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Data Analytics

Data Analytics. (Sub j ect C o de: 4 1 0 243 ) (Class: BE Co m p u ter E ngineeri n g) 2 0 1 5 P attern Designed By: Prof Vishakha Bhadane. Institute Vision "Satisfy the aspirations of youth force, who want to lead nation towards prosperity through techno-economic development."

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Data Analytics

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  1. Data Analytics (SubjectCode:410243) (Class:BEComputerEngineering) 2015Pattern Designed By: Prof VishakhaBhadane

  2. Institute Vision "Satisfy the aspirations of youth force, who want to lead nation towards prosperity through techno-economic development." Institute Mission To provide, nurture and maintain an environment of high academic excellence, research & entrepreneurship for all aspiring students, which will prepare them to face global challenges maintaining high ethical and moral standards." Department Vision Empowering the students to be professionally competent & socially responsible for techno-economic development of society. Department Mission • To provide quality education enabling students for higher studies, research & entrepreneurship • To inculcate professionalism and ethical values through day to day practices.

  3. Objectives andoutcomes CourseObjectives • – – – – Tounderstandandapplydifferentdesignmethodsandtechniques Tounderstandarchitecturaldesignandmodeling Tounderstandandapplytestingtechniques To implement design and testing using current tools and techniquesindistributed,concurrentandparallel Environments – • CourseOutcomes – – Topresentasurveyondesigntechniquesforsoftwaresystem TopresentadesignandmodelusingUMLforagivensoftware system Topresentadesignoftestcasesandimplementautomatedtesting forclientserver,distributed,mobileapplications – 2

  4. OtherInformation • TeachingSchemeLectures: –3Hrs/Week ExaminationScheme • – – InSemesterAssessment:30 EndSemesterAssessment:70 3

  5. Concepts UNIT-II Basic Data Analytic Methods 4

  6. UNIT-I CONCEPTS Syllabus Statistical Methods for Evaluation- Hypothesis testing, difference of means, wilcoxon rank–sum test, type 1 type 2 errors, power and sample size, ANNOVA. Advanced Analytical Theory and Methods: Clustering- Overview, K means- Use cases, Overview of methods, determining number of clusters, diagnostics, reasons to choose and cautions. 4

  7. 3.3 Statistical Methods for EvaluationSubsections • 3.3.1 Hypothesis Testing • 3.3.2 Difference of Means • 3.3.3 Wilcoxon Rank-Sum Test • 3.3.4 Type I and Type II Errors • 3.3.5 Power and Sample Size • 3.3.6 ANOVA (Analysis of Variance)

  8. 3.3.1 Hypothesis Testing • Basic concept is to form an assertion and test it with data • Common assumption is that there is no difference between samples (default assumption) • Statisticians refer to this as the null hypothesis (H0) • The alternative hypothesis (HA) is that there is a difference between samples

  9. 3.3.1 Hypothesis TestingExample Null and Alternative Hypotheses

  10. 3.3.2 Difference of MeansTwo populations – same or different?

  11. 3.3.2 Difference of MeansTwo Parametric Methods • Student’s t-test • Assumes two normally distributed populations, and that they have equal variance • Welch’s t-test • Assumes two normally distributed populations, and they don’t necessarily have equal variance

  12. 3.3.3 Wilcoxon Rank-Sum TestA Nonparametric Method • Makes no assumptions about the underlying probability distributions

  13. 3.3.4 Type I and Type II Errors • An hypothesis test may result in two types of errors • Type I error – rejection of the null hypothesis when the null hypothesis is TRUE • Type II error – acceptance of the null hypothesis when the null hypothesis is FALSE

  14. 3.3.4 Type I and Type II Errors

  15. 3.3.5 Power and Sample Size • The power of a test is the probability of correctly rejecting the null hypothesis • The power of a test increases as the sample size increases • Effect size d= difference between the means • It is important to consider an appropriate effect size for the problem at hand

  16. 3.3.5 Power and Sample Size

  17. 3.3.6 ANOVA (Analysis of Variance) • A generalization of the hypothesis testing of the difference of two population means • Good for analyzing more than two populations • ANOVA tests if any of the population means differ from the other population means

  18. 4.1 Overview of Clustering • Clustering is the use of unsupervised techniques for grouping similar objects • Supervised methods use labeled objects • Unsupervised methods use unlabeled objects • Clustering looks for hidden structure in the data, similarities based on attributes • Often used for exploratory analysis • No predictions are made

  19. 4.2 K-means Algorithm • Given a collection of objects each with n measurable attributes and a chosen value k of the number of clusters, the algorithm identifies the k clusters of objects based on the objects proximity to the centers of the k groups. • The algorithm is iterative with the centers adjusted to the mean of each cluster’s n-dimensional vector of attributes

  20. 4.2.1 Use Cases • Clustering is often used as a lead-in to classification, where labels are applied to the identified clusters • Some applications • Image processing • With security images, successive frames are examined for change • Medical • Patients can be grouped to identify naturally occurring clusters • Customer segmentation • Marketing and sales groups identify customers having similar behaviors and spending patterns

  21. 4.2.2 Overview of the MethodFour Steps • Choose the value of k and the initial guesses for the centroids • Compute the distance from each data point to each centroid, and assign each point to the closest centroid • Compute the centroid of each newly defined cluster from step 2 • Repeat steps 2 and 3 until the algorithm converges (no changes occur)

  22. 4.2.2 Overview of the MethodExample – Step 1 Set k = 3 and initial clusters centers

  23. 4.2.2 Overview of the MethodExample – Step 2 Points are assigned to the closest centroid

  24. 4.2.2 Overview of the MethodExample – Step 3 Compute centroids of the new clusters

  25. 4.2.2 Overview of the MethodExample – Step 4 • Repeat steps 2 and 3 until convergence • Convergence occurs when the centroids do not change or when the centroids oscillate back and forth • This can occur when one or more points have equal distances from the centroid centers • Videos • http://www.youtube.com/watch?v=aiJ8II94qck • https://class.coursera.org/ml-003/lecture/78

  26. 4.2.3 Determining Number of Clusters • Reasonable guess • Predefined requirement • Use heuristic – e.g., Within Sum of Squares (WSS) • WSS metric is the sum of the squares of the distances between each data point and the closest centroid • The process of identifying the appropriate value of k is referred to as finding the “elbow” of the WSS curve

  27. 4.2.3 Determining Number of ClustersExample of WSS vs #Clusters curve The elbow of the curve appears to occur at k = 3.

  28. 4.2.3 Determining Number of ClustersHigh School Student Cluster Analysis

  29. 4.2.4 Diagnostics • When the number of clusters is small, plotting the data helps refine the choice of k • The following questions should be considered • Are the clusters well separated from each other? • Do any of the clusters have only a few points • Do any of the centroids appear to be too close to each other?

  30. 4.2.4 DiagnosticsExample of distinct clusters

  31. 4.2.4 DiagnosticsExample of less obvious clusters

  32. 4.2.4 DiagnosticsSix clusters from points of previous figure

  33. 4.2.5 Reasons to Choose and Cautions • Decisions the practitioner must make • What object attributes should be included in the analysis? • What unit of measure should be used for each attribute? • Do the attributes need to be rescaled? • What other considerations might apply?

  34. 4.2.5 Reasons to Choose and CautionsObject Attributes • Important to understand what attributes will be known at the time a new object is assigned to a cluster • E.g., customer satisfaction may be available for modeling but not available for potential customers • Best to reduce number of attributes when possible • Too many attributes minimize the impact of key variables • Identify highly correlated attributes for reduction • Combine several attributes into one: e.g., debt/asset ratio

  35. 4.2.5 Reasons to Choose and CautionsObject attributes: scatterplot matrix for seven attributes

  36. 4.2.5 Reasons to Choose and CautionsUnits of Measure • K-means algorithm will identify different clusters depending on the units of measure k = 2

  37. 4.2.5 Reasons to Choose and CautionsUnits of Measure Age dominates k = 2

  38. 4.2.5 Reasons to Choose and CautionsRescaling • Rescaling can reduce domination effect • E.g., divide each variable by the appropriate standard deviation Rescaled attributes k = 2

  39. 4.2.5 Reasons to Choose and CautionsAdditional Considerations • K-means sensitive to starting seeds • Important to rerun with several seeds – R has the nstart option • Could explore distance metrics other than Euclidean • E.g., Manhattan, Mahalanobis, etc. • K-means is easily applied to numeric data and does not work well with nominal attributes • E.g., color

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