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PGP in Data Science and Machine Learning

Our Data Science and Machine Learning course is integrated with MITxMicroMasters and designed by domain experts to help you master data science, Python, machine learning, etc., with real-time projects. learn from the top faculty at MIT and get access to minimum 10 interviews within 6 months of the course completion. Enroll Now!

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Ranked #1 Data Science Program by India TV

Upskill for Your Dream Job

Learning Format

Online Bootcamp

Live Classes

6 Months


10 interviews




Hiring Partners

About Program

This Post Graduate program in Data Science course has been designed by industry experts to help you master essential data science skills to land into your dream job in fortune 500 companies and top startups.

Data Science course Program

Min. 10 interviews within 6 Months of Course Completion
218 Hrs of Self-Paced Learning
6 months of Live Sessions from Industry Experts
One-on-One with Industry Mentors
Dedicated Learning Managers
50+ Industry Projects & Case Studies
24*7 Support
Soft Skills Essential Training
Integrated with MIT MicroMaster Program
e-Learning Videos from MIT faculty
No-cost EMI
Suitable for Technical as well as Non-technical Graduates

Free Career Counselling

We are happy to help you 24/7

About MIT and MIT IDSS

MIT provides its students with an education that blends the excitement of discovery and invention with rigorous academic sessions. Their motto is “mens et manus”, meaning “mind and hand”, signifying the fusion of academic knowledge with practical functionality. MIT IDSS strives to advance education and research in state-of-the-art analytical methods in information andRead More..

Upon completion of this PG program on Data Science and Machine Learning, you will:

  • Receive an industry-recognized certification by Intellipaat.
  • Receive a course completion certification by MITxMicromasters upon completion of modules by MIT.
  • Minimum of 10 Job Interviews from top MNCs.

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

Who Can Apply for this Data Science Course?

  • College students in the last year of their graduation or post-graduation
  • Anyone looking for a career transition to data science and machine learning
  • IT professionals
  • Technical and Non Technical domain professionals/freshers can also apply
  • Freshers who want to pursue a career in data science and machine learning
Who can aaply

What roles can a Data Science and Machine Learning professional play?

Machine Learning Expert

With the help of several machine learning tools and technologies, they build statistical models with large chunks of business data.

Senior Data Scientist

They understand the issues and create models based on the data gathered and manage a team of data scientists.

Applied Scientist

They design and build machine learning models to derive intelligence for the services and products offered by the organization.

AI Expert

They build strategies on frameworks and technologies to develop AI solutions and help the organization prosper.

Big Data Specialist

They create and manage pluggable service-based frameworks that are customized to import, cleanse, transform, and validate data.

Senior Business Analyst

They extract data from the respective sources to perform business analysis and generate reports, dashboards, and metrics to monitor the organization’s performance.

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Skills to Master

Linear Classifiers

Perceptron Algorithm

Maximum Margin Hyperplane

Stochastic Gradient Descent

Linear Regression

Recommender Problems

Collaborative Filtering

Non-linear Classification


Neural Networks

Deep Learning

Recurrent Neural Networks


Unsupervised Learning

Generative Models

EM Algorithm

Reinforcement Learning

Natural Language Processing (NLP)

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Meet Your Mentors

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.


Live Course

MS-Excel – 

1. Excel Fundamentals 

  • Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
  • VBA Macros, Ranges and Worksheet in VBA 
  • IF conditions, loops, Debugging, etc.

2. Excel For Data Analytics 

  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

3. Data Visualization with Excel

  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

1. Python 

  • Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 

2. Linux

  • Introduction to Linux  – Establishing the fundamental knowledge of how Linux works and how you can begin with Linux OS. 
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.

1. SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

2. Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

3. Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, triggers, etc. 

4. SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions

Hands-on exercise: 

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

1. Descriptive Statistics – 

  • Measure of central tendency, measure of spread, five points summary, etc. 

2. Probability 

  • Probability Distributions, bayes theorem, central limit theorem. 

3. Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

1. Machine Learning libraries – You will learn about various libraries in python that supports machine learning like scikit-learn, keras, tensorflow, etc and other supporting libraries like pandas, numpy, pandas, matplotlib, seaborn, scipy, stats, etc. 

  1. These libraries will help you grasp a good command over the various steps involved in the machine learning life cycle like data extraction, loading, transformation, manipulation, visualization, feature engineering, feature selection, standardization, creating machine learning models, optimization, performance metrics, etc. 
  2. Other necessary verticals include statistical tests, hypothesis testing, linear algebra that covers the basics of any machine learning problem. 

2. Introduction to Machine learning 

  1. Supervised, Unsupervised learning.
  2. Introduction to scikit-learn, Keras, etc. 

3. Regression 

  1. Introduction classification problems, Identification of a regression problem, dependent and independent variables. 
  2. How to train the model in a regression problem. 
  3. How to evaluate the model for a regression problem. 
  4. How to optimize the efficiency of the regression model. 

4. Classification 

  1. Introduction to classification problems, Identification of a classification problem, dependent and independent variables. 
  2. How to train the model in a classification problem. 
  3. How to evaluate the model for a classification problem. 
  4. How to optimize the efficiency of the classification model. 

5. Clustering 

  1. Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables. 
  2. How to train the model in a clustering problem. 
  3. How to evaluate the model for a clustering problem. 
  4. How to optimize the efficiency of the clustering model.

1. Supervised Learning 

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc. 
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc. 
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.   
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach. 
  • Support Vector Machine – SVM or support vector machines for regression and classification problems. 
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function. 
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems. 
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting. 

2. Unsupervised Learning 

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

3. Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc. 
  • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model. 
  • r2, adjusted r2, mean squared error, etc.

4. Reinforcement Learning

  • Mapping the human mind with deep neural networks (dnns)
  • Several building blocks of artificial neural networks (anns)
  • The architecture of dnn and its building blocks
  • Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

5. Text Mining, Cleaning, and Pre-processing

  • Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

6. Text classification, NLTK, sentiment analysis, etc.

  • Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

7. Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

  • Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

8. AI Chatbots and Recommendations Engine 

  • Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.

1. Introduction to MLOps 

  • MLOps lifecycle
  • MLOps pipeline 
  • MLOps Components, Processes, etc.

2. Deploying Machine Learning Models 

  • Introduction to Azure Machine Learning 
  • Deploying Machine Learning Models using Azure

1. Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI. 
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

2. DAX 

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

3. Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity 
  • Embedded Power BI API and Power BI Mobile 
  • Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

The capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.
  1. Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc. 
  2. Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  3. Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  4. Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  5. Object Detection –  A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
  6. Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
  7. Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
  8. AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
  9. Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same.
  10. Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next given time period(weeks, months, years, etc.)
  11. HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing.
  12. Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems.
  13. Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.
  14. Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques.
  15. Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in early detection of various diseases.
  16. Image Classification – The case study will entail working with image data, and how simple machine learning techniques can be useful to recognize image data at the behest of well trained and optimized machine learning models.
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Program Highlights

Live Session across 6 months
Minimum 10 interviews
50+ Industry Projects & Case Studies
24*7 Support

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.


Projects in data science and machine learning will be a part of your certification to consolidate your learning and ensure that you have real-world industry experience.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience



Hear From Our Hiring Partners

Career Services By Intellipaat

Career Services

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage

Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.

Minimum 10 job Interviews

Within 6 months of course completion

Get a minimum of 10 job interviews in this unique program. You will be working on real-world capstone projects to build a strong project portfolio. Get interviewed by our 400+ hiring partners.

Access to Intellipaat portal

within 6 months of course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 500 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Our Alumni Works At

Master Client Desktop

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!


Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Submit Application

Submit Application

Tell us a bit about yourself and why you want to join this program

Application Review

Application Review

An admission panel will shortlist candidates based on their application


Application Review

Selected candidates will be notified within 3 days

Program Fee

Total Admission Fee

$ 2,632

Upcoming Application Deadline 26th June 2022

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Program Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 26th June 2022 08:00 PM IST Weekend (Sat-Sun)
Weekend (Sat-Sun) 26th June 2022 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

How long will it take for me to appear in a minimum of 10 interviews?

You will get a chance to appear and prepare for a minimum of 10 job interviews with top MNCs post the completion of your course. During this period our placement assistance services will be constantly in touch with you to facilitate the interview process.

The following is expected from the candidates during the minimum 10 interview program’ period:

  • Should give their 100% to secure a good job.
  • Attend all the career preparation sessions that are conducted
  • Remain active in job search and apply to at least 30 jobs per month
  • Once shortlisted for a job, the candidate should go through the entire selection process

The following is expected from the candidates during the course period:

  • Should be sincere and give their 100% to secure a good job.
  • Attend all the career preparation sessions that are conducted
  • Should improve on feedback provided to clear the crack interviews.
  • Remain active in job search and apply to at least 30 jobs per month
  • Once shortlisted for a job, the candidate should go through the entire selection process.
  • You should be open for relocation, mostly all our hiring partners are from metro cities.

The course will go on for 6 months along with the live classes, assignments, case studies, and project work. To effectively understand the topics and grasp the concepts, candidates are required to devote 10 hours per week. This will also help in better preparation for the job interviews.

You can always reach out to our support team. Additionally, you will be allocated dedicated teaching assistants who will also be available, apart from your instructors, for doubt clearance.

The trainers of this program are leading industry experts with tremendous knowledge in the field.

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What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work

I’m Interested in This Program

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