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
1,95,000 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
Module 1 - Python for Data Science
- Python Programming Fundamentals
- Data Manipulation and Analysis with NumPy and Pandas
- Data Visualization with Seaborn and Matplotlib
- Exploratory Data Analysis
- Data Preprocessing
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)
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
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
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
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.