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PGP in Data Science and Machine Learning - Job Guarantee Program

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 with real-time projects. Intellipaat offers a 100% placement guarantee within 8 months of the course completion or you will get a refund. Enroll now!

Integrated with

Only Few Seats Left 100% Job Guarantee by Intellipaat

Ranked #1 Data Science Program by India TV

Upskill for Your Dream Job

Learning Format

Online Bootcamp

Live Classes

8 Months


Job Guarantee



No Cost EMI Starts

at ₹4000/month*

About Program

This Post Graduate program in Data Science has been designed by industry experts to land you into your dream jobs. You will learn through live classes from top Industry experts and e-learning videos of MIT faculty. Intellipaat offers a 100% placement guarantee within 8 months of course completion. If we fail to get you a job within the job-guarantee period, you will be eligible for a refund.

Data Science course with Job Guarantee Program

100% Job Guaranteed within 8 Months of Course Completion
218 Hrs of Self-Paced Learning
8 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
Receive MITx Verified Certification
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

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 cutting-edge data analysis, statistics, and machine learning, and applies these tools in collaboration with social scientists, community stakeholders, and policy makers to addressRead 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 MITx upon completion of modules by MIT.
  • 100% Job Guarantee from Intellipaat and refund in case we fail to do so.

To know more about the MIT IDSS, click here

Career Transition

55% Average Salary Hike

45 LPA Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Career Transition Handbook

Who Can Apply for the Data Science Course with Job Guarantee?

  • Anyone with 0-3 years of work experience
  • 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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MIT Faculty

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 8 months
100% Job Guarantee
20+ 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

Data Science Course with Job Guarantee program Reviews


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.


100% Job Guarantee

Within 8 Months of Course Completion

Get a 100% job guarantee with 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 Job Portal

Within 8 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

₹ 1,50,024 + GST

No Cost EMI Starts at

₹ 4,000

We partnered with financing companies to provide competitive finance option at 0% intrest rate with no hidden costs

Financing Partners

EMI Partner

Upcoming Application Deadline 24th Sep 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 24th Sep 2022 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 24th Sep 2022 08:00 PM IST Weekend (Sat-Sun)

Other Cohorts

Others Cohorts

Date Time Batch Type
Program Induction 27th Sep 2022 07:00 - 09:00 AM IST Weekdays (Tue-Fri)
Regular Classes 27th Sep 2022 07:00 - 09:00 AM IST Weekdays (Tue-Fri)

FAQs on Data Science Course with Job Guarantee

How will I receive my certification?

On the completion of the Post Graduate Program in Data Science and Machine Learning and the completion of the various projects and assignments in this program, you will receive industry recognized certification from Intellipaat. Additionally, you will receive course completion certification by MITx upon completion of modules by MIT.

Intellipaat provides career services that include Placement Guarantee for all the learners enrolled in the PG program on Data Science and Machine Learning. MIT is not responsible for placements and career services.

This Post Graduate Program in Data Science and Machine Learning is led by the experts at MIT and industry experts to help you gain mastery over this domain through various skills, tools, and industry-based projects. You will gain in-depth knowledge and hands-on experience.

After completing the course on Data Science and Machine Learning and successfully executing the assignments and projects, you will receive your certification, which will qualify you for top-paying jobs around the world. Furthermore, our career services team will prepare you for future job interviews by conducting mock interviews, preparing your resume, and more.

If you fail to attend any of the live lectures, you will get a copy of the recorded session in the LMS and you can reach out to our support team for any doubt clearance. Our support team is available to help you 24*7.

The trainers of this Data Science and Machine Learning program are top industry experts with tremendous knowledge in the field. You will get to learn from the video lessons by the leading faculty from MIT.

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.

You will get the opportunity to work on multiple projects and assignments, which will provide you with real-world industry exposure. At the end of the course, you will be working on a capstone project as well, which has a research element, and you can showcase the same to top companies for getting hired.

Classes are during the weekends i.e. Saturdays and Sundays. Committing 15 hours a week is good enough to grasp and crack the interviews. You will be expected to allocate some time for your assessments and projects as well. You will undergo multiple contests and hackathons as part of the training program.

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

Yes. You will be able to talk to professionals who work at top companies.

The duration of this online Data Science and Machine Learning placement guarantee program is 8 months.

Our placement assistance team will help you prepare well with mock interview rounds. If you have completed all the assignments and projects by the end of the course, you will not have to worry about tech interviews. You will be ready!

Yes. This placement guarantee program is only for Indian citizens for now, and you should have a work permit to work in India.

You will be awarded an industry-recognized certification after the completion of the program.

This program is only for those who are looking forward to working professionally after the course.

At the time of enrollment, you will be provided with information related to your instructors, teaching assistants, and the support team. You can contact them whenever in need.

Based on your performance, interests, and strengths, we will refer you for job interviews. You can decide to sit for as many different interviews as you want before taking up a job, but once you have taken up a job, then you will automatically move out of the placement process as we offer equal opportunities in our placements.

If you are already having an offer with any company, please share this information with the course advisor team, and we will update you on what best we can do to help you in your career growth.

The student should have some basic knowledge of programming and have decent communication skills. Besides, knowledge of Python or any OOPs language will be preferred even if you don’t have any technical skills knowledge we provide free bootcamp classes to our learners and you can learn programming in those sessions

This program is designed for working professionals with 0-3 years of experience. 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.

You can expect a salary range of Rs.7-8 lakhs from the job offer. However it totally depends on how you perform in your interview. We have seen our learners achieving up to 30 LPA in salary package.

Once you receive your certification, you will be eligible to avail the services under the Job Guarantee clause. To receive your certification, you will have to :

  • Maintain at least 85% attendance in live classes for each phase of the program
  • Submit all your course-end projects and capstone projects within 7 days of program completion.
  • Complete all the mandatory assignments, case studies and projects within the given timelines.
  • Actively participate in the hackathons & actively participate on world renowned sites like Kaggle, etc.
  • Successfully clear all the courses and modules of the program Complete at least 80% of the self-learning videos

The following is expected from the candidates during the job- guarantee 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 applying to at least 30 jobs per month
  • Once shortlisted for a job, the candidate should go through with the entire selection process

Note: Failure to comply with any of the above will result in the job-guarantee clause being terminated for the candidate.

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

  • Hold an IT degree, B.Sc., M.Sc. B.E, B.Tech, B.Com, MBA, or a degree in Finance from an accredited institution. Final year students in any of the above degree programs can also apply.
  • Have a minimum of 60% 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 60% up to your last semester) and proof from your college that mentions the month and year of your graduation.

  • In case you fail to get placed within 8 months of your eligibility date for Intellipaat’s Job Guarantee program, you will be refunded the entire program fee paid at the time of enrollment, excluding GST.
  • All interviews which are a part of our Job Guarantee Program will be scheduled only after the consent of the candidate and it is mandatory for him/her to attend these interviews referred by Intellipaat. The full refund clause will not be applicable if the candidate is not interested in the job references given by Intellipaat or does not attend the interviews at the scheduled time.
  • As a part of Intellipaat’s Job Guarantee Program for students pursuing UG/PG, the resumes will be sent to the companies only if the online program on Data Science and Machine Learning is duly completed along with all the assignments within the validity period.
  • Intellipaat will provide internship opportunities for students who are still pursuing their UG/PG, provided you have successfully completed the course assignments at Intellipaat.

This a job Guarantee program and hence we only allow the candidates where we feel that we can help them to grow professionally in their career and we expect the candidates to be 100% committed to it. There is no refund applicable to this course.

We value the candidates who wish to learn but do not have the financial bandwidth to make an upfront payment of the fees. Hence, Intellipaat offers easy No Cost e.m.i option to the candidates.

All the students who have enrolled for this Job Guarantee program and completed the course along with passing the Placement Readiness Test, are eligible to avail the career services offered by Intellipaat. The career services are provided to the students for a period of 8 months. However, if the student is unable to secure a job within these 8 months, he/she can raise a request for refund. Refund of the fees will be done within 30 days from the date of the request raised excluding Taxes.

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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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