Data Science Course: PG Level Certification at IISc

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

3 Reasons Why This Data Science Course
is Unique

1 CCE-IISc Advantage

Designed by Faculty Members of Department of Computational and Data Sciences
Taught by 5 Engineering and Management Sciences faculty members 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

+91-7075767163

Data Science Course 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 4 days of 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

Data Science Course Fees

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^*

*Applicable only for enterprises nominating their employees as a group

Fee paid is non-refundable and non-transferable.

Modes of payment available

Internet Banking
Credit/Debit Card
UPI Payments

Easy Financing Options

Interest-Based Schemes

Financing as low as ₹12,710/Month

EMI Options

Loan Partners

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

Data Science Course

3 Reasons Why This Data Science Course
is Unique

1 CCE-IISc Advantage

2 Demonstrate your expertise to get an edge in your career

3 Reboot your career for hyper-digital economy

Data Science Course Curriculum

Foundational Modules - 6 weeks

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

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).

Capstone Projects - 12 weeks

Capstone Projects

Tools covered

What should you expect from this Programme?

Schedule

Eligibility

Data Science Course Fees

Easy Financing Options

Quick Links

Data Science Course

3 Reasons Why This Data Science Course is Unique

1 CCE-IISc Advantage

2 Demonstrate your expertise to get an edge in your career

3 Reboot your career for hyper-digital economy

Data Science Course Curriculum

Bridge Module- 4 weeks

Foundational Modules - 6 weeks

Module 1: Foundations of Data Science: Probability and Statistics Overall Goal: Gaining working knowledge in the math required to understand and use data science tools

Probability Axioms, Random Variables, PDF, PMF, Conditional Probability, Independence, Expectation, Variance

Describing Common Events using Probability: Bernoulli, Geometric, Binomial, Poisson, Uniform, Normal, Exponential

Statistical Learning - A/B Testing, Type I and II errors, Sample Size calculation Mini project in hypothesis testing

Module 2: Foundations of Data Science: Calculus and Matrix

Introduction to Calculus and Linear Algebra in Data Science

Deep Dive into Calculus for Data Science

Optimizing with Gradient Descent and Backpropagation

Fundamentals of Linear Algebra

Principal Component Analysis and Matrix Factorization

Core Modules - 28 weeks

Module 3: Machine Learning

Foundations of ML Problem-solving strategy with data science tools, ML, and DL. Model selection, feature importance

Regression Models Least Squares; Regularization - Elastic Net, Ridge, Lasso; Bias-Variance tradeoff Development-testing paradigm

Classification Models Classification algorithms Evaluation Metrics: MSE, Accuracy, Precision, Recall, F1 Score

Decision Trees and Random Forests Decision Tree Algorithms Voting Classifiers, Bagging Ensemble models Random Forests

Boosting Models XGBoost Light GBM

Unsupervised Learning Clustering (k-means, dbscan), Agglomerative clustering Anomaly Detection

Mini-Project: Five Mini Projects in Machine Learning

Module 4: AI at Scale

Introduction to Scalable Computing

Introduction to Parallel Computing

Deep Dive into Scalable Computing Technologies

Scalable Data Science Tools

Machine Learning at Scale with Parallel ML Libraries

Module 5: Neural Networks

Deep Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Dimensionality Reduction and Self-supervised Neural Networks

Fundamentals of NLP, attention mechanism, and transformer models.

Mini-Project in Neural Networks

Module 6: Generative AI in production

Introduction to Generative AI 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).

Enhancing Applications with Advanced Techniques

Building and Deploying Business Applications

Module 7: Data Engineering

Introduction to Big Data storage systems

Introduction to Big Data processing platforms

Deep dive into Apache Spark

Mini-Project in Data Engineering

Module 8 : Business Analytics

Time Series Modeling

Business Case Studies

Mini-Project in Business Analytics

Capstone Projects - 12 weeks

Module 9: Capstone Part 1

Module 10: Capstone Part 2

Capstone Projects

Tools covered

What should you expect from this Programme?

Schedule

Eligibility

Admission Process

Apply for the Programme

Submit Details

Await Selection

Join the Programme

Data Science Course Fees

Easy Financing Options

Quick Links

3 Reasons Why This Data Science Course
is Unique

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

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).