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Advanced Certification in Data Science and Artificial Intelligence Course

89,994 Ratings

Top Ranked IIT Data Science Course taught by IIT Madras Faculty

Master the skills of Machine Learning & Artificial Intelligence with this advanced certification in Data Science and Artificial Intelligence by IITM Pravartak (A Technology Innovation Hub of IIT Madras) & Intellipaat. You will get to learn from the IIT Madras Data Science Faculty & industry experts. The advanced online boot camp features 1:1 mentorship by Intellipaat instructors. Be a part of 2 days campus immersion at IIT Madras upon course completion.

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Batch

50

Learning Format

Online Bootcamp

Live Classes

7 Months

Campus Immersion

at IIT Madras Pravartak

IITM Pravartak

Certification

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About Program

This Data Science and Artificial Intelligence certification course taught by IIT Madras faculty and industry experts aims to help you master all the basic and advanced level skills that are crucial in the fields of Data Science, ML, Deep Learning, and AI.

Key Highlights

400 Hrs of Applied Learning
50+ Live sessions across 7 months
218 Hrs of Self-Paced Learning
Learn from IIT Madras Faculty & Industry Practitioners
50+ Industry Projects & Case Studies
One-on-One with Industry Mentors
Resume Preparation and LinkedIn Profile Review
24*7 Support
Designed for Working Professionals & Freshers
No Cost EMI Option
Campus Immersion at IITM Research Park

About IIT Madras Pravartak Digital Skills Academy

IIT Madras Pravartak, a Technology Innovation Hub of IIT Madras is funded by the Department of Science and Technology, GoI under its National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS), focuses on application-oriented research and innovation in the areas SNACS. BharOS, India’s first mobile operating system is developed by an IIT Madras Pravartak incubatedRead More..

Key Achievements of IIT Madras:

  • Ranked No. 1 in India for both ‘Overall’ and ‘Engineering’ Categories in NIRF 2024 for 5 consecutive years.
  • IIT Madras has been identified as an ‘Institution of Eminence’ by the Government of India.
  • Ranked No. 4 as an Indian Institute in QS World University Rankings and Ranked No. 250 in the International QS World Rankings 2023.
Advanced Certification in Data Science and AI Click to Zoom
Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of the IIT Madras Pravartak.

Career Transition

55% Average Salary Hike

45 LPA Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Who Can Apply for the Data Science and Artificial Intelligence Course?

  • Individuals with a bachelor’s degree and a keen interest to learn AI and Data Science
  • IT professionals looking for a career transition as Data Scientists and Artificial Intelligence Engineers
  • Professionals aiming to move ahead in their IT career
  • Artificial Intelligence and Business Intelligence professionals
  • Developers and Project Managers
  • Freshers who aspire to build their career in the field of Artificial Intelligence and Data Science
who can apply

What roles can a person trained in Data Science & Artificial Intelligence play?

Senior Data Scientist

Understand the issues and create models based on the data gathered, and also manage a team of data scientists.

AI Expert

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

Machine Learning Expert

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

Applied Scientist

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

Big Data Specialist

Create and manage pluggable service-based frameworks that are customized in order to import, cleanse, transform, and validate data.

Senior Business Analyst

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

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Skills to Master

Python

Data Science

Data Analysis

AI

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

Model

Data visualization

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Tools to Master

python 2 jupyter 3 Scipy 2 numpy 2 pandas matplotlib tensorflow 1 SQL Power BI excel 1
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Meet Your Mentors

SHANKAR NARASIMHAN

Prof. Shankar Narasimhan

Professor at IIT Madras

From the Indian National Academy of Engineering, his research area includes data mining, process design and optimization, FDD, etc. He has co-authored several reconciliation papers and a course book, ‘Data Science for Engineers’ on the NPTEL platform.

Prof.ArunTangirala

Dr. Arun K. Tangirala

Professor, IIT Madras

Dr. Tangirala is a professor at IIT Madras with expertise in analytical chemistry and a keen interest in controller performance monitoring, time-frequency analysis of processes, fuel cells, etc.

BabjiSrinivasan

Dr. Babji Srinivasan

Associate Professor at IIT Madras

Dr. Babji Srinivasan is an assistant professor in the Department of Applied Mechanics at IIT Madras. He holds a PhD from Texas Tech University, and his research interests span from behavioral informatics to cognitive systems engineering.

NiravBhatt

Dr. Nirav Bhatt

Assistant Professor, IIT Madras

He is an assistant professor in the biotech department at IIT Madras and holds a DSc from EPFL, Switzerland. His research interests include the modeling and optimization of biological networks and Machine Learning.

Suresh Ramadurai

Suresh Ramadurai

Data Science and Analytics Expert

Suresh Ramadurai is involved in teaching market research as a visiting faculty member at various IIMs and in training analytics professionals. He is an IIT Delhi graduate with a degree in chemical engineering and holds a PG Diploma in Management from IIM Bangalore.

Ravi Hegde

Dr. Ravi Hegde

Associate Professor at IIT Gandhinagar

Dr. Ravi Hegde, who holds a PhD from the University of Michigan, Ann Arbor, has over 22 years of experience specializing in the intersections of nanotechnology and AI, nanophotonics for beyond-moore computing, and immersive sensing.

Madhu babu

Madhu Babu Cherukuri

Sr. Data Scientist at Intel Corporation

He has over 15 years of expertise handling various analytics projects, using several ML algorithms in multiple business domains. He has extensive knowledge of SAS, SQL, R, and Python and is proficient in big data environments.

Anurag

Anurag Singh

Principal AI Architect at Schneider Electric Hub

Anurag Singh works at Schneider Electric Hub as a principal AI architect. He started as a software engineer in 2007 and has been working as a data scientist since 2018. He specializes in big data, data science, machine learning, and AI system architecture.

Phaneendra

Phaneendra Akula

Senior Manager, Data Science - Ex Tesco

Phaneendra Akula is currently working at Sunrise Biztech Systems Private Limited. He has more than nine years of experience in data science and has worked in prestigious companies like Genpact, TCS, and Tesco.

Shivam

Nishkam Shivam

Senior Data Scientist - Ex-Walmart

Nishkam Shivam is a senior data scientist in analytics and AI at Bristlecone. He has more than nine years of experience in this field and has also worked in renowned companies like Accenture, Walmart, Emeritus, and many more.

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.

Vishwath

Vishwath Parthasarathy

Data Scientist-III at InMobi

Vishwath Parthasarathy is a data scientist-III at InMobi. He has also worked at Amazon and PipeCandy. His expertise extends to machine learning, deep learning, MLOps, and algorithmic trading. He has been working as a data scientist since 2017.

Satya Jayadev P

Dr. Satya Jayadev Pappu

Ph.D. from IIT Madras

Dr. Jayadev completed his Ph.D. from IIT Madras. He is currently employed as a Senior Data Scientist at GyanData and has over 11 years of experience, including working as a Project Associate at IIT Madras.

Data Science and Artificial Intelligence Course Syllabus

Live Course Self Paced Industry Expert Academic Faculty

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, 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. 
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SQL Basics – 

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

Advanced SQL –  

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

Deep Dive into User Defined Functions

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

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.
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Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.

Data Manipulation Using Pandas

  • Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.
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Descriptive Statistics – 

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

Probability 

  • Probability Distributions, Bayes’ theorem, central limit theorem. 

Inferential Statistics –  

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

Linear Algebra –

  • Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.
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Introduction to Machine Learning 

  • Supervised, Unsupervised Learning.
  • Introduction to scikit-learn, etc.

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.

Classification 

  • Introduction to classification problems, Identification of a classification problem, and 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.

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.
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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.
  • 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.

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.
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Artificial Intelligence Basics 

  • Introduction to keras API and TensorFlow

Neural Networks

  • Single Cell (perceptron)
  • Multi cell perceptron Topology
  • Weights & Biases
  • Build a NN from scratch (using numpy)

Deep Learning

  • Use cases of DL in industry
  • Difference between DS, ML, DL & AI
  • Lifecycle of Deep Learning Project
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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 various classification, regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.
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Electives:

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.

DAX 

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

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.

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  • Job Search Strategy
  • Resume Building
  • Linkedin Profile Creation
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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

50+ Live Session across 7 months
218 Hrs of Self-Paced Learning
30+ Industry Projects & Case Studies
24*7 Support

Data Science and Artificial Intelligence Projects

Projects will be a part of your Certification in Data Science & Artificial Intelligence to consolidate your learning. It will ensure that you have real-world experience in Data Science and AI.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience

Face Detection

Use Python 3.5 (64-bit) with OpenCV for face detection. The learners must ensure that the system will have to detect multiple faces in a single image. Students must work with essential libraries such as CV2 and Glob.

Restaurant Revenue Prediction

Work with Ensemble Model for predicting annual restaurant sales using various features like opening data, type of city, and type of restaurant. Work with packages like Caret, Boruta, and dplyr to analyse the dataset and predict the sales.

Build the Book Recommender Application

Work with packages like a recommended lab, dplyr, tidyr, stringr, corrplot, and many others to create your book recommender engine using the ‘user-based collaborative filtering’ model that recommends the books based on past ratings.

Census Project

Work with census income dataset from UCI Machine Learning repository that contains income information for more than 48k individuals. Use data handling techniques to handle missing values and also predict the annual income of people.

Housing Price Prediction

In this project on housing price prediction, work with a house price dataset and predict the sale price for each house with 79 explanatory variables describing every aspect of the houses.

HR Analytics

Learn to work with the HR Analytics dataset and understand how methodologies can help you to re-imagine HR problem statements. Understand the features of the dataset and in the end, evaluate the model by metric identification process.

Joke Rating Prediction

Work with the dataset taken from the famous jester online Joke Recommender system and successfully create a model to predict the ratings for jokes that will be given by the users (the same users who earlier rated another joke)

Build Recommendation Engine

Create a recommending engine by using the SVD algorithm to predict movies on Netflix based on their past ratings. Work with various packages, such as NumPy, pandas, matplotlib and Pyplot, to handle missing values from the dataset.

Legendary of Pokémon

Extract various Pokémon based on a particular parameter and use a classification model to predict the legendary Pokémon. Work with the regression algorithm to predict the attack and defence of a particular Pokémon.

Reviews

( 5 )
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.

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.

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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 latest technology.

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.

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 with the concepts and have great content.

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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.

5926b76d54843 bpfull

Shreyash Limbhetwala

Technical Delivery Lead

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

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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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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 1–2 weeks

Program Fee

Total Admission Fee

₹ 85,044 (Inclusive of All)

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EMI Starts at

₹ 5,500

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

Financing Partners

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The credit facility is provided by a third-party credit facility provider and any arrangement with such third party is outside Intellipaat’s purview.

Upcoming Application Deadline 21st November 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 19th Jan 2025 08:00 PM IST Weekend (Sat-Sun)
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Frequently Asked Questions

How will I receive my certificate?

Upon completion of the Data Science and Artificial Intelligence Course training you will receive a joint Advanced Certification in Data Science and Machine Learning from Intellipaat and IIT Madras Pravartak.

Intellipaat provides career services for all the learners enrolled in this course. IITM Pravartak is not responsible for career services.

Intellipaat career services for this program contains resume building and review of linkedin profile.

This AI Course and Data Science Course is conducted by leading experts from IIT Madras Pravartak and Intellipaat who will make you proficient in these fields through online video lectures and projects. They will help you gain in-depth knowledge in Artificial Intelligence and Data Science, apart from providing hands-on experience in these domains through real-time projects.

After completing the Data Science and Artificial Intelligence course and successfully executing the assignments and projects, you will gain an Advanced Certification in Data Science and Machine Learning from Intellipaat and IIT Madras Pravartak which will be recognized by top organizations around the world.

You can receive IBM courses worth of Rs. 13,000 with this program. You can speak to the course advisor for the same.

If you fail to attend any of the live lectures, you will get a copy of the recorded session in the next 12 hours. Moreover, if you have any other queries, you can get in touch with our course advisors or post them on our community.

There will be a two-day campus immersion module at IIT Madras Pravartak during which learners will visit the campus. You will learn from the faculty as well as interact with your peers. However, this is subject to the COVID-19 situation and guidelines provided by the institute. The cost of travel and accommodation will be borne by the learners. However, the campus immersion module is optional.

Please note that the course fees is non-refundable and we will be at every step with you for your upskilling and professional growth needs.

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.

Learner can request for 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 for. 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 is 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 enrol 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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