Course Content
Introduction to Data Science Methodologies
- Data Types
- Introduction to Data Science Tools
- Statistics
- Approach to Business Problems
- Numerical Categorical
- R, Python, WEKA, RapidMiner
- Hypothesis testing: Z, T, F test Anova, ChiSq
Correlation / AssociationRegressionCategorical variables
- Introduction to Correlation Spearman Rank Correlation
- OLS Regression – Simple and Multiple Dummy variables
- Multiple regression
- Assumptions violation – MLE estimates
- Using UCI ML repository dataset or Built-in R dataset
Data Preparation
- Data preparation & Variable identification
- Advanced regression
- Parameter Estimation / Interpretation
- Robust Regression
- Accuracy in Parameter Estimation
- Using UCI ML repository dataset or Built-in R dataset
Logistic Regression
- Introduction to Logistic Regression
- Logit Function
- Training-Validation approach
- Lift charts
- Decile Analysis
- Using UCI ML repository dataset or Built-in R dataset
Cluster AnalysisClassification Models
- Introduction to Cluster Techniques
- Distance Methodologies
- Hierarchical and Non-Hierarchical Procedure
- K-Means clustering
- Introduction to decision trees/segmentation with Case Study
- Using UCI ML repository dataset or Built-in R dataset
Introduction and to Forecasting Techniques
- Introduction to Time Series
- Data and Analysis
- Decomposition of Time Series
- Trend and Seasonality detection and forecasting
- Exponential Smoothing
- Building R Dataset
- Sales forecasting Case Study
Advanced Time Series Modeling
- Box – Jenkins Methodology
- Introduction to Auto Regression and Moving Averages, ACF, PACF
- Detecting order of ARIMA processes
- Seasonal ARIMA Models (P,D,Q)(p,d,q)
- Introduction to Multivariate Time-series Analysis
- Using built-in R datasets
Stock market prediction
- Live example/ live project
- Using client given stock prices / taking stock price data
Pharmaceuticals
- Case Study with the Data
- Based on open set data
Market Research
- Case Study with the Data
- Based on open set data
Machine Learning
- Supervised Learning Techniques
- Conceptual Overview
- Unsupervised Learning Techniques
- Association Rule Mining Segmentation
Fraud Analytics
- Fraud Identification Process in Parts procuring
- Sample data from online
Text Analytics
- Text Analytics
- Sample text from online
Social Media Analytics
- Social Media Analytics
- Sample text from online
