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- PG Program in Data Science and Business Analytics
PG Program in Data Science and Business Analytics
Quick Facts
| particular | details | ||||
|---|---|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study, Campus Based/Physical Classroom
|
Mode of Delivery
Video and Text Based
|
Frequency of Classes
Weekends
|
Course and certificate fees
Fees information
₹ 295,000
The fees for course PG Program in Data Science and Business Analytics is -
| Head | Amount |
| Programme fees | Rs. 2,95,000 + GST |
| EMI | Rs. 7,319/month |
certificate availability
Yes
certificate providing authority
The syllabus
Foundation Courses
Module 1 - Python for Data Science
Module 2 - Inferential Statistics
- Introduction to Probability
- Probability Distributions (Binomial Distribution, Normal Distribution, Uniform Distribution)
- Sampling
- Central Limit Theorem
- Point Estimation and Confidence Intervals
- Introduction to Hypothesis Testing (Null and Alternative hypothesis, p-value, One-tailed and Two-tailed Tests)
- Common Statistical Tests (z-test, t-test, Chi-square Test of Independence, ANOVA)
Analytics Techniques
Module 1 - Predictive Modeling
- Introduction to learning from data
- Simple and Multiple Linear Regression
- Regression Metrics
- Linear Regression Assumptions
- Statistical Inferences from Linear Regression
Module 2 - Machine Learning - 1
- Introduction to Logistic Regression
- Statistical Inferences from Logistic Regression
- Classification Threshold in Logistic Regression
- Classification Metrics
- Bayes Rule
- Naive Bayes Algorithm
- Distance Metrics
- KNN Algorithm
- Introduction to Decision Tree
- Impurity Measures
- Pruning
- Regression Trees
Module 3 - Machine Learning - 2
- Decision Trees
- Random Forests
- Bagging
- Boosting (AdaBoost, Gradient Boosting, XGBoost)
- K-fold Cross Validation
- Oversampling and Undersampling
- Regularization
- Data Leakage
- Hyperparameter Tuning
- GridSearchCV and RandomizedSearchCV
Module 4 - Unsupervised Learning
- K-means Clustering
- Hierarchical Clustering
- Introduction to Dimensionality Reduction
- PCA
Module 5 - Introduction to SQL
- Introduction to Databases and SQL
- Fetching data in SQL
- Filtering data in SQL
- SQL In-Built Functions (Numeric, Date, String)
- Aggregating data in SQL
- Joins
- Window Functions
- Subqueries
- Normalization
Module 6 - Time Series Forecasting
- Components of Time Series
- Naive Forecasting Methods
- Exponential Smoothening
- Stationarity
- ARIMA, SARIMA
Module 7 - Data Visualization using Tableau
- Dimensions, Measures, Data Types
- Calculations and Filtering
- Different Visualizations
- Parameters
- Sets and Blends
- Creating Interactive Dashboards
- Storyboarding
Domain Applications of Analytics
Module 1 - Marketing and Retail Analytics
- Marketing Terminologies
- RFM Analysis
- Cluster Analysis
- Churn Rate Prediction
- Market Basket Analysis
- Customer Lifetime Value (CLV) Model
Module 2 - Financial and Risk Analytics
- Introduction to Credit Risk
- Credit Risk Modelling
- PD Model - Altzman's and Discriminant Function
- Commercial Credit Risk
- Corporate Credit Risk
- Introduction to Market Risk
- Returns and Risk
- Market Risk Optimization
Capstone
Additional Modules: Learn at your Own Pace
Introduction to Data Science
- The Fascinating History of Data Science
- Transforming Industries through Data Science
- The Math and Stats underlying the technology
- Navigating the Data Science Lifecycle
Web and Social Media Analytics
- Introduction to Digital Data and Consumer Behaviour
- Google Trends
- Google Ads
- Google Analytics
- Evolution of Social Media Analytics
- Social Media Analytics
- Text Mining and Sentiment Analysis
Supply Chain and Logistics Analytics
- Forecasting Models
- Inventory Management and Classification
- Monte Carlo Simulation
- Supply Chain Network Optimization
- Supply Chain Management Strategy
Generative AI
- ChatGPT and Generative AI - Overview
- ChatGPT - Applications and Business
- Breaking Down ChatGPT
- Limitations and Beyond ChatGPT
- Generative AI Demonstrations
Introduction to Model Deployment
- Introduction to Model Deployment
- Model Serialization - Pickling
- Batch Mode and Flask
- Docker
- Kubernetes
Introduction to R
- Overview of R
- Data Types and Structures
- Loading and Manipulating Data
- Summarizing Data
- Visualization in R
Marketing and CRM
- Core Concepts of Marketing
- Marketing as a Strategic Component
- Customer Value Creation in Marketing
- Segmentation, Targeting, and Positioning Strategies
- Marketing Metrics and Analysis
Business Finance
- Fundamentals of Finance and Value Creation
- Introduction to Financial Statements
- Financial Statements Analysis
- Planning and Analyzing Operational Data
- Accounting Measurement Practices for Stakeholder Analysis
Optimization Techniques
- Understanding Decision-Making in Organizations
- Application of Linear Programming
- Exploring Integer and Mixed-Integer Programming
- Introduction to Goal Programming
- Real-life Optimization Problem-solving Strategies
Getting started with Git
- Introduction to Version Control with Git
- Repository Management and Versioning
- Branching and Merging Strategies
- Collaboration Techniques with Git
- Streamlining Development Workflow with Git
Career Assistance: Resume building & Mock Interviews
PG Certificate from UT Austin and Great Lakes
Instructors
Dr Abhinanda Sarkar
Academic Director
Great Learning
Other Bachelors, Other Masters, Ph.D
Mr Dipayan Sarkar
Professor
Freelancer
Other Masters
Mr Saurabh Aggarwal
Faculty
Great Learning
M.E /M.Tech.
Mr R Vivekanand
Operations Director
Freelancer
MBA
Dr Kumar Muthuraman
Professor
Texas McCombs
Ph.D
Dr Daniel Mitchell
Clinical Assistant Professor
University of Texas,...
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