Practitioners Course in Descriptive,Predictive and Prescriptive Analytics

Overview

Data analytics is a demanding field and industry is looking for potential employees who are having a practitioners approach to data analytics. This course is aimed at providing exposure to various tools and techniques along with relevant exposure to appropriate problems so that the know-how and do-how aspect of analytics, which is required by industry can be fulfilled. The course also aims at introducing various applications with the involvement of real-life practitioners so that appropriate exposure to audience who intend to build a career in this area is possible.INTENDED AUDIENCE : Students of all Engineering and Science disciplines.PREREQUISITES : The student should have completed fivesemesters of UG Engineering or Science program.INDUSTRY SUPPORT : Analytics companies – Mu Sigma, Cisco, EXL analytics, KPMG, Ernst & Young, etc. Financial companies - CapitalOne, SBI Cap, ICICI, Amex, etc. Banking sector – SBI, UBI, Reserve Bank, HDFC, HSBC, Canara Bank, Yes Bank, etc.

Syllabus

Introduction to analytics
Differentiating descriptive, predictive, and prescriptive analytics, data mining vs data analytics
Industrial problem solving process
Decision needs and analytics, stakeholders and analytics, SWOT analysis
Model and modeling process, modeling pitfalls, good modelers, decision models and business expectations,
Different types of models – overview of context diagrams, mathematical models, network models, control systems models, workflow models, capability models
Data and its types, phases of data analysis, hypothesis and data
Scales, relations, similarity and dissimilarity measures, sampling process, types of sampling, sampling strategies, error mitigation
Visualization of numeric data, visualization of non-numeric data, tools available for visualizations
Hypothesis testing, pairwise comparisons, t-test, ANOVA, Wilcoxon signed-rank test, Kruskal-Wallis test, A/B testing
Data infrastructure, analytics and BI, data sources, data warehouse, data stewardship, meta data management
Data and forecasting, super-forecasting, S-curve (lifecycle), moving average, exponential smoothing, error in forecasting
Linear correlation, correlation and causality, spearman’s rank correlation, Linear regression, logistic regression, robust regression
Hierarchical clustering (Euclidean & Manhattan), k-means clustering,Nearest neighbor, decision trees
Basics, customer lifetime value, customer probability model,Net promoter score, survival analysis
Product lifecycle analysis, Ansoff’s matrix, competitive map, Fundamentals of simulation, simulation types, Monte-Carlo simulation