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PGP in Data Science and Machine Learning

100% Guaranteed Job Interviews*

Learn from MIT faculty and industry experts! Our Data Science program is integrated with an MITx course designed by MIT faculty to help you master Data Science, Python, and Machine Learning by hands-on with real-time projects. Along with this Intellipaat offers 10+ Guaranteed Interviews within 6 Months upon moving to the placement pool.

Integrated with

Only Few Seats Left

Ranked #1 Data Science Program by India TV

Upskill for Your Dream Job

Learning Format

Online Bootcamp

Live Classes

8 Months

10+ Guaranteed Interviews

by Intellipaat




Hiring Partners

About Program

This Post Graduate program in Data Science is designed by industry experts and is integrated with the MITx course designed by MIT faculty. You will learn through live classes from top industry experts and e-learning videos of MIT faculty. Moreover, 10+ guaranteed interviews will be provided during the period of 6 months when the learner is shortlisted for the placement pool along with 24/7 support assistance.

Key Highlights

10+ Guaranteed Interviews within 6 Months upon moving to the placement pool
218 Hrs of Self-Paced Learning
8 months of Live Sessions from Industry Experts
One-on-One with Industry Mentors
Dedicated Learning Management Team
50+ Industry Projects & Case Studies
24*7 Support
Essential Soft Skills Training
Receive MITx Verified Certification
e-Learning Videos from MIT faculty
No-cost EMI
Suitable for Technical as well as Non-technical Graduates
Technical Career Mentor
Revision & Doubt Clearing Sessions on Weekdays
Group Learning Activities

Free Career Counselling

We are happy to help you 24/7

About MIT and MIT IDSS

The Institute for Data, Systems, and Society (IDSS) is a cross-disciplinary unit made up of faculty from across the Massachusetts Institute of Technology (MIT). IDSS advances education and research in data analysis, statistics, and machine learning, and applies these tools in collaboration with social scientists, community, and policymakers to address complex societal challenges.

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 MITx upon completion of modules for Machine Learning with Python – From Linear Models to Deep Learning
  • 10+ Guaranteed Interviews by Intellipaat.

To know more about the MIT IDSS, click here

Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of the MITx.

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Who Can Apply for the PGP Data Science and Machine learning Course?

  • Individuals who have pursued B.E., B.Tech, B.Sc., BCA, M.E., M.Tech, M.Sc., and MCA.
  • College students in the last year of their graduation or post-graduation.
  • Any working professional up to 28 years of age.
  • Anyone looking for a career transition to data science and machine learning.
  • IT professionals.
  • Technical and Non Technical domain professionals.
  • 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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MIT Faculty

Interested in This Program? Secure your spot now.

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


Live Course Self Paced
  1. Introduction to SQL
    • Intro to Databases
    • Installation
    • Tables
  2. Data Warehousing
    • Data Warehousing
    • Data mart
    • Data Modelling
  3. DataTypes and Constraints
    • Create, Use, Drop
    • Data types
    • Constraints
    • Normalization
  4. Operators
    • Select
    • Where
    • AND, OR, NOT operators
    • Like and Between operators
  5. Joins
    • Inner join
    • Left join
    • Right join
    • Full join
    • Delete & Truncate
    • Update
    • Update& Delete using join
    • Merge
    • Alter
    • Temporary table
    • Case statement
  6. Functions
    • Inbuilt functions in SQL
    • String functions in SQL
    • Mathematical function
    • IIF function
    • User-defined functions
    • Date-time functions
    • Inline table value
    • Multi-statement table
    • Stored procedures & Views
    • Rank function
    • SQL rollup
  7. SQL Optimization and Performance
    • Pivot
    • Stuff
    • Clustered indexes non-indexes
    • Transactions
    • Triggers
    • Record grouping
    • Common table expressions
  8. Case Study – Lahman Baseball Case Study Using SQL
  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
  1. Introduction to Python and IDEs
    • The basics of the python programming language
    • how you can use various IDEs for python development like Jupyter, Colab, etc.
  2. Python Basics
    • Variables, Data Types, Loops, Conditional Statements, functions, lambda functions, file handling, exception handling ,etc.
  3. Object Oriented Programming
    • Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  4. Hands-on Sessions And Assignments for Practice
    • The culmination of all the above concepts with real-world problem statements for better understanding
  5. Case Study – Polygon Area Calculator Using Python Programming
  1. Introduction To Python Libraries
    • Introduction to NumPy, Pandas, Matplotlib ,Seaborn, etc
    • Importance of Data Analysis
    • Preprocessing Of Data – Do’s and Don’ts
  2. NumPy
    • What is NumPy?
    • Why NumPy over Lists?
    • What is NumPy Array?
    • Array Manipulations
    • Matrices
    • Numpy Linear Algebra
  3. Pandas
    • What is Pandas?
    • Pandas Series
    • Pandas Dataframes
    • Working with Various Data Sources using Pandas
    • Data Manipulation and Wrangling Using Pandas
  4. Matplotlib and Seaborn
    • What is Data Visualization?
    • Why Data Visualization?
    • What are various types of Plots
    • Plotting Categorical Data
    • Inferences from the Plots
  5. Case Study – HR Analytics Case Study
  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
  1. Descriptive Statistics
    • Measure of Central Tendency
    • Measure of Spread
    • Five Points Summary
  2. Probability Theory
    • Central Limit Theorem
    • Bayes Theorem
    • Conditional Probability
  3. Case Study
    • Advanced HR Analytics Case Study
    • EDA Case Study – Purchase Data
  1. Introduction to Machine learning
    • Supervised, Unsupervised learning
    • Introduction to scikit-learn, Keras, etc.
  2. Regression
    • Introduction classification problems, Identification of a regression problem, dependent and independent variables
    • How to train the model in a regression problem
    • How to evaluate the model for a regression problem
    • How to optimize the efficiency of the regression model
  3. Classification
    • Introduction to classification problems, Identification of a classification problem, dependent and independent variables
    • How to train the model in a classification problem
    • How to evaluate the model for a classification problem
    • How to optimize the efficiency of the classification model
  4. Clustering
    • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables
    • How to train the model in a clustering problem
    • How to evaluate the model for a clustering problem
    • How to optimize the efficiency of the clustering model
  5. 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
  6. 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
  7. 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.
  8. Case Studies
    • Concrete Data Case Study
    • Insurance Data And Scrap Price Regression Case Study
    • Weather Prediction Case Study
    • Banking Case Study
    • Heart Disease Prediction
    • Recruitment and Factory Salary Case Study
    • Customer Acquisition Cost Case Study
    • Airline Delay Prediction Case Study
    • Customer Churn Case Study
    • Online Retail Case Study
    • MNIST Digit Data Case Study For Dimensionality Reduction
    • Basketball Case Study
    • AirPassengers Influx Case Study
    • Average Temperature Case Study
  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
  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
  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
  • The Data Science 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 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 Income – 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. 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.
  6. 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.
  7. 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.).
  8. 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.
  9. 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.
  10. 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.
  1. Job Search Strategy
  2. Resume Building
  3. Linkedin Profile Creation
  4. Interview Preparation Sessions by Industry Experts
  5. Mock Interviews
  6. Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
  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
  4. Excel Power Tools
    • Power Pivot, Power Query and Power View
  5. Classification Problems using Excel
    • Binary Classification Problems, Confusion Matrix, AUC and ROC curve
    • Multiple Classification Problems
  6. Information Measure in Excel
    • Probability, Entropy, Dependence
    • Mutual Information
  7. Regression Problems Using Excel
    • Standardization, Normalization, Probability Distributions
    • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
    • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression
  1. 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
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Program Highlights

Live Session across 8 months
100% Guaranteed Job Interviews
50+ Industry Capstone 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


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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.

Guaranteed Job Interviews

Within 6 Months of Course Completion

As a part of this program, you’ll be provided with 10+ guaranteed job interviews once you are eligible for the placement pool after the completion of the course. You will be provided with a dedicated Career services mentor to help you in your job search strategy.

Access to Intellipaat Job 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 March 2023

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 March 2023 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 26th March 2023 08:00 PM IST Weekend (Sat-Sun)

Other Cohorts

Others Cohorts

Date Time Batch Type
Program Induction 25th March 2023 10:00 AM - 01:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

How will I receive my certification?

You’ll get access to the certificate upon successful completion of the program. In order to successfully complete the program learner must submit all the mandatory projects, assignments and case studies. You will receive industry-recognized certification from Intellipaat. Additionally, you will also receive course completion certification by MITx upon completion of MIT course modules provided by MIT.

Our PGP in Data Science and Machine Learning program is aimed at helping you land your dream job. It offers 10+ guaranteed interviews for you upon successful movement to the placement pool to explore various competitive openings in the corporate world and find a well-paid job that matches your profile. The final hiring decision will always be based on your performance in the interview. Rest assured, Intellipaat will be at every step with you for your upskilling and professional career growth needs.

Intellipaat offers career services to all students enrolled in the PG Data Science and Machine Learning program and is eligible to get into the placement pool. MIT is not responsible for guaranteed job interviews or career services.

To avail the  guaranteed job interviews, you’ll have to:

  • Maintain attendance of at least 85% during the live classes for each phase of the program.
  • Submit all your course-end projects and capstone projects within 7 days of program completion.
  • Mandatorily submit all assignments, projects and case studies within the due timeline.
  • Successfully complete all the course modules along with at least 80% of the self-learning videos.
  • Avoid malpractices during appearing for the test or assignment submission else you will be permanently disqualified from this program.
  • Candidates must clear the PRT ( Placement Readiness Test) after the course completion to get into the placement pool and get access to our job portal as well as the career mentoring program.

The duration of this online PG in Data Science and Machine Learning program is 8 months.

Yes. For time being, the placement  assistance program is only for Indian citizens. In case you are not a citizen of India, then you should have a work permit to work in India.

This program is designed for working professionals upto 28 years of age. You can also sign up for this PG program on Data Science and Machine Learning if you are in the final year of your bachelor’s or post-graduate program.

The following is expected from the candidates during the job-assistance program:

  • 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.

Note: Failure to comply with any of the above will result in debarring from the placement process.

In this program you will get:

  • Dedicated Learning Management team and Career Service Mentor who gives you personalised mentorship throughout your job searching process.
  • Extra sessions for doubt clearance or topic revision that will be conducted during weekdays.
  • Access to the job portal during the period of 6 months upon clearing the PRT.
  • Assistance in creating a world-class resume & Linkedin Profile from our career services team.
  • Essential soft-skills training.
  • A chance to attend mock interviews and mock interview preparation.

This program comes with 100% guaranteed job interviews and hence we only allow the candidates where we feel that we can help them to grow professionally in their career. We expect the candidates to be 100% committed to it. There is no refund applicable to this course, hence if you are enrolling in this course, we understand that you have read all the terms and conditions carefully.

To be eligible for this program, you will need to meet the following criteria:

  • Individuals who have pursued B.E., B.Tech, B.Sc., BCA, M.E., M.Tech, M.Sc., and MCA.
  • Any working professional up to 28 years of age.
  • Have a minimum of 50% throughout their academic journey (i.e. X, XII, Graduation, and Postgraduation) Have valid mark sheets and degree certificates for verification.
  • Must be allowed to legally work in India.
  • Have a valid Aadhar Card and PAN Card.
  • Must pass the background check from previous employer/institute.

Note: If you are in your final year of college, then you will be required to submit all mark sheets & certificates earned till the last semester (with at least 50% up to your last semester) and proof from your college that mentions the month and year of your graduation.

No, there are no assessments performed for you to take up this course. Anyone who is passionate about learning Data Science and Machine Learning is welcome to join our program.

To be eligible for getting into the placement pool, the learner has to complete the course along with the submission of all projects and assignments. After this, he/she has to clear the PRT ( Placement Readiness Test) to get into the placement pool and get access to our job portal as well as the career mentoring program.

Intellipaat provides you with three attempts to clear the PRT after completion of the program during the 6 months post-course completion. If you are not able to clear the PRT test in three attempts, then you are not eligible for the guaranteed job interviews from the Intellipaat side.

We will provide you with the best career services to help you with your goal to achieve a job in the desired domain. Although, cracking a job interview is totally up to the candidate’s performance during the interview. Intellipaat doesn’t guarantee a job however we are always with you for making sure that you land up into your dream job with our career services.

You will get the MIT Certification after completing the course successfully and clearing the quiz at the end, even if you do not clear the PRT. In case of not clearing the PRT, you will not be eligible to get into the placement pool.

No, if you have received a job offer outside our placement program within 6 months then you we will no longer be eligible for our career services.

The dates for the cohort for the MITx course will be announced by the MITx and the students will be provided only one of the courses. Students will be asked to enroll in the respective course whose cohort date is nearest to the time of completion of the course delivery. In order to get the certificate from MITx the student has to complete the quiz successfully and they will be issued a certificate by the MITx.

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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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