, data warehousing is literally the entire process. Data warehousing is a discipline that results in applications that provide decision support capability, allows ready access to business information, and creates business insight.

Whereas a DW combines databases across an en- tire enterprise, a DM is usually smaller and focuses on a particular subject or department. A DM is a subset of a data warehouse, typically consisting of a single subject area e. An operational data store ODS provides a fairly recent form of customer information file. This type of database is often used as an interim staging area for a DW.

Unlike the static contents of a DW, the contents of an ODS are updated throughout the course of business operations. An EDW is a large-scale data warehouse that is used across the enterprise for decision support.

The large-scale nature of an EDW provides in- tegration of data from many sources into a standard format for effective BI and decision support applications. In Figure 1. Data from many different sources can be extracted, transformed, and loaded into a DW for further access and analytics for decision support.

To illustrate the major characteristics of BI, first we will show what BI is not�namely, transaction processing. We are all familiar with the information systems that support our transactions, like ATM withdrawals, bank deposits, and cash register scans at the grocery store. These transaction processing systems are constantly involved in handling updates to what we might call operational databases.

These online transaction processing OLTP systems handle a. In contrast, a DW is typically a distinct system that provides storage for data that will be used for analysis. Each request is considered to be a transaction, which is a computerized record of a discrete event, such as the receipt of inventory or a customer order. In other words, a transaction requires a set of two or more database updates that must be completed in an all-or-nothing fashion.

The very design that makes an OLTP system efficient for transaction processing makes it inefficient for end-user ad hoc reports, queries, and analysis. To resolve these issues, the notions of DW and BI were created. DWs contain a wide variety of data that present a coherent picture of business con- ditions at a single point in time.

The idea was to create a database infrastructure that was always online and contained all the information from the OLTP systems, including histori- cal data, but reorganized and structured in such a way that it was fast and efficient for querying, analysis, and decision support.

TUN includes videos similar to the television show CSI to illustrate concepts of analytics in different industries. Watch the video that appears on YouTube. Essentially, you have to assume the role of a customer service center professional.

An incoming flight is run- ning late, and several passengers are likely to miss their connecting flights. There are seats on one outgoing flight that can accommodate two of the four passengers. Which two passengers should be given priority? Watch the video, pause it as appropriate, and answer the questions on which pas- sengers should be given priority. Then resume the video to get more information.

After the video is complete, you can see the slides related to this video and how the analy- sis was prepared on a slide set at www. This multimedia excursion provides an example of how additional available information through an enterprise DW can assist in decision making. It is interesting to note that some people believe that DSS is a part of BI�one of its analytical tools. Further, as noted in the next section onward, in many circles, BI has been subsumed by the new terms analytics or data science.

BI cannot simply be a technical exercise for the information systems department. It has to serve as a way to change the manner in which the company con- ducts business by improving its business processes and transforming decision- making processes to be more data driven.

Many BI consultants and practitioners involved in suc- cessful BI initiatives advise that a framework for planning is a necessary precondition.

One framework, proposed by Gartner, Inc. At the busi- ness and organizational levels, strategic and operational objectives must be defined while considering the available organizational skills to achieve those objectives. Issues of orga- nizational culture surrounding BI initiatives and building enthusiasm for those initiatives and procedures for the intra-organizational sharing of BI best practices must be consid- ered by upper management�with plans in place to prepare the organization for change.

One of the first steps in that process is to assess the IS organization, the skill sets of the potential Ncert Solution Class 10th Hindi Kritika Eng classes of users, and whether the culture is amenable to change.

From this as- sessment, and assuming there are justification and the need to move ahead, a company can prepare a detailed action plan. Another critical issue for BI implementation success is the integration of several BI projects most enterprises use several BI projects among themselves and with the other IT systems in the organization and its business partners.

Gartner and many other analytics consulting organizations promoted the concept of a BI competence center that would serve the following functions:. Over the last 10 years, the idea of a BI competence center has been abandoned because many advanced technologies covered in this book have reduced the need for a central group to organize many of these functions. For example, many data visualizations are easily accomplished by end users using the latest visualization pack- ages Chapter 3 will introduce some of these.

As noted by Duncan , the BI team would now be more focused on producing curated data sets to enable self- service BI. Because analytics is now permeating across the whole organization, the BI competency center could evolve into an analytics community of excellence to promote best practices and ensure overall alignment of analytics initiatives with organizational strategy.

BI tools sometimes needed to be integrated among themselves, creating synergy. The need for integration pushed software vendors to continuously add capabilities to their products. Customers who buy an all-in-one software package deal with only one vendor and do not have to deal with system connectivity.

This led to major chaos in the BI market space. Many of the software tools that rode the BI wave e. This book covers many of these topics in significant detail by giving examples of how the technologies are evolving and being applied, and the managerial implications.

List three of the terms that have been predecessors of analytics. Information Systems? Define BI. List and describe the major components of BI.

Define OLTP. Define OLAP. List some other success factors of BI. The word analytics has largely replaced the previous individual components of comput- erized decision support technologies that have been available under various labels in the past. Indeed, many practitioners and academics now use the word analytics in place of BI.

Although many authors and consultants have defined it slightly differently, one can. Of course, many other organizations have proposed their own interpreta- tions and motivations for analytics.

These reports essentially provide a sense of what is happening with an organization. Additional technolo- gies have enabled us to create more customized reports that can be generated on an ad hoc basis. The next extension of reporting takes us to OLAP-type queries that allow a user to dig deeper and determine specific sources of concern or opportunities. Technologies available today can also automatically issue alerts for a decision maker when performance warrants such alerts.

At a consumer level, we see such alerts for weather or other issues. But similar alerts can also be generated in specific settings when sales fall above or below a certain level within a certain time period or when the inventory for a specific product is running low. All of these applications are made possible through analysis and queries of data being collected by an organization. The next level of analysis might entail statistical analysis to better understand patterns.

When an organization has a good view of what is happening and what is likely to happen, it can also employ other techniques to make the best decisions under the circumstances. This idea of looking at all the data to understand what is happening, what will happen, and how to make the best of it has also been encapsulated by INFORMS in pro- posing three levels of analytics.

These three levels are identified as descriptive, predictive, and prescriptive. It suggests that these three are somewhat independent steps and one type of analytics.

It also suggests that there is actually some overlap across these three types of analytics. In either case, the interconnected nature of different types of analytics applications is evident. We next introduce these three levels of analytics. Descriptive or reporting analytics refers to knowing what is happening in the or- ganization and understanding some underlying trends and causes of such occurrences.

First, this involves the consolidation of data sources and availability of all relevant data in a form that enables appropriate reporting and analysis. Usually, the development of this data infrastructure is part of DWs.

From this data infrastructure, we can develop ap- propriate reports, queries, alerts, and trends using various reporting tools and techniques. A significant technology that has become a key player in this area is visualization.

Using the latest visualization tools in the marketplace, we can now develop powerful in- sights in the operations of our organization. Application Cases 1. Silvaris Corporation was founded in by a team of forest industry professionals to pro- vide technological advancement in the lumber and building material sector. Silvaris is the first e- commerce platform in the United States spe- cifically for forest products and is headquartered in Seattle, Washington.

It is a leading wholesale provider of industrial wood products and surplus building materials. Silvaris sells its products and provides interna- tional logistics services to more than 3, custom- ers. To manage various processes that are involved in a transaction, the company created a proprietary online trading platform to track information flow related to transactions between traders, accounting, credit, and logistics.

This allowed Silvaris to share its real-time information with its customers and partners. But due to the rapidly changing prices of materials, it became necessary for Silvaris to get a real-time view of data without moving them into a separate reporting format. Silvaris started using Tableau because of its abil- ity to connect with and visualize live data.

With dash- boards created by Tableau that are easy to understand and explain, Silvaris started using it for reporting pur- poses. This helped Silvaris in pulling out informa- tion quickly from the data and identifying issues that impact its business. Silvaris succeeded in managing.

Now, Silvaris keeps track of online orders placed by customers and knows when to send renew pushes to which customers to keep them purchasing online. Also, analysts of Silvaris can save time by generating dashboards instead of writ- ing hundreds of pages of reports by using Tableau.

Sources: Tableau. What was the challenge faced by Silvaris? How did Silvaris solve its problem using data. Many industries need to analyze data in real time. Real-time analysis enables the analysts to identify issues 10th Class Mathematics All Formulas Web that impact their business. Visualization is sometimes the best way to begin analyzing the live data streams. Tableau is one such data visualization tool that has the capability to analyze live data with- out bringing live data into a separate reporting format.

Predictive analytics aims to determine what is likely to happen in the future. This analysis is based on statistical techniques as well as other more recently developed techniques that fall under the general category of data mining.

A number of techniques are used in developing predictive analytical applications, including various classification algorithms. For example, as described in Chapters 4 and 5, we can use classification techniques such as logistic regression, de- cision tree models, and neural networks to predict how well a motion picture will do at the box office. We can also use clustering algorithms for segmenting customers into different clusters to be able to target specific promotions to them.

Finally, we can use association mining techniques Chapters 4 and 5 to estimate relationships between different purchasing behaviors. That is, if a customer buys one product, what else is the customer likely to purchase?

Such analysis can assist a retailer in recommending or promoting related products. For example, any product search on Amazon. We will study these techniques and their applications in Chapters 3 through 6. Siemens is a German company headquartered in Berlin, Germany.

It has an annual rev- enue of 76 billion euros. The visual analytics group of Siemens is tasked with end-to-end reporting solutions and consulting for all of Siemens internal BI needs. This group was fac- ing the challenge of providing reporting solutions to the entire Siemens organization across different depart- ments while maintaining a balance between gover- nance and self-service capabilities. Siemens needed a platform that could analyze its multiple cases of cus- tomer satisfaction surveys, logistic processes, and finan- cial reporting.

This platform should be easy to use for their employees so that they could use these data for analysis and decision making. In addition, the platform should be easily integrated with existing Siemens sys- tems and give employees a seamless user experience.

It allowed Siemens to create highly interactive dash- boards that enabled it to detect issues early and thus save a significant amount of money. The dashboards developed by Dundas BI helped Siemens global. Many organizations want tools that can be used to analyze data from multiple divisions. These tools can help them improve performance and make data discovery transparent to their users so that they can identify issues within the business easily.

Sources: Dundas. Any athletic activity is prone to injuries. If the inju- ries are not handled properly, then the team suffers.

Using analytics to understand injuries can help in deriving valuable insights that would enable coaches and team doctors to manage the team composition, understand player profiles, and ultimately aid in bet- ter decision making concerning which players might be available to play at any given time.

In an exploratory study, Oklahoma State University analyzed U. The project followed the Cross-Industry Standard Process for Data Mining CRISP-DM methodology to be described in Chapter 4 to understand the problem of making recommendations on managing injuries, understanding the various data elements collected about injuries, cleaning the data, developing visual- izations to draw various inferences, building predic- tive models to analyze the injury healing time period, and drawing sequence rules to predict the relation- ships among the injuries and the various body part parts afflicted with injuries.

Healing time was calculated for each record, which was classified into different sets of time periods: 0�1 month, 1�2 months, 2�4 months, 4�6 months, and 6�24 months. Some of the predictor variables were current status of injury, severity, body part, body site, type of injury, activity, event location, action taken, and position played.

The success of classifying the healing category was quite good: Accuracy was What is a classification problem? What can be derived by performing sequence. For any analytics project, it is always important to understand the business domain and the cur- rent state of the business problem through exten- sive analysis of the only resource�historical data. Visualizations often provide a great tool for gaining the initial insights into data, which can be further refined based on expert opinions to identify the rela- tive importance of the data elements related to the problem.

Visualizations also aid in generating ideas for obscure problems, which can be pursued in building PMs that could help organizations in deci- sion making. The third category of analytics is termed prescriptive analytics. The goal of prescriptive analytics is to recognize what is going on as well as the likely forecast and make deci- sions to achieve the best performance possible. This group of techniques has historically been studied under the umbrella of OR or management sciences and is generally aimed at.

The goal here is to provide a decision or a recom- mendation for a specific action. The decisions may be presented to a deci- sion maker in a report or may be used directly in an automated decision rules system e. Thus, these types of analytics can also be termed decision or normative analytics. We will learn about some aspects of prescriptive analytics in Chapter 8. It is almost fashionable to attach the word analytics to any specific industry or type of data.

Besides the general category of text analytics�aimed at getting value out of text to be studied in Chapter 7 �or Web analytics�analyzing Web data streams also in.

This application case is based on a project that involved one of the coauthors A company that does not wish to disclose its name or even its precise industry was facing a major problem of making decisions on which inventory of raw materials to use to satisfy which customers.

This company sup- plies custom configured steel bars to its customers. These bars may be cut into specific shapes or sizes and may have unique material and finishing require- ments. The company procures raw materials from around the world and stores them in its warehouse.

When a prospective customer calls the company to request a quote for the specialty bars meeting spe- cific material requirements composition, origin of the metal, quality, shapes, sizes, etc. It must make available-to-promise ATP decisions, which determine in real time the dates when the salesper- son can promise delivery of products that customers requested during the quotation stage.

Previously, a salesperson had to make such decisions by analyz- ing reports on available inventory of raw materials. Thus, the inventory in stock might not really be inven- tory available. On the other hand, there may be raw material that is expected to be delivered in the near future that could also be used for satisfying the order. Finally, there might even be an opportunity to charge a premium for a new order by repurposing previously committed inventory to satisfy this new order while delaying an already committed order.

Of course, such deci- sions should be based on the cost�benefit analyses of delaying a previous order. The system should thus be able to pull real-time data about inventory, committed orders, incoming raw material, produc- tion constraints, and so on.

To support these ATP decisions, a real-time DSS was developed to find an optimal assignment of the available inventory and to support additional what-if analysis. The DSS uses a suite of mixed- integer pro- gramming models that are solved using commercial software. The company has incorporated the DSS into its enterprise resource planning system to seam- lessly facilitate its use of business analytics.

Source: M. Pajouh Foad, D. Xing, S. Hariharan, Y. Zhou, B. Balasundaram, T. Sharda, R. Examples of such areas are marketing analytics, retail analytics, fraud analytics, transportation analytics, health analytics, sports analytics, talent ana- lytics, behavioral analytics, and so forth.

For example, we will soon see several appli- cations in sports analytics. The next section will introduce health analytics and market analytics broadly. Although this may result in overselling the concept of analytics, the benefit is that more people in specific industries are aware of the power and potential of analytics.

It also provides a focus to professionals developing and ap- plying the concepts of analytics in a vertical sector. Although many of the techniques to develop analytics applications may be common, there are unique issues within each vertical segment that influence how the data may be collected, processed, ana- lyzed, and the applications implemented.

Thus, the differentiation of analytics based on a vertical focus is good for the overall growth of the discipline. Even as the concept of analytics is receiving more at- tention in industry and academic circles, another term has already been introduced and is becoming popular. The new term is data science. Thus, the practitioners of data sci- ence are data scientists. Patil of LinkedIn is sometimes credited with creating the term data science.

There have been some attempts to describe the differences between data analysts and data scientists e. One view is that data analyst is just another term for professionals who were doing BI in the form of data compila- tion, cleaning, reporting, and perhaps some visualization. Their skill sets included Excel use, some SQL knowledge, and reporting. You would recognize those capabilities as descriptive or reporting analytics.

In contrast, data scientists are responsible for predic- tive analysis, statistical analysis, and use of more advanced analytical tools and algo- rithms. They may have a deeper knowledge of algorithms and may recognize them under various labels�data mining, knowledge discovery, or machine learning.

Many analytics professionals also need to build signifi- cant expertise in statistical modeling, experimentation, and analysis. Again, our readers should recognize that Ncert Solutions Class 10th Exercise 2.2 Key these fall under the predictive and prescriptive analytics umbrella. However, prescriptive analytics also includes more significant expertise in OR including optimization, simulation, and decision analysis. Those who cover these fields are more likely to be called data scientists than analytics professionals.

Our view is that the distinction between analytics professional and data scientist is more of a degree of technical knowledge and skill sets than functions. It may also be more of a distinction across disciplines. Computer science, statistics, and applied mathematics programs appear to prefer the data science label, reserving the analytics label for more business-oriented professionals. As another example of this, applied physics professionals have proposed using network science as the term for describing analytics that relate to groups of people�social networks, supply chain networks, and so forth.

We observe that graduates of our analytics programs tend to be responsible for tasks that are more in line with data. This book is clearly aimed at introducing the capabilities and functionality of all analytics which include data science , not just reporting analytics.

From now on, we will use these terms interchangeably. Any book on analytics and data science has to include significant coverage of what is called Big Data analytics. We cover it in Chapter 9 but here is a very brief introduction. Our brains work extremely quickly and efficiently and are ver- satile in processing large amounts of all kinds of data: images, text, sounds, smells, and video. We process all different forms of data relatively easily.

Computers, on the other hand, are still finding it hard to keep up with the pace at which data are generated, let alone analyze them quickly. This is why we have the problem of Big Data. So, what is Big Data? Simply put, Big Data refers to data that cannot be stored in a single storage unit. Big Data typically refers to data that come in many different forms: structured, un- structured, in a stream, and so forth.

Major sources of such data are clickstreams from Web sites, postings on social media sites such as Facebook, and data from traffic, sensors, or weather. A Web search engine such as Google needs to search and index billions of Web pages to give you relevant search results in a fraction of a second.

Although this is not done in real time, generating an index of all the Web pages on the Internet is not an easy task. Luckily for Google, it was able to solve this problem. Among other tools, it has employed Big Data analytical techniques.

There are two aspects to managing data on this scale: storing and processing. If we could purchase an extremely expensive storage solution to store all this at one place on one unit, making this unit fault tolerant would involve a major expense.

An ingenious solution was proposed that involved storing these data in chunks on different machines connected by a network�putting a copy or two of this chunk in different locations on the network, both logically and physically. However, storing these data is only half of the problem. Data are worthless if they do not provide business value, and for them to provide business value, they must be analyzed. How can such vast amounts of data be analyzed?

Passing all computation to one powerful computer does not work; this scale would create a huge overhead on such a powerful computer. Another ingenious solution was proposed: Push computa- tion to the data instead of pushing data to a computing node.

This was a new paradigm and gave rise to a whole new way of processing data. This is what we know today as the MapReduce programming paradigm, which made processing Big Data a reality.

MapReduce was originally developed at Google, and a subsequent version was released by the Apache project called Hadoop MapReduce. Other relevant standards and software solutions have been proposed.

Although the major toolkit is available as an open source, several companies have been launched to provide training or specialized analytical hardware or software services in this space. Over the past few years, what was called Big Data changed more and more as Big Data applications appeared. The need to process data coming in at a rapid rate added ve- locity to the equation. An example of fast data processing is algorithmic trading.

This uses electronic platforms based on algorithms for trading shares on the financial market, which operates in microseconds. The need to process different kinds of data added variety to the equation. Another example of a wide variety of data is sentiment analysis, which. Today, Big Data is associated with almost any kind of large data that have the characteristics of volume, velocity, and variety.

As noted before, these are evolving quickly to encompass stream analytics, IoT, cloud computing, and deep learning� enabled AI.

We will study these in various chapters in the book. Define analytics. What is descriptive analytics? What are the various tools that are employed in descrip-. How is descriptive analytics different from traditional reporting? What is a DW? How can DW technology help enable analytics?

What is predictive analytics? How can organizations employ predictive analytics? What is prescriptive analytics? What kinds of problems can be solved by prescriptive. Define modeling from the analytics perspective. Is it a good idea to follow a hierarchy of descriptive and predictive analytics before.

What is Big Data analytics?





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