IVY is ranked in the top 10 institutes for Big data and Analytics schools in the country (Analytics India magazine, 2015 ranking). We are the official training partners of companies like Cap Gemini, Genpact, HSBC, Cognizant, eBay/Paypal etc and more than 65 Analytics companies have recruited our students (view recent placement list). Our prized faculty are associated with esteemed organizations like ISI, IIMs IITs and prestiguous US universities.
Course Outline
- Statistics Refresher
- Advanced Excel + Macro + VBA + Dashboards
- SQL & RDBMS Concepts Using MS-Access
- Analytical Problem Solving
- Effective Interviewing Skill
- Analytic Industry Resume Building Workshop
- Importance of Data for Management Decisions
- Time Series Analysis
- Probability Concepts and Applications
- Basics of Sampling and Sampling Distributions
- Theory of Estimation and Testing of Hypothesis
- Correlation and Regression Models
- Forecasting
- Theory of Attributes
- Statistical Decision Analysis
- Analysis of Variance
- Multivariate Analysis
- Base and Advanced SAS 9 Concepts
- Advanced Analytics Modeling Techniques
- Regression
- Clustering
- Factor Analysis
- Logistics
- Time Series Forecasting / ARIMA etc.
- For Projects / Case Studies in SAS, please look below in the project section
- Data Handling and Visualization in R
- Introduction and Overview of R Package
- Data Cleaning and Management in R
- Logic Building in R
- Data Visualization in R
- Exercise: Data Summarization using Financial Retail Datasets
- Data Modeling & Statistics Refresher
- Data Modeling Techniques Overview
- Missing Imputations
- Multi-collinearity Check
- Hypothesis Testing
- In-Case Study: Academic Performance Case Study
- Self-Case Study: Health Care Case Study
- Linear Regression
- Regression
- BLUE Property
- Residual Analysis
- Multiple Regression
- Model Building
- In-class Case Study: Predict Academic Performance of School Students
- Self Case Study: Predict Customer Value for an Insurance Firm
- Logistic Regression
- Model theory, Model Fit Statistics
- Reject Reference, Binning, Classing
- Dummy Creation, Dummy Correlation
- Model Development (Multicolinearity, WOE, IV, HLT, Gini KS, Rank Ordering, Clustering Check)
- Model Validation (Rerun, Scoring)
- Final Dashboard
- In-class Case Study: Predict Customer Churn for a Telecom firm
- Self Case Study: Predict Propensity to Buy Financial Product among Existing Bank Customers
- Factor Analysis & Clustering
- Factor Analysis
- Cluster Analysis
- In-class & Self Case Study: Loan Dataset
- Time Series / ARIMA Forecasting
- Univariate Time Series
- ARIMA
- In-class Case Study: Forecast Gold Prices
- Self Case Study: Forecast US Treasury Bond Prices
- Text Mining & Analysis
- Extract & Process Unstructured Text Data from Social Media Channels
- How to Choose Correct Statistical Tools for Text Analysis
- In-class Case Study: Brand Specific Twitter Stream Data
- Self Case Study: Brand Impact of Maggi Disaster on Nestle
- Introduction to Machine Learning
- How does Machine Learning Work?
- ML using Random Forest
- In-class Case Study: Create Ideal Home Buyer Profile
Course Duration
Module I – 81 hours
Module II – 21 hours
Module III – 63 hours
Module IV – 30 hours
Total – 195 hours
Course Fee
Module I + II + III + IV – INR 37,400 plus taxes
Detailed course brochure.
Eligibility
Under-grad or Post-grad in Maths/ Stats/ Economics/ Commerce/ Finance
MBA Students (All Streams)
3rd and Final Year B.E./ B.Tech
Banking/ Finance/ IT/ KPO Working Professional
Industry Projects
Real Industry Projects & Internships in SAS / R
- B2B Chemical Sales: Predict Customer Churn Score for a Chemical Manufacturing Company
- Banking & Finance: Create a Customer Ranking Scorecard for a Cross-selling Insurance Product in a Bank
- Banking & Finance: Predict Probability of Default of Customers for a Money Lending Firm
- Pharma: Predict Drug Efficacy for a Pharmaceutical Company
- Automobile Insurance: Predict Customer Total Claim Amount
- Retail: Predict Product Affinities for 100+ Products
- Rental / Real-Estate: Predict Rental Rates considering 100+ Parameters
- Aviation: Predict Airlines Revenue & Ticketing Pricing Mix
- HR: Predict Employee Attrition
- Retail Marketing: Analyse Marketing Campaign Effectiveness