Post Graduate Certificate in Data Science & Machine Learning
Discover the fundamental concepts of data science and machine learning in this 6 month programme. Learn the core competencies in the focus areas of Data Science, Machine Learning, Mathematics, and Data Visualisation. This program assumes no prior knowledge of coding in Python or R and begins with basic principles.
Dates for next cohort will be announced soon
6 months
6-8 hours per week. May vary by student.
$1,500 / INR 1,12,500
Explore flexible payment options while enroling.
100% Online
+ Live classes and feedback from faculty.
Start thinking like a data scientist
Top-ranked Institute of National Importance
Learn from the Continuing Education Centre at IIT Roorkee, ranked as a leading university in higher technological education, basic and applied research, and engineering. Earn a PG Certificate issued directly by IIT Roorkee [View sample certificate].
Live and interactive learning experience
Interact with instructors and peers to build new skills, share knowledge, and grow your professional network.
Real world projects
Showcase your new skills with an applied industry project
Program description
Overview
This Certificate builds a solid foundation in Data Science & Analytics by covering industry standard tools and techniques through a practical, industry-oriented curriculum. You’ll learn competencies in the core focus areas of Data Science, Machine Learning, Mathematics, and Data Visualisation. This program assumes no prior knowledge of coding in Python or R and begins with basic principles.
By the end of the 6-month program, you will have a solid understanding of techniques critical in performing Data Analytics and will be able to create analytical models using real-life data that delivers invaluable insights for your business and career.
Required background
No prior coding experience is required to successfully complete this program. You should, however, have exposure to high school mathematics. The course contains reading material and lectures on selected topics which bridge the gap between high school mathematics and the minimum level required to understand and use machine learning algorithms. Knowledge of basic linear algebra, calculus, and statistics will be helpful, and some experience with spreadsheets is recommended.
Webinar
In case you missed it, the Live Q&A session with the faculty on September 17th is now available to watch. Learn more about the certificate,the curriculum, and learning outcomes directly from the faculty in this video.
Skills you will gain
- Data Science
- Mathematics for Data Science
- Python Programming Language
- Exploratory Data Analysis
- Data Visualisation
- Machine Learning
5 courses, including capstone project
Course 1 of 5
Linear Algebra Basics
Overview
This course provides all conceptual knowledge from Linear Algebra required in the domain of Data Science and Machine Learning. First, you will be introduced to real vector spaces and then to the linear transformations and their representations in terms of matrices. You will learn the importance of eigenpairs in machine learning and various concepts such as orthogonality and projection.
Course 2 of 5
Foundations of Data Analytics
Overview
This course is divided into two parts. In the first part, you will be introduced to the concepts from probability theory and statistics with strong relevance in machine learning and data science. In the second part, you’ll explore gradient calculus and numerical optimization algorithms like gradient descent, stochastic gradient descent, etc.
Course 3 of 5
Programming for AI & Data Analytics
Overview
In this course, you will learn and develop the necessary knowledge and skills for the most prevalent programming languages in the Data Science domain: Python and R. You will learn Python and R basics, data structures, programming constructs, and how to work with data using key Python packages Pandas, Matplotlib, Seaborn, and R.
Course 4 of 5
Machine Learning Techniques for AI & Data Analytics
Overview
In this course, you will learn Machine Learning (ML) concepts and algorithms. This course will cover machine learning concepts and popular supervised and unsupervised learning techniques.
Course 5 of 5
Selected projects from Kaggle
Overview
As a data scientist or ML engineer you are supposed to implement the selected machine learning project and compete with your peer-group.
Through this industry style project, you will learn to:-
- Adjust to team work environments and gain industry-like experience in a machine learning project
- Familiarize oneself with Kaggle platform and understand the objective of the analytics problem in the project
- Download data and produce first prototype of the ML model
- Experiment with their ML model to improve its performance
- Submit the project and share the learnings as a team
Instructors
Frequently asked questions
If you need further assistance, please email coursera_dsml@iitr.ac.in with any questions.