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Curriculum

  • Introduction to Business Analytics
  • R for Data Science
  • Introduction to R and R-Studio
  • Dealing with Data using R
  • Visualization using R
  • Descriptive Statistics
  • Introduction to Probability
  • Probability Distributions
  • Hypothesis Testing and Estimation
  • Goodness of Fit
  • Fundamentals of Finance
  • Working Capital Management
  • Capital Budgeting
  • Capital Structure
  • Core Concepts of Marketing
  • Customer Life Time Value

Analytics techniques

  • Analysis of Variance
  • Regression Analysis
  • Dimension Reduction Techniques
  • Introduction to Supervised and Unsupervised Learning
  • Clustering
  • Decision Trees
  • Random Forest
  • Neural Networks
  • Multiple Linear Regression
  • Logistic Regression
  • Linear Discriminant Analysis
  • Introduction to Time Series
  • Correlation
  • Forecasting
  • Autoregressive models
  • Handling Unstructured Data
  • Machine Learning Algorithms
  • Bias Variance trade-off
  • Handling Unbalanced Data
  • Boosting
  • Model Validation
  • Linear programming
  • Goal Programming
  • Integer Programming
  • Mixed Integer Programming
  • Distribution and Network Models

Domain exposure

  • Marketing and Retail Terminologies
  • Customer Analytics
  • KNIME
  • Retail Dashboard
  • Customer Churn
  • Association Rules Mining
  • Web Analytics: Understanding the metrics
  • Basic & Advanced Web Metrics
  • Google Analytics: Demo & Hands on
  • Campaign Analytics
  • Text Mining
  • Why Credit Risk-Using a market case study
  • Comparison of Credit Risk Models
  • Overview of Probability of Default (PD) Modeling
  • PD Models, types of models, Steps to make a good model
  • Market Risk
  • Value at Risk - using stock case study
  • Introduction to Supply Chain
  • Dealing with Demand uncertainty
  • Designing Optimal Strategy using Case Study
  • Inventory Control & Management
  • Inventory classification
  • Inventory Modeling
  • Costs Involved in Inventory
  • Economic Order Quantity
  • Forecasting
  • Advanced Forecasting Methods
  • Examples & Case Studies

Visualization and insights

  • Introduction to Data Visualization
  • Introduction to Tableau
  • Basic charts and dashboard
  • Descriptive Statistics, Dimensions and Measures
  • Visual analytics: Storytelling through data
  • Dashboard design & principles
  • Advanced design components/ principles : Enhancing the power of dashboards
  • Special chart types
  • Case Study: Hands on using Tableau
  • Integrate Tableau with Google Sheets
  • R and Python
  • Tableau
  • SAS (Online Module)
  • Hackathons
  • Group Presentation
  • Sl. No.
  • Chapter
  • Chapter - 1
  • Introduction to Analytics:-
  • Chapter - 2
  • Statistical Method for Decision Making:-
  • Chapter - 3
  • Business Finance:-
  • Chapter - 4
  • Marketing and CRM:-
  • Chapter - 5
  • Analytics techniques
  • Chapter - 6
  • Data Mining:-
  • Chapter - 7
  • Predictive Modeling:-
  • Chapter - 8
  • Time Series Forecasting:-
  • Chapter - 9
  • Machine Learning:-
  • Chapter - 10
  • Optimization Techniques:-
  • Chapter - 11
  • Domain Specialization
  • Chapter - 12
  • Visualization and insights
  • Chapter - 13
  • Capstone project

Contact Us

Masters in Data Science and Analytics, this Course and batch also available in other locations View

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