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PGP in Data Science and Machine Learning Course

6,209 Ratings

Ranked #1 Data Science Program by India TV

Fuel your career in data science and machine learning. Get 100% job interviews guaranteed with our data science and machine learning course.

  • Data Science and Machine Learning course taught by Industry Experts
  • Master Data Science, Python, and Machine Learning with real-time projects
  • Gain the skills and knowledge needed to become a certified Data Scientist
  • Placement assistance upon movement to the placement pool

Integrated with

MITxMicromaster-MS
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Learning Format

Online Bootcamp

Live Classes

8 Months

100%

Job Opportunities Guaranteed*

MITx

Certification

500+

Hiring Partners

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About Data Science and Machine Learning Course

This PG program in Data Science has been designed by industry experts to help you land your dream jobs. You will learn through live classes from experts and e-learning videos of MIT faculty.

Key Highlights

100% Job Opportunities Guaranteed within 8 Months of Course Completion 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
Dedicated Technical Career Mentor
Revision & Doubt Clearing Sessions on Weekdays
Capstone Projects Completion Certificate to make you Industry-Ready
Group Learning Activities
Certifications from Microsoft
Free AZ-900: Microsoft Azure Fundamentals worth $99

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.Read More..

Upon completion of this Data Science and Machine Learning Course, 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. ( This is provided to only those learners who have purchased vouchers by paying an additional Rs.30,000)
  • 10 Guaranteed Interviews by Intellipaat upon movement to the placement pool.

To know more about the MIT IDSS, click here

MITX-Sample-Certificate-Small-1 Click to Zoom
Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of the MITx.

Program in Collaboration with Microsoft

Benefits for students from Microsoft:

  • Official study material from Microsoft
  • Industry-recognized certification from Microsoft
  • Real-time projects and exercises
Az Click to Zoom

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

300+ Hiring Partners

*Past record is no guarantee of future job prospects

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

  • Individuals who have pursued B.E., B.Tech, B.Sc., BCA, B.Com, M.Com, 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 30 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-apply

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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Meet your Mentors & MIT Faculty

Karene

Karene Chu

Digital Learning Scientist and Research Scientist, MIT

Karene Chu holds a Ph.D. in mathematics and is a postdoctoral fellow at MIT, with a research focus on knot theory. She has experience teaching single and multi-variable calculus and linear algebra.

Tommi

Tommy S. Jaakkola

Professor, MIT

Tommy S. Jaakkola is the Thomas Siebel Professor in EECS and Institute for Data, Systems, and Society at MIT. His research includes algorithms, applications of ML, NLP, ML for chemistry, and more.

Regina

Regina Barzilay

Professor, MIT

Regina Barzilay is a Delta Electronics Professor in the Department of Electrical Engineering and Computer Science at MIT. Her research interests lie in NLP, applications of deep learning in chemistry, and oncology.

Muthusamy-Manigandan

Muthusamy Manigandan

Data Science Consultant, Ex-Apple

He comes with more than 16 years of experience and worked with top MNCs like Apple, Amazon, etc.

Shajee

Shajee Pulukkool

Head - Data & Analytics at Rapyder Cloud Solutions

Shajee Pulukkool is working as the head of Data & Analytics. He comes with 22+ years of experience in software design & solutions.

Srikanth

Srikanth Sharma

Engagement Manager at Amazon

He has 15+ years of experience in developing and architecting cloud solutions at Amazon. He has also worked with Microsoft, Tesla, and Wipro.

Pankaj

Pankaj Saxena

Sr. Director- Cloud Operations at Oracle

He carries 24 years of experience in managing complex and heterogeneous infrastructure alongside delivering cloud-based software solutions to customers. He has vast experience in multiple tools and technologies and in building complex systems.

Curriculum

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
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  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
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  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
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  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 the various types of Plots
    • Plotting Categorical Data
    • Inferences from the Plots
  5. Case Study – HR Analytics Case Study
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  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
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  1. Descriptive Statistics
    • The measure of Central Tendency
    • Measure of Spread
    • Five Points Summary
  2. Probability Theory
    • Central Limit Theorem
    • Bayes Theorem
    • Conditional Probability
  3. Inferential Statistics
    • Correlation
    • Covariance
    • Confidence intervals
    • Hypothesis testing
    • F-test, Z-test, t-test, ANOVA, chi-square test, etc.
  4. Case Study
    • Advanced HR Analytics Case Study
    • EDA Case Study – Purchase Data
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  1. Introduction to Machine Learning
    • Supervised, Unsupervised learning.
    • Introduction to scikit-learn, Keras, etc.
  2. Regression
    • Introduction to regression 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 – The gradient descent algorithm is an iterative optimization approach to finding the 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 multidimensional 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 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, and 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
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  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
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  1. Power BI Basics
    • Introduction to Power BI, 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
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  1. Regular Test & Performance Monitoring by Technical Mentors
  2. Interview Preparation Sessions
  3. Hackathons / Projects Evaluation & Solutioning
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Artificial Intelligence Basics 

  • Introduction to Keras API and tensorflow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning 

  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted Boltzmann machine 
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  • The Data Science capstone project focuses on establishing a stronghold 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 a 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.
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  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 feature engineering and feature selection and how they offer 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 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 the early detection of various diseases.
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  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.
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  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 Tables, 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
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  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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Disclaimer
Intellipaat reserves the right to modify, amend or change the structure of module & the curriculum, after due consensus with the university/certification partner.

Program Highlights

Live Session across 8 months
100% Job Opportunities Guaranteed*
20+ Industry Capstone Projects & Case Studies
24*7 Support

Projects

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

Working with NumPy

Get hands-on training to successfully work with the NumPy library to solve various Python problems. Also, the project requires you to efficiently create 2D arrays and perform simple arithmetic operations on the two arrays.

Visualizing and Analyzing the Customer Churn Dataset using Python

Analyze data by building aesthetic graphs to make better sense of it. Also, work with the bar plots and their applications which also include histogram graphs for data analysis, and box plots and outliers in them.

Deal with Financial Data

The project is included to provide learners with hands-on experience in financial data analysis. Analyze global sales numbers and profit data by developing an interactive map, and using map styles and layers for enhanced visualization.

Work with Agricultural Data

As a part of the project, learners will have to display district-wise data to successfully build an interactive treemap with state labels. Also, the districts must be displayed by hovering over the respective states.

Analyzing Naming Trends using Python

This project involves the analysis of naming trends using Python. Also, use the Python programming language to understand the applications of data manipulation, extracting files with data, and concepts of data visualization.

Performing Analysis of Customer Churn Dataset

As an important part of the project, learners will be required to analyze employment reliability in the telecom industry, and further work on real-time analysis of data with multiple labels, and data visualization for reliability factors.

Movie Recommendation

This movie recommendation project will allow you to use the Apache Spark MLlib component and statistical analysis to successfully create collaborative filtering, and as well as regression, clustering, and dimensionality reductions.

Web Scraping

The project gives practical exposure to the applications of Python in web scraping. Also get a chance to work on various web scraping libraries, Beautiful Soup, Navigable String, parser, searching tree deployment, and more.

Twitter Sentiment Analysis

The project lets you use and successfully apply Twitter sentiment analysis to find the reaction of people concerning the demonetization move by India by analyzing their tweets. Also, download the tweets and load them into Pig storage.

Market Basket Analysis

The project requires learners to identify trends in the company’s inventory dataset to increase sales numbers. Moreover, they also need to implement data extraction, manipulation, etc. for market basket analysis.

Credit Card Fraud Detection

In this project, you will work on a system that analyses user transactions and behavior. Based on this analysis, your system will identify parameters for unusual behavior in the system, such as incorrect passwords.

Prediction of Loan Approval

The project involves using banking datasets to analyze, clean, process, and visualize the data. Once the data analysis is completed, the project also lets you learn to implement Naive Bayes and principal component analysis.

Analyzing the Trends of COVID-19 with Python

Use the Pandas module to collect data from various files. Also, use Plotly to build interactive visualizations. The project also requires you to use the Prophet library from Facebook to efficiently develop time-series models.

Analyzing IPL T20 Cricket

This interesting project has been included to help you analyze an entire IPL T20 cricket match and get some details of the match. As a next step, load the IPL dataset into HDFS and analyze the data using Apache Pig or Hive.

Data Science and Machine Learning Course Reviews

( 5 )
anoop

Anoop Prasad

Assistant Professor at EWIT

The training and support team are highly cooperative. The best thing about it is the prompt support. The trainers are well-versed in the concepts and have great content.

Manas-Ranajan

Manas Ranjan

Data Analyst at Amazon

I am happy to have enrolled in this data science program. The syllabus is organized and the course is well designed. The best features are the 24*7 support and trainers who are domain experts.

5926b76d54843-bpfull

Shreyash Limbhetwala

Technical Delivery Lead

Intellipaat offers well curated rich learning content which I liked the most. The set of PPTs, PDFs, and other course materials were of the highest quality, due to which my learning was great too.

5e0f2a1ba6d11-bpfull

Sudipto

PS Consultant at Genesys

This advanced certification is outstanding. The trainer is an experienced Data Scientist who has a good hold on the concepts. Now I am a confident data science professional and joined a reputed firm.

Adarsh-Vijay

Adarsh Vijay

Data Analyst at Maxgen Technologies

Best Data science course with placements. I was able to upgrade my skills with the help of the rich content and expert training by instructors who carried good experience in the domains.

5b30a107b2e13-bpfull

Vikrant Singh

Senior Big Data Consultant at Amazon

It was a wonderful learning experience to learn from the trainers at Intellipaat. They were hands-on and provided real-time scenarios. Intellipaat is the right place for learning the latest technology.

Afsana-Zaman

Afsana Zaman

Data Scientist

Great learning experience with this course. The support team was always available. The collaboration of practical with theoretical knowledge makes it highly suitable for those who want to upskill.

Career Services By Intellipaat

Career Services
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Job Opportunities Guaranteed*
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Access to Intellipaat Job Portal
Mock Interview Preparation
1 on 1 Career Mentoring Sessions
resume
Career Oriented Sessions
linkedin
Resume & LinkedIn Profile Building
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Our Alumni Works At

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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.

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Submit Application

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

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Application Review

An admission panel will shortlist candidates based on their application

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Admission

Selected candidates will be notified within 3 days

Program Fee

Total Admission Fee

$ 2,632

Apply Now

Upcoming Application Deadline 16th Nov 2024

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 16th Nov 2024 08:00 PM - 11:00 PM IST Weekend (Sat-Sun)
Regular Classes 17th Nov 2024 10:00 AM - 01:00 PM IST Weekend (Sat-Sun)

Data Science and Machine Learning Course FAQs

How will I receive my certification?

On completion of the Data Science and Machine Learning Course with mandatory projects, assignments, and quizzes, you will receive industry-recognized certification from Intellipaat. Upon completion of the MITx course for Machine Learning with Python – From Linear Models to Deep Learning you will receive a certificate from MIT.

Yes, you need to pay an additional amount of Rs. 30,000/-

Intellipaat provides career services that include Placement assistance for all the learners enrolled in the PG program on Data Science and Machine Learning Course. 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 Data Science and Machine Learning course 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.

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, M.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.

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 following is expected from the candidates during the job opportunities 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 apply for 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 the job opportunities guarantee clause being terminated for the candidate.

The trainers of this Data Science and Machine Learning course 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, Anyone who is passionate about learning Data Science and Machine Learning is welcome to join our program

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 resolve all your queries and doubts 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 opportunities 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 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.

If you 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.

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

The student should have some basic knowledge of programming and have decent communication skills. Besides, knowledge of Python or any OOP language will be preferred even if you don’t have any technical skills knowledge we provide free boot camp 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 depends on how you perform in your interview. We have seen our learners securing a package of up to 30 LPA.

Once you receive your certification, you will be eligible to avail services under the Job Opportunities 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 hackathons & world-renowned sites like Kaggle, etc.
  • Successfully clear all the courses and modules of the program and complete at least 80% of the self-learning videos
  • 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. Placement opportunities will be provided to those candidates who made it to the placement pool
  • All interviews which are a part of our Job Opportunity Program will be scheduled only after the consent of the candidate and he/she must attend these interviews referred by Intellipaat.
  • As a part of Intellipaat’s Job Opportunities Guarantee Program for students pursuing UG/PG, the resumes will be sent to the companies only if the online Data Science and Machine Learning course is duly completed along with all the assignments within the validity period.
  • Intellipaat will provide 1-year internship opportunities for students who are still pursuing their UG/PG, provided you have successfully completed the course assignments at Intellipaat.

This a Job Opportunities 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 an easy No-cost EMI option to the candidates.

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. To get the certificate from MITx the student has to complete the quiz successfully and they will be issued a certificate by MITx.

All the students who have enrolled for the Job Opportunities 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 8 months.

Due to any reason you want to defer the batch or restart the classes in a new batch then you need to send the batch defer request on [email protected] and only 1 time batch defer request is allowed without any additional cost.

Learners can request a batch deferral to any of the cohorts starting in the next 3-6 months from the start date of the initial batch in which the student was originally enrolled. Batch deferral requests are accepted only once but you should not have completed more than 20% of the program. If you want to defer the batch 2nd time then you need to pay batch defer fees which are equal to 10% of the total course fees paid for the program + Taxes.

Yes, Intellipaat certification is highly recognized in the industry. Our alumni work in more than 10,000 corporations and startups, which is a testament that our programs are industry-aligned and well-recognized. Additionally, the Intellipaat program is in partnership with the National Skill Development Corporation (NSDC), which further validates its credibility. Learners will get an NSDC certificate along with Intellipaat certificate for the programs they enroll in.

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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

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