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Advanced Certification in Data Science and Artificial Intelligence Course

3,237 Ratings

Learn from IIT Madras Data Science Faculty & Industry Experts with Campus Immersion at IIT Madras Research Park

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 IITM Data Science Faculty & industry experts. The advanced online boot camp features 1:1 mentorship by Intellipaat instructors.

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Batch

47

Learning Format

Online Bootcamp

Live Classes

7 Months

Campus Immersion

at IIT Madras Pravartak

500+

Hiring Partners

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About Program

This online Data Science and Artificial Intelligence advanced 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 field of Data Science, Machine Learning, Deep Learning, and Artificial Intelligence.

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 2022 for 4 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

$1,20,000 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
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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 jupyter Scipy numpy pandas matplotlib tensorflow SQL Power-BI excel
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Meet Your Mentors

Data Science and Artificial Intelligence Course Syllabus

Live Course 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.

Hands-on exercise: 

Writing comparison data between the past year and the 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).

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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

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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 1–2 weeks

Program Fee

Total Admission Fee

$ 1,229

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Upcoming Application Deadline 25th May 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 25th May 2024 08:00 PM IST Weekend (Sat-Sun)

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.

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

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.

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.

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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