Post Graduate Program in Data Science
Make a career as Data Scientist by completing Build a successful career in Data Science and Business Analytics.
Online
12 Months
2,10,000 INR Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Course Overview
Post Graduate Program in Data Science is a professional managerial course offered by one of the best analytics schools named Great Learning. The course is being provided by Great Learning in collaboration with The McCombs School of Business at The University of Texas at Austin and Great Lakes in India. This comprehensive Build a successful career in Data Science and Business Analytics covers all the latest analytics tools and techniques. Additionally, it allows you to explore their business applications. The course combines practical and theoretical knowledge.
Post Graduate Program in Data Science course enables the students to succeed in business roles using data analytics by practically covering the curriculum with real-world case studies. The Post Graduate Program in Data Science course is unique in the following ways.
- Industry-relevant topics covered in details
- Small groups of 15 learners with personalised mentors
- Case studies and application-based curriculum
- Hands-on experience on exposure to different tools such as Tableau, Python, R, Advanced Excel.
- Job-friendly and help you build a career in Data Science.
The Highlights
- The duration of the course is 12 months.
- The candidates must have two years of proven work experience after their Bachelor’s Degree.
- The course mode will remain online.
- After the course you can make a career as Data Scientist or Business Analyst.
Programme Offerings
- Program structure
- Industry Exposure
- World-Class Faculty
- Career
- Corporate collaboration
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Great LearningTexas McCombs |
The fees for the Post Graduate Program in Data Science course is :
| Head | Amount |
| Program Fee | ₹ 1,95,000 + GST |
| EMI | ₹ 4,604/month |
Eligibility Criteria
Post Graduate Program in Data Science also demands the eligibility criteria before joining the course. Candidates are required to have at least 2 years of work experience after their Bachelor’s Degree. They must not have less than 50 percent aggregate marks. Though graduation(minimum of 3 years) is a must for the course, it is often seen that students after their MBA/PGPM
The candidate must have completed their Bachelor’s Degree irrespective of any discipline with at least 50 percent aggregate marks.
What you will learn
After completing the course of Post Graduate Program in Data Science certification classes, you will have gained knowledge in Data Science managerial skills. With the acquired skills, you will be able to deal with any kind of business problem. As you are specialising in a particular subject (DSBA), unlike regular PGPM/MBA, you will be able to concentrate better, and chances of getting a dream job are higher. In MBA, there is a possibility you might not get a job in your preferred specialisation.
Admission Details
Post Graduate Program in Data Science has a very easy and straightforward admission process. We will discuss the various steps that need to be followed for the admission process.
Step 1: The first step is to fill the PG Program in Data Science and Business Analytics online application form at https://www.mygreatlearning.com/pg-program-data-science-and-business-analytics-course
Step 2: Enter your basic details like name, email id, phone number, education details, and work experience.
Step 3: You will also have to upload your resume to apply for the course of Build a successful career in Data Science and Business Analytics and submit your application.
Application Details
Once candidates have filled the form for the Post Graduate Program in Data Science course, the candidates will be shortlisted based on their profiles and notified through mail or call followed by their interview and admission.
While filling the admission form, you have to give 3 information
- Personal Information - In this part, you have to fill in your name, father’s name, address, etc.
- Educational Information - In this part, you have to fill in all the details of your past academics.
- Professional Information - As 2 years of work experience is mandatory, you need to fill in the details of your work.
The Syllabus
- Introduction to Data Science
- Introduction to Generative AI
- Python Programming Essentials
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
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
Text Analysis with LLMs
- Introduction to Text-to-Label Generation
- Data Preparation Process
- Introduction to Text-to-Text Generation
- Structure of Text Generation Tasks
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
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
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
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
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
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
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
NEURAL NETWORKS AND COMPUTER VISION
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
MARKETING AND RETAIL ANALYTICS
- RFM Analysis
- Cluster Analysis
- Market Basket Analysis
- Customer Lifetime Value (CLV) Model
FINANCIAL AND RISK ANALYTICS
- Credit Risk Modelling
- 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
Time Series Forecasting
Model Deployment
Masterclass on Model Context Protocol (MCP)
Evaluation process
You directly have to apply on the official website. You will get a call if they select your candidature after evaluating your resume. There are no exams as such now.
Instructors
Texas McCombs Frequently Asked Questions (FAQ's)
Yes, candidates get the certificate for the Build a successful career in Data Science and Business Analytics.
The unique features of the program include exposure to tools like R, Tableau, SAS, and Python through this program, unlike general management studies.
The Post Graduate Program in Data Science gives you relevant knowledge and skills that enable you to pursue managerial careers in analytics as a Big Data Developer, Lead Analyst, Senior Research Analyst, Research Reporting Analyst, Data Scientist and more.
At Great Learning, all the career support activities come under the GL Excelerate program. Students can access regular recruitment drives, career mentorship from professionals, up to 50 curated jobs every month, workshops on interview preparations under GL Excelerate.