Date Standard Module Module Title Content Details Trainer
13-Feb-19 Module 1 Introduction to Business Analytics and its tools Introduction to analytics & different terms of analytics.Need of Analytics. Business analytics vs business analysis,Business intelligence vs Data Science,Data Analyst Vs Business Analyst,Types of Analytics,Tools for Analytics Latest. Trends of   analytics Business Analytics In Practice​-Asset Health Analytics​,Supply Chain  Analytics​,Operational Analytics​,HR Analytics​,Financial Analytics​,Marketing Analytics​,Text Analytics​ Dr.Pankaj Roygupta
Module 2 Basics of Data Significance of Data​,Analyzing Data ​,Identify Types of Data Variables​,Summarizing data​,Identify Measures of central tendency​,Describe Measures of spread​,Identify Skew-ness of data distribution​, Data Collection and Management Framework​,Data Collection​,Data Dictionary​,Outlier Treatment​, Missing Value Imputation​. Standardization of scores, Standard Deviation, Standard Scores Data distribution​, Normal Distribution​, Hypothesis Testing- Developing Null and Alternative Hypotheses​,Type I and Type II Errors​ One-Tailed Tests About a Population Mean​ Two-Tailed Tests About a Population Mean.Introduction to Data Structure in R Ms. Supriya Katte
Module 3 Introduction to R (Demo) What is R? Data science & R,Components of R,Installing R,Using command line in R, Introduction to R Studio ( IDE),Finding Help & solving issues in R,Data types in R,Program Structure in R,Flow Control : For loop, If condition, While conditions and repeat loop ,Debugging tools,Concatenation of Data,Combining Vars , cbind, rbind,Sapply, apply, tapply functions, Built - in functions in R,File operations in R, Reading file, Writing to a file,Importing and exporting a file,Vectors,Lists,Scalars,Data Frames,Matrices, Arrays,Factors,Use of data structures in different conditions Mr. Atul Tripathi
Module 4 Linear & Multi-Linear Regression What is Regression?, Covariance & Correlation, Features of r (correlation)​, Testing the significance of the correlation coefficient,​Types of regression analysis, Purpose of regression analysis​, Purpose of regression analysis​,  R2  coefficient determination, Coefficient of determination (R2) and Adjusted R2, Multiple Linear Regression​, Typical Applications of Regression Analysis, Residual Analysis​. Multi-collinear​, Hetero-skedasticity​ Ms. Supriya Katte
Module 5 Clustering What is clustering?​, When to use cluster analysis?  ​Application of cluster analysis​, Types of cluster analysis ​,K means (In detail), Case Study with R Ms. Supriya Katte
Module 6 Case Study - Mr. Atul Tripathi
14-Feb-19 Module 7 Logistic Regression Logistic Regression Basics, Generalized Linear Model (glm), What is logistic regression?​,Types of logistic regression analysis​, Applications of logistic regression analysis ,Prerequisite / when & why binary logistic regression​. Ms. Supriya Katte
Module 8 Decision Tree What is decision tree?​ Why decision tree? ​Types of decision tree ​Constructing decision tree​ , Random forest and CART (In detail), Case Study with R Ms. Supriya Katte
Module 9 Introduction to machine learning Supervised, unsupervised, reinforcement learning, Machine learning and Artificial Intelligence, Machine learning and data mining, Machine learning and Business analytics Mr. Premanjan Biswas
Module 10 Case Study - Mr. Premanjan Biswas
Module 11 Time Series Modelling What is Time series​,Components of Time Series​,Techniques for forecasting​- Simple Moving Average​,Weighted Moving Average​, Simple Exponential Smoothing​, Double Exponential Smoothing​, Triple Exponential Smoothing​, Time Series Models Comparison, Use Cases, Industry Applications​, Basic Concepts (acf, pacf, AR, MA)​, ARMA Model​, ARIMA Model ​,Industry Applications​ Mr. Atul Tripathi
Module 12 Case Study - Mr. Atul Tripathi