PG Program in Data Science and Business Analytics

Great Learning , Texas McCombs

Online

12 Months

₹ 210000

Inclusive of GST

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekends

Course and certificate fees

Fees information

₹ 210,000 (Inclusive of GST)

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

Great Lakes Chennai (GLIM)

The syllabus

Module 1 - Python & GenAI: Pre-work

Introduction to Data Science
Introduction to Generative AI
Python Programming Essentials

Module 1 - Data-Driven Insights Using Python

Python Fundamentals for Working with Data

Variables and Data Types
Data Structures
Conditional and Looping Statements
Functions

Data Manipulation Using NumPy and Pandas

NumPy Arrays and Functions
Accessing and Modifying NumPy Arrays
Saving and Loading NumPy Arrays
Pandas Series (Creating, Accessing, and Modifying Series)
Pandas Dataframes (Creating, Accessing, Modifying, and Combining Dataframes)
Pandas Functions
Saving and Loading Datasets Using Pandas

Exploratory Data Analysis for Extracting Insights

Data Overview
Univariate Analysis (Histogram, Boxplots, and Bar Graphs)
Bivariate/Multivariate Analysis (Line Plot, Scatterplot, Lmplot, Jointplot, Violin Plot, Striplot, Swarmplot, Catplot, Pairplot, Heatmap)
Customizing Plots
Missing Value Treatment
Outlier Detection and Treatment

Module 2 - Generative Al for Text Analysis

Introduction to Prompt Engineering

Introduction to Prompts
The Need for Prompt Engineering
Different Types of Prompts (Conditional, Few-Shot, Chain-of-Thought, Returning Structured Output)
Limitations of Prompt Engineering

Module 2 - Generative Al for Text Analysis

Text Analysis with LLMs

Introduction to Text-to-Label Generation
Data Preparation Process
Introduction to Text-to-Text Generation
Structure of Text Generation Tasks

Module 3 - Decision Making with Business Statistics

Inferential Statistics Foundations

Experiments, Events, and Definition of Probability
Introduction to Inferential Statistics
Introduction to Probability Distributions (Random Variable, Discrete and Continuous Random Variables, Probability Distributions)
Binomial Distribution
Normal Distribution

Data Sampling and Inference for Accurate Insights

Sampling
Central Limit Theorem
Estimation
Introduction to Hypothesis Testing Hypothesis Formulation and Performing a Hypothesis Test
One-tailed and Two-tailed Tests
Confidence Intervals and Hypothesis Testing

Common Statistical Tests for Informed Decisions

Test for One Mean
Test for Equality of Means
Chi-square Test of Independence
One-way ANOVA

Module 4 - Predictive Analysis with Linear Regression

Introduction to Modeling Linear Regression

Introduction to Learning from Data
Simple and Multiple Linear Regression
Evaluating a Regression Model
Pros and Cons of Linear Regression

Statistical Inferences from Linear Regression

Statistician vs ML Practitioner
Linear Regression Assumptions
Statistical Inferences from a Linear Regression Model

Module 5 - Classification Techniques for Predictive Analysis

Logistic Regression for Probability-Based Insights

Introduction to Logistic Regression
Interpretation from a Logistic Regression Model
Changing the Threshold of a Logistic Regression Model
Evaluation of a Classification Model, Pros and Cons

Decision Tree for Transparent Decision Making

Introduction to Decision Tree
Different Impurity Measures
Splitting Criteria in a Decision Tree
Methods of Pruning a Decision Tree
Regression Trees, Pros and Cons

Module 6 - Robust Data Modeling with Ensembling and Tuning Techniques

Bagging Ensemble for Improved Predictive Performance

Introduction to Ensemble Techniques
Introduction to Bagging, Sampling with Replacement
Introduction to Random Forest

Boosting Ensemble for Improved Predictive Performance

Introduction to Boosting
Boosting Algorithms (Adaboost, Gradient Boost, XGBoost)
Stacking

Tuning and Validation Techniques for Optimized Model Performance

Feature Engineering
Cross-validation
Oversampling and Undersampling
Model Tuning and Performance
Hyperparameter Tuning Grid Search
Random Search
Regularization

Module 7 - Pattern Discovery with Unsupervised Learning

Insightful Data Segmentation with K-Means Clustering

Introduction to Clustering
Types of Clustering
K-means Clustering
Importance of Scaling
Silhouette Score
Visual Analysis of Clustering

Discovering Patterns with Hierarchical Clustering and PCA

Hierarchical Clustering
Cophenetic Correlation
Introduction to Dimensionality Reduction
Principal Component Analysis

Module 8 - Data Querying and Analytics with SQL

Data Retrieval and Aggregation Essentials

Introduction to Databases and SQL
Fetching Data
Filtering Data
Aggregating Data

Querying Techniques for Relational Data Analysis

In-built Functions (Numeric, Datetime, Strings)
Joins
Window Functions

Module 9 - Data Visualization Using Tableau

Storytelling with Data

Dimensions Measures
Data Types
Calculations and Filtering
Different Visualizations

Creating Interactive Dashboards

Parameters
Level of Detail Calculation
Sets and Blends
Creating Interactive Dashboards
Storyboarding

Advanced Querying for Enhanced Proficiency and Insights

Subqueries
Order of Query Execution

Module 10 - Artificial Intelligence SELF-PACED MODULES

NEURAL NETWORKS AND COMPUTER VISION

This course helps you implement neural networks to synthesize knowledge from data, introduce you to the world of computer vision, demonstrate an understanding of image processing and different methods to extract informative features from images, build convolutional neural networks (CNNs) to unearth hidden patterns in image data, and leverage common CNN architectures to solve image classification problems.

Introduction to Neural Networks

Deep Learning and History
Multi-layer Perceptron
Types of Activation Functions
Training a Neural Network
Backpropagation

Image Processing

Overview of Computer Vision
Color Pixel and Image Representation
Edge Detection
Kernels
Padding
Strides, and Pooling
Flattening to a 1D Array

Convolutional Neural Networks

ANN Vs CNN
CNN Architecture
Introduction to Transfer Learning
Common CNN Architectures

Module 11 - Business Analytics SELF-PACED MODULES

MARKETING AND RETAIL ANALYTICS

RFM Analysis
Cluster Analysis
Market Basket Analysis
Customer Lifetime Value (CLV) Model

FINANCIAL AND RISK ANALYTICS

PD Model - Altzman's and Discriminant Function
Market Risk Optimization

WEB AND SOCIAL MEDIA ANALYTICS

Google Trends and Analysis
Google Ads Google Analytics
Social Media Analytics
Text Mining and Sentiment Analysis

SUPPLY CHAIN AND LOGISTICS ANALYTICS

Forecasting Models
Monte Carlo Simulation
Supply Chain Network Optimization
Supply Chain Management Strategy

Module 12 - ADDITIONAL MODULES & MASTERCLASS: LEARN AT YOUR OWN PACE

Time Series Forecasting

Introduction to Time Series Analysis
Introduction to Forecasting
ARIMA and SARIMA

Model Deployment

Introduction to Model Deployment
Serialization and Containerization

Masterclass on Model Context Protocol (MCP)

Understand how apps talk to servers and where MCP fits in Explore how MCP simplifies complex logic using Al Live demo: Build your first MCP server from scratch Discover real-world use cases from different domains Ask your questions and get insights from the experts

Module 13 - CAPSTONE PROJECT

This course will help you identify and define a real-world problem considering factors such as data availability, feasibility, and potential impact, design and develop a Data Science solution that addresses the identified problem, explore, analyze, and process the data, apply and evaluate appropriate Data Science & GenAl to implement the solution effectively and communicate insights and implications to stakeholders.

Instructors

Dr Abhinanda Sarkar
Academic Director
Great Learning

Other Bachelors, Other Masters, Ph.D

Mr R Vivekanand
Operations Director
Freelancer

MBA

Dr Kumar Muthuraman
Professor
Texas McCombs

Ph.D

Dr Daniel Mitchell
Clinical Assistant Professor
University of Texas,...

Dr Pavankumar Gurazada
Faculty
Great Learning

Other Bachelors, MBA

Mr Mukesh Rao
Professor
Great Learning

Mr Bradford Tuckfield
Data Scientist
Freelancer

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PG Program in Data Science and Business Analytics

Online

12 Months

₹ 210000

Quick Facts

Course and certificate fees

Fees information

certificate availability

certificate providing authority

The syllabus

Module 1 - Python & GenAI: Pre-work

Module 1 - Data-Driven Insights Using Python

Python Fundamentals for Working with Data

Data Manipulation Using NumPy and Pandas

Exploratory Data Analysis for Extracting Insights

Module 2 - Generative Al for Text Analysis

Introduction to Prompt Engineering

Module 2 - Generative Al for Text Analysis

Text Analysis with LLMs

Module 3 - Decision Making with Business Statistics

Inferential Statistics Foundations

Data Sampling and Inference for Accurate Insights

Common Statistical Tests for Informed Decisions

Module 4 - Predictive Analysis with Linear Regression

Introduction to Modeling Linear Regression

Statistical Inferences from Linear Regression

Module 5 - Classification Techniques for Predictive Analysis

Logistic Regression for Probability-Based Insights

Decision Tree for Transparent Decision Making

Module 6 - Robust Data Modeling with Ensembling and Tuning Techniques

Bagging Ensemble for Improved Predictive Performance

Boosting Ensemble for Improved Predictive Performance

Tuning and Validation Techniques for Optimized Model Performance

Module 7 - Pattern Discovery with Unsupervised Learning

Insightful Data Segmentation with K-Means Clustering

Discovering Patterns with Hierarchical Clustering and PCA

Module 8 - Data Querying and Analytics with SQL

Data Retrieval and Aggregation Essentials

Querying Techniques for Relational Data Analysis

Module 9 - Data Visualization Using Tableau

Storytelling with Data

Creating Interactive Dashboards

Advanced Querying for Enhanced Proficiency and Insights

Module 10 - Artificial Intelligence SELF-PACED MODULES

NEURAL NETWORKS AND COMPUTER VISION

Introduction to Neural Networks

Image Processing

Convolutional Neural Networks

Module 11 - Business Analytics SELF-PACED MODULES

MARKETING AND RETAIL ANALYTICS

FINANCIAL AND RISK ANALYTICS

WEB AND SOCIAL MEDIA ANALYTICS

SUPPLY CHAIN AND LOGISTICS ANALYTICS

Module 12 - ADDITIONAL MODULES & MASTERCLASS: LEARN AT YOUR OWN PACE

Time Series Forecasting

Model Deployment

Masterclass on Model Context Protocol (MCP)

Module 13 - CAPSTONE PROJECT

Instructors

Articles

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