Data Science Course: PG Level Certification at IISc

India’s #1 University (2016-24) & Research Institute (2021-24)*
12 Months
Executive Friendly
2 Campus visits of
2 days each
100%LIVE Interactive Classes

*As per NIRF Ranking 2024

3 Reasons Why This Programme is Unique

1 Learn from the Leading Faculty at IISc, India’s #1 University and Research Institute*

Designed by Faculty Members of IISc’s Department of Computational and Data Sciences
Taught by 5 Engineering and Management Sciences Faculty from IISc
Curated Industry Capstones, and option to Bring Your Own Project
360° Learning on TalentSprint’s best-in-class Pracademic Platform
12 Month Online Live Interactive Sessions
1:1 Mentoring by Industry and Academia Experts

2 Demonstrate your expertise to get an edge in your career

Build compelling Data Stories to chronicle your learning journey
Strengthen your resume by showcasing your expertise through Data Stories

3 Reboot your career for hyper-digital economy

Get ready for a new world where Data Science is transforming business and society at an unprecedented pace
Design, build and deploy solutions to take data-driven real-world decisions
Derive lifelong benefits from TalentSprint’s growing network of Data Scientists

*As per NIRF Ranking 2024

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Real-World Curriculum

Cutting-edge curriculum that reflects the latest and proven industry practices

Google Colab
Python
1. Matplotlib
2. Numpy
3. Pandas
Basic mathematics: Matrix, Calculus, Probability
Basic data visualization
Data Cleaning/Munging

Foundational Modules - 6 weeks

Importance of Calculus and Linear Algebra in Data Science
Basics of Univariate and Multivariate Calculus Vector Operations and Norms

Derivatives and Partial Derivatives
Composite Functions and the Chain Rule
Introduction to Automatic Differentiation

Gradient Descent Fundamentals
The Mechanics of Backpropagation

Vector Spaces, Bases, and Dimensions
Linear Transformations and Matrices Matrix Operations in Data Science

Principal Component Analysis (PCA): Theory and Application
Overview of Matrix Factorization Techniques (e.g., SVD, QR decomposition)

Core Modules - 28 weeks

Problem-solving strategy with data science tools, ML, and DL.

Model selection, feature importance

Least Squares; Regularization - Elastic Net, Ridge, Lasso; Bias-Variance tradeoff

Development-testing paradigm

Classification algorithms

Evaluation Metrics: MSE, Accuracy, Precision, Recall, F1 Score

Decision Tree Algorithms

Voting Classifiers, Bagging Ensemble models

Random Forests

XGBoost

Light GBM

Clustering (k-means, dbscan), Agglomerative clustering

Anomaly Detection

Overview of scalable computing in AI/ML
Understanding parallel architectures
Importance of scalability in data science

Types of parallelism: Data parallelism vs. Task parallelism
Introduction to Shared Memory (OpenMP) and Distributed Memory (MPI)

Deep dive into OpenMP and MPI
Introduction to GPUs and Nvidia accelerators
Overview of CUDA programming

JIT Compiler, Numba, and Dask
Introduction to TensorFlow distributed computing

Introduction to Dask, MLLib, cuDF, cuML, cuPY
Focused exploration of Horovod, Ray, and Rapids

Multi-Layer Perceptrons (MLP), backprop, Regression/Classification with MLP, gradient issues, and activation functions batch normalization, overfitting, drop out, optimizers and learning rate

Filtering, Convolution, pooling, Various architectures: U-net, Resnet, etc., classification, localization, segmentation (Computer Vision Applications)

Sequence modeling, memory cell, GRU, LSTM, gradient issues (NLP Applications)

PCA, Matrix Completion, LDA, CCA Manifold Learning, t-SNE, LLE
Advanced Auto-encoder type architectures

Understanding Generative AI - Introduction to models like GPT, DALL-E, Midjourney, Sora (and others) including their capabilities and limitations.

Exploring the Potential of Generative AI using APIs of large models (e.g., OpenAI).

Advanced Interaction with Generative Models - Prompt Engineering Techniques and Fine-Tuning
Retrieval-Augmented Generation (RAG)

Identifying business problems and architecting solutions incorporating generative models.
Integrating Generative AI into Applications though API integration
Navigating Ethical Implications - Privacy, bias, and copyright issues.

Relational Databases
NoSQL Databases: HBase, Graph DB
Distributed File Systems/HDFS
Cloud storage

Data Volume: Hadoop, Spark
Data Velocity: Storm, Complex Event Processing
Cloud platforms

Resilient Distributed Dataset
Transformations, Actions
Designing computational and analytics applications using Spark
Hands-on with PySpark programming

Fundamentals, Stationarity, Measures of Dependence
ARMA modeling

Market Basket Analysis (Association Rule Mining)
Optimal Financial Portfolio Allocation
Customer Churn Analysis

Capstone Projects - 12 weeks

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Capstone Projects

Real-time system
for Tweet Analytics
ChatGPT based
project
Food Image
Segmentation
Talent Retention and
Attrition Prediction
Identification of Quora question
pairs with the same intent
Stock Market predictions
based on Time Series
Prediction of Client Subscription
to a Bank term Deposit
Direct Retail Marketing efforts based on Customer Segmentation using ML based Clustering techniques
Movie Recommendation
System
Predict the future daily-demand
for a large Logistics Company
Achieving image super-resolution using a Generative Adversarial Network
Determine key factors driving literacy rate in the Indian demography using Predictive Data Analytics
Urban Crime Data Analytics
for safety improvement
Breast Cancer classification from digitized FNA image feature measurements
Exploratory and Predictive Data Analytics using Indian Premier League (IPL) dataset
Anomaly detection in Bearing
Vibration Measurements

Tools covered

Schedule

12 Months Executive friendly programme
IISc faculty members-led Live interactive classes, capstone projects,
industry sessions and campus visits to IISc
335 hours of immersive learning (245 hours of classes, practicals, case studies, mini-projects, tests, campus visits and 90 hours of tutorials)
Participants will have two campus visits of 2 days each
Classes will be held every weekend for 2-3 hours

Eligibility

Education: Bachelors (four years or equivalent) or Masters in Science /
Engineering / Management
Work Experience: Minimum 1 Year
Coding Experience: Programming Knowledge Required

Note: Graduates in other stream with relevant coding experience can apply

What is My Investment?

Application Fee
₹2,000

Program Fee ₹4,00,000 Program Fee with Scholarship ₹3,00,000 Check my eligibility for the scholarship
(18% GST extra as applicable)

Special Pricing for Corporates

Fee paid is non-refundable and non-transferable.

Modes of payment available

Internet Banking
Credit/Debit Card
UPI Payments

Easy Financing Options

12 Month 0% Interest Scheme / Interest-Based Schemes

Financing as low as ₹10,030/Month

EMI Options

Loan Partners

Programme Fee for International Participants	$5,000
With Scholarship	$3,750

Harness the power of data for
real-world decision making

PG Level Advanced Certification Programme in
Computational Data Science

3 Reasons Why This Programme is Unique

1 Learn from the Leading Faculty at IISc, India’s #1 University and Research Institute*

2 Demonstrate your expertise to get an edge in your career

3 Reboot your career for hyper-digital economy