IIIT Bangalore - Post Graduate Programme in Data Science & AI (Executive)

Offered byupGrad
Private Institute
UGCApproved
Estd. 2015

Post Graduate Programme in Data Science & AI (Executive)
at
upGrad
Overview

Duration	12 months
Start from	5 days to go
Total fee	₹2.99 Lakh
Mode of learning	Online
Official Website	Go to Website
Credential	Certificate

Post Graduate Programme in Data Science & AI (Executive)
at
upGrad
Highlights

Earn a certificate and IIIT bangalore alumni status after completion of the course
No Cost EMI Options Available
Placement assistance with 300+ hiring partners
Career mentorship Sessions(1:1)
Mock interviews and just in time interview preparation support
Hands-on projects and case studies for real world exposure

Post Graduate Programme in Data Science & AI (Executive)
at
upGrad
Course details

Skills you will learn

Python Statistics Data analysis

Who should do this course?

Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals

What are the course deliverables?

Data Processing and Transformation

Statistical Analysis

Machine Learning Models

Big Data Analytics

Visualization and Storytelling

Generative AI and Deep Learning

Data Engineering

Cloud Computing

More about this course

The graduate Programme in Data Science & AI (Executive) course starts with a solid foundation in Python and gradually transitions into advanced topics like deep learning, data engineering, and generative AI

This IIIT Bangalore data science program – coming straight from the minds of the country's best Data Analytics and Data Science instructors – ensures you develop practical, job-ready expertise to drive tangible career success

Post Graduate Programme in Data Science & AI (Executive)
at
upGrad
Curriculum

Data Toolkit

Introduction to Python

Programming in Python

Python for DS

Data Visualisation in Python

Exploratory Data Analysis

Credit EDA Assignment

Inferential Stats

Hypothesis Testing

Machine Learning - I

Linear Regression - I

Linear Regression - II + Gradient Descent for SLR

Linear Regression Assignment

Logistic Regression - I

Logistic Regression - II

Classification using Decision Trees

Unsupervised Learning: Clustering

Basics of NLP and Lexical Processing

Business Problem Solving + Intro to GIT and GITHUB

Case Study: Lead Scoring

Specialisation:

Data Analytics

Data Modelling

Advanced SQL Programming

Introduction to Cloud and AWS

Analytics at Large Scale in Spark - I

Analytics at Large Scale in Spark - II

Big Data Case Study

Basic Viz. using Tableau

Business Analytics

Bagging & Random Forests

Model Selection

Time Series Forecasting

Model Selection Case Study

Basic Viz. using Tableau

Advanced Excel

Data Analysis and Visualisation in Power BI

Analytical Thinking and Structured Problem Solving using Frameworks

Data Storytelling

Deep Learning

Bagging & Random Forests

Boosting

Model Selection

PCA

Advanced Regression + Time Series Forecasting (Optional)

Advanced ML Case Study

Natural Language Processing

Bagging & Random Forests

Boosting

Model Selection

PCA

Advanced Regression + Time Series Forecasting (Optional)

Advanced ML Case Study

Neural Nets for NLP

Syntactic Processing

Data Engineering

Data Management and Relational Database Modelling

Introduction to Cloud and AWS Setup

Introduction to Hadoop and MapReduce Programming

NoSQL Databases and Apache HBase

Data Ingestion with Apache Sqoop and Apache Flume

MapReduce Programming Assignment

Hive and Quering + Optional Assignment

Post Graduate Programme in Data Science & AI (Executive)
at
upGrad
Faculty details

Chandrashekar Ramanathan

Prof. Chandrashekar has a Ph.D. from Mississippi State University and experience of over 10 years in several multinational organizations

Tricha Anjali

Prof. Anjali has received her Integrated M.Tech. (EE) from Indian Institute of Technology, Bombay in 1998 and Ph.D. from Georgia Institute of Technology in Atlanta in 2004. She has been with the Department of Electrical and Computer Engineering at Illinois Institute of Technology, Chicago since 2004. Her broad research interests include computer networks and wireless networks.