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Data Science 

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Course Curriculum

Module1: Introduction Data Science Part -1 Referential details for Data science Business Analytics • Scope & Fact of Data Science and Business analytics • SWOT Analysis of Data Science Business Analytics • Introduction to Advanced Data Analytics • Journey Mathematics-Statistics-Econometrics • Flow chart for Data Science and Business Analytics • Data wherehouse conceptual discussions • Hadoop for Data Science • OLTP OLAP for Data information • Web Application report Module 2: Data Visualization and Summarization Part-2: Descriptive Statistics: • Descriptive Statistical • Inferential Statistics • Types of Variables • Measures of central tendency • Data Viability Dispersion • Five number Summary Analysis • Data Distribution Techniques • Exploration Techniques for Numerical data • Exploration techniques for Character Data • Visualization Exploration • Summary Exploration • Chebychev’s Inequality. Part-3: Basic Probability for Business Issues: • Simple Probability • Marginal Probability • Joint Probability • Conditional probability (linked with decision Tress Algorithms) • Bayes’ Theorem probability (linked with Naïve Bayes Algorithms) • Discrete Distributions • Binomial Distribution • Hypergeomatric Distributions • Poisson Distribution • Continuous Distributions • Normal Distribution and Properties • Scandalized Distributions Part-4: Sampling Techniques Big Data • Sampling Distributions • Simple Random • Systematic Sample • Stratified sample • Cluster Sample • Standard Error of the Mean • Skewed Std. Error • Kurtosis Std. Error • Central Limit Theorem, • Sampling from Infinity • Sampling Distributions for Mean • Sampling Distributions for proportions Module 3: Data Preparation and Quality Check Part-5: Data Validation Data Normality • Unvariate normality techniques • Bivariate techniques • Multivariate techniques • Q-Q probability plots • Cumulative frequency • Explorer analysis • Steam and leaf analysis • Histogram • Box plot • Scores for Normality Check • Kolmogorov Smirnov test • Shapiro Wilks test • Anderson darling test Part – 6 Data Cleaning process Quality check • PCA for Big Data Analysis or Unsupervised data • PCA Regression Scores for Supervised aata • Noise Data detecting • Data cleaning with Regression Residual • Data Scrubbing with statistical sense Part-7: Data Imputation and outlier treatment • Outlier treatment with robust measurements • Outlier treatment with central tendency Mean • Outlier with Min Max Likelihood methods • Outlier Detection with Density Based • Visualize Outlier Treatment • Summarized Outlier Treatment • Multivariate Outlier Detection Mahalanobis Distance • Multivariate Chi-square statistics • Outlier with Residual Analysis • Outlier Detection with PCA Analysis • Data Imputation with series Central Tendency Part-8: Test of Hypothesis • Null Hypothesis formulation • Alternative Hypothesis • Type I and Type II errors • Power Value • One tail and Two tail • One Sample T-TEST • Paired T-TEST • Independent Sample T-TEST • Analysis of Variance ( ANOVA), • MANOVA • Chi Square Test • Kendall Chi Square • Kruskal-Wallis Rank Test Chi Square • Mann-Whitney, Chi Square • Wilcoxon, Chi Square • McNemar test Chi Square Part-9: Data Transformation • Log transformation • Arcsine transformation • Box- Cox transformation • Square root transformation • Inverse transformation • Min Max Data normalization Module 4: Predictive & Estimation Models (Supervised earning) Part-10: Predictive modeling & Diagnostics • Correlation - Pearson, Kendall, Wilcox • SLR Regression • MLR Regression • Examination Residual analysis • Auto Correlation • Test of ANOVA Significant • VIF Analysis • Test of Ttest Significant • CP Indexing • Eigen Value for PCA Analysis • Homoscedasticity • Heteroskedasticity • Stepwise regression • Forward Regression • Backward Regression • Multicollinearity • Cross validation • MAPE • Check prediction accuracy • Standized regression • Quadraint Regression • Transformed Regression • Dummy Variables Regression Part-11 Logistic Regression Analysis • Logistic Regression • Discriminate Regression Analysis • Multiple Discriminant Analysis • Stepwise Discriminant Analysis • Logit function • Test of Associations • Chi-square strength of association • Binary Regression Analysis • Profit and Logit Models • Estimation of probability using logistic regression, • Wald Test statistics for Model • Hosmer Lemshow • Nagurkake R square • Pseudio R square • Maximum likelihood estimation • Model Fit • Model cross validation • Discrimination functions • AIC • BIC (Bayesian information criterion) Module 5: Advanced Big Data Analytics Part-12: Dimension Reduction Analysis • Introduction to Factor Analysis • Principle component analysis • Reliability Test • KMO MSA tests, Eigen Value Interpretation, • Rotation and Extraction steps • Varmix Models • Conformity Factor Analysis • Exploitary Factor Analysis • Factor Score for Regression Part-13: Cluster Analysis • Introduction to Cluster Techniques • Hierarchical clustering • K Means clustering • Wards Methods • Agglomerative Clustering • Variation Methods • Maximum distance Linkage Methods • Centroid distance Methods • Minimum distance Linkage Method • Cluster Dengogram, • Ecludin distance method s Module 6: Data Mining (Machine Learning) Part -14: Data Mining Machine Learning / Artificial Intelligence Functional Models • Prediction • Support Vector Machines (SVM) • Gaussian Models • Neural Network Classification Models • Binary Regression/Logit Model • Probit Model • Na¨ıve Bayes • Na¨ıve Bayes Multinomial • Ordinal Regression • Multinomial Regression • Discriminate analysis Clustering Models • DBSCAN • EM (Expectation Maximization) • K-Means Clustering • Simple Cluster • Hierarchical Cluster • k-Nearest Neighbor Classification Tree Models • Random Forests :Bagging & Boosting • Decision Stump • CHAID Analysis • C4.5 / C5.0 • J48 Pronning, Unproning • Decision trees Suvervial Analysis • Mantel—Haenszel Test • Kaplan-Meier (Product- Limit) Estimator • Cox's Proportional Hazards Model • Cox—Snell Residual • Hazard Functions • Proportional Hazards Assumption Part-15 Time series Auto Regression Models Moving Average Model Multiplicative model ARMA Model Additive Model Part-16 Model Validation and Testing • Kappa Statistics • AIC • BIC • Error/ Confusion matrices • ROC • APE • MAPE • Lift Curve • Sensitivity • Misclassification Rating • Specificity • Maximum Absolute Error • Root Final Prediction Error • Gini Coefficient • Schwarz's Bayesian Criterion Part-17 Hadoop Ecosystem (Big Data Handling) Pig Hive MapReduce Mahount

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