Frequently Asked Questions

Most Frequently Asked Questions

What are the programme outcomes?

The programme equips professionals with both academic, research insights coupled with practical knowledge and helps navigate the fast-evolving world of Data Science and allied areas. You will earn a certification from Centre for Continuing Education at IISc.

You will learn from the leading IISc faculty members and work on interesting use-cases and research projects.

Network and associate with current and future Data Science practitioners.

Is the program approved?

Certificate programs are approved by the Centre for Continuing Education at IISc. As an Institution of Eminence, IISc enjoys considerable autonomy. The program complies with all the relevant regulations applicable. The programs are offered by TalentSprint in collaboration with CCE-IISc.

What kind of infrastructure do I need to access the digital learning platform?

You will need the following infrastructure to access our platform:

  1. Device: A Desktop/Laptop/Tablet/Smartphone with camera and mic.
  2. Internet: A regular broadband/wifi connection or a mobile 4G connection

Note: Proctored online assessments can be taken only on a Desktop/Laptop with a web camera and mic (not allowed on tablets/smartphones).

How will I interact with my group members for a group activity/project?

Groups can conveniently connect among themselves at an appointed time for group work, outside the online classes. This feature comes in handy during group activities and projects.
Students can also use the platform 24 x 7 to engage and learn from their peers using our interactive discussion forums available under each course of the programme.
Our platform also has the provision of group-specific discussion forums for offline discussion among the group members.

What are the benefits of AI-powered recorded videos?

Our recorded videos have AI-generated Table of Content (TOC) to help easy and smart navigation within the videos. Students can click on the items under TOC to directly jump to the relevant section in a video. Video archives also have a Phrase Cloud of keywords/phrases used in the video. Students can click on a particular keyword/phrase to find yellow markers, indicating the points where the selected keyword/phrase was covered on the video timeline.

Selection Process

How will the candidates be selected?

  • Candidate submit their application forms for the program by sharing details of education, work experience, and a statement of purpose.
  • These applications are reviewed by IISc faculty members corresponding to their respective programs.
  • A selected candidates list issued by the IISc faculty members and the candidates are informed by the TalentSprint team through a selection email.
  • A payment link for paying the program fee is shared along with the selection email.
  • Candidates can enrol into the program by making payment of the program fee.

What is the eligibility criteria to join this programme?

Candidates must have completed their graduation or post graduation with at least 50% marks and minimum of 1 year work experience. In addition, programming knowledge is required.

Programme Fee

How do you define an International Participant?

Participants who are not holding an Indian Passport or are not residing in India will be considered as an International Participant.

Can the programme fee be paid in instalments?

Yes, the programme fee can be paid in interest-free instalments. Please reach out to your RM to know about the details of the schemes available.

What is the programme fee?

The programme fee for PG Level Advanced Certification in Computational Data Science is ₹4,00,000 + GST. For EMI details, visit Fee Page.Campus visit fee will be based on actuals and to be borne by the participants.

Is the application fee refundable?

No, the application fee is non-refundable.

Is the total programme fee refundable?

No, the total programme fee is non-refundable.

How do professionals fund their deep-tech expertise development?

Building deep-tech expertise is an absolute necessity for professionals in this fast-changing world that is constantly disrupted by technology.

Professionals have multiple options for funding.

  1. Sponsored by employer
  2. Self-funded

Based on our experience in enabling 3000 professionals who have participated in our executive programs, self-funding seemed to be the most preferred option with over 80% choosing this.

Three key reasons why professionals opt for it:

  1. Freedom: To opt for a program of their choice, at a time of their choice, with an institution of their choice and not be restrained by organisation’s policy.
  2. Flexibility: To pursue greater career opportunities well even beyond their current employer or to nurture their entrepreneurial ambitions.
  3. Funding: Access to flexible EMI schemes (Paying as low as Rs. 12,710 per lac per month).

We have also observed that the commitment levels were higher among self-funding participants.

Content

What is the detailed curriculum of this programme?

Bridge Module [4 weeks]
  • Google Colab
  • Python
    1. Matplotlib
    2. Numpy
    3. Pandas
  • Basic mathematics
  • Basic data visualization
  • Data Cleaning/Munging
Module 1: Foundations of Data Science: Probability and Statistics [3 weeks]

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 [3 weeks]
  • Introduction to Calculus and Linear Algebra in Data Science
    1. Importance of Calculus and Linear Algebra in Data Science
    2. Basics of Univariate and Multivariate Calculus Vector Operations and Norms
  • Deep Dive into Calculus for Data Science
    1. Derivatives and Partial Derivatives
    2. Composite Functions and the Chain Rule
    3. Introduction to Automatic Differentiation
  • Optimizing with Gradient Descent and Backpropagation
    1. Gradient Descent Fundamentals
    2. The Mechanics of Backpropagation
  • Fundamentals of Linear Algebra
    1. Vector Spaces, Bases, and Dimensions
    2. Linear Transformations and Matrices Matrix Operations in Data Science
  • Principal Component Analysis and Matrix Factorization
    1. Principal Component Analysis (PCA): Theory and Application
    2. Overview of Matrix Factorization Techniques (e.g., SVD, QR decomposition)
Module 3: Machine Learning [5 weeks]
  • Foundations of ML
    1. Problem-solving strategy with data science tools, ML, and DL.
    2. Model selection, feature importance
  • Regression Models
    1. Least Squares; Regularization - Elastic Net, Ridge, Lasso; Bias-Variance tradeoff
    2. Development-testing paradigm
  • Classification Models
    1. Classification algorithms
    2. Evaluation Metrics: MSE, Accuracy, Precision, Recall, F1 Score
  • Decision Trees and Random Forests
    1. Decision Tree Algorithms
    2. Voting Classifiers, Bagging Ensemble models
    3. Random Forests
  • Boosting Models
    1. XGBoost
    2. Light GBM
  • Unsupervised Learning
    1. Clustering (k-means, dbscan), Agglomerative clustering
    2. Anomaly Detection
  • Mini-Project: Five Mini Projects in Machine Learning
Module 4: AI at Scale [4 Weeks]
  • Introduction to Scalable Computing
    1. Overview of scalable computing in AI/ML
    2. Understanding parallel architectures
    3. Importance of scalability in data science
  • Introduction to Parallel Computing
    1. Types of parallelism: Data parallelism vs. Task parallelism
    2. Introduction to Shared Memory (OpenMP) and Distributed Memory (MPI)
  • Deep Dive into Scalable Computing Technologies
    1. Deep dive into OpenMP and MPI
    2. Introduction to GPUs and Nvidia accelerators
    3. Overview of CUDA programming
  • Scalable Data Science Tools
    1. JIT Compiler, Numba, and Dask
    2. Introduction to TensorFlow distributed computing
  • Machine Learning at Scale with Parallel ML Libraries
    1. Introduction to Dask, MLLib, cuDF, cuML, cuPY
    2. Focused exploration of Horovod, Ray, and Rapids
Module 5: Neural Networks [6 weeks]
  • Deep Neural Networks
    1. Multi-Layer Perceptrons (MLP), backprop, Regression/Classification with MLP, gradient issues, and activation functions batch normalization, overfitting, drop out, optimizers and learning rate
  • Convolutional Neural Networks
    1. Filtering, Convolution, pooling, Various architectures: U-net, Resnet, etc., classification, localization, segmentation (Computer Vision Applications)
  • Recurrent Neural Networks
    1. Sequence modeling, memory cell, GRU, LSTM, gradient issues (NLP Applications)
  • Dimensionality Reduction and Self-supervised Neural Networks
    1. PCA, Matrix Completion, LDA, CCA Manifold Learning, t-SNE, LLE
    2. Advanced Auto-encoder type architectures
  • Fundamentals of NLP, attention mechanism, and transformer models.
  • Mini-Project in Neural Networks
Module 6: Generative AI in production [3 weeks]
  • Introduction to Generative AI
    1. Understanding Generative AI - Introduction to models like GPT, DALL-E, Midjourney, Sora (and others) including their capabilities and limitations.
    2. Exploring the Potential of Generative AI using APIs of large models (e.g., OpenAI).
  • Enhancing Applications with Advanced Techniques
    1. Advanced Interaction with Generative Models - Prompt Engineering Techniques and Fine-Tuning
    2. Retrieval-Augmented Generation (RAG)
  • Building and Deploying Business Applications
    1. Identifying business problems and architecting solutions incorporating generative models.
    2. Integrating Generative AI into Applications though API integration
    3. Navigating Ethical Implications - Privacy, bias, and copyright issues.
Module 7: Data Engineering [5 weeks]
  • Introduction to Big Data storage systems
    1. Relational Databases
    2. NoSQL Databases: HBase, Graph DB
    3. Distributed File Systems/HDFS
    4. Cloud storage
  • Introduction to Big Data processing platforms
    1. Data Volume: Hadoop, Spark
    2. Data Velocity: Storm, Complex Event Processing
    3. Cloud platforms
  • Deep dive into Apache Spark
    1. Resilient Distributed Dataset
    2. Transformations, Actions
    3. Designing computational and analytics applications using Spark
    4. Hands-on with PySpark programming
  • Mini-Project in Data Engineering
Module 8: Business Analytics [5 weeks]
  • Time Series Modeling
    1. Fundamentals, Stationarity, Measures of Dependence
    2. ARMA modeling
  • Business Case Studies
    1. Market Basket Analysis (Association Rule Mining)
    2. Optimal Financial Portfolio Allocation
    3. Customer Churn Analysis
  • Mini-Project in Business Analytics
Module 9: Capstone Part 1 [8 weeks]
Selection of capstone topic, proposal preparation; Runs concurrently with Module 7,8
Module 10: Capstone Part 2 [4 weeks]
Work on the capstone topic, final presentation.

Schedule and Details

What will be the timings of the online sessions?

The online sessions will be held during the weekends, Saturdays & Sundays.

How will the programme be delivered?

The programme will be delivered through faculty-led interactive live sessions on TalentSprint’s ipearl.ai

How will I receive the reading material and books?

All reading material (pre/post session) will be shared regularly through the Online Learning Management System.

When will the programme commence?

Applications Closed for Batch 9

What’s the total duration of the programme?

The total duration of the programme is 12 months.

What certificate do we get?

A certificate of successful completion is provided upon completion of all requirements of the program. All examinations and evaluations related to the certification are carried out by CCE-IISc. View sample certificate here

What will be the batch size for this programme?

The batch will consist of 100 participants.

Who will conduct the sessions?

All sessions are conducted by IISc faculty members. Industry experts may be invited for sharing their valuable experience.

About the Platform

What if I miss an online session?

Do not worry. In case you miss a session, you will be given access to view the recorded version of the session within a specified timeframe.

Is there a minimum attendance criteria?

Yes, minimum 75% attendance is mandatory for the programme. To complete the programme you need to complete and submit all program submissions that may be due at your end.

What are the prerequisites to access this programme on the direct to device platform?

  • An internet-connected device, Computer/Laptop/Tablet/Smartphone, is enough to access the platform.
  • Web-camera and Mic with necessary power backup will be needed for proctored online assessments.

What are the Program Terms and Conditions?

All participants are required to execute, before the start of the program, an agreement that consists of the standard Program Terms and Conditions. It has the following components:

  • Part A: Etiquette and Platform Rights
    • Classroom Etiquette & General Policy Guidelines
    • Tools and Platforms in Use
    • Tools and Platforms: Terms of Use
  • Part B: Honor Code
  • Part C: Certification
  • Part D: Program Fee, Refund and Termination Policy

Others

About IISc Bangalore

The PG Level Advanced Certification Programme in Computational Data Science is offered by TalentSprint in collaboration with Centre for Continuing Education (CCE) at IISc. CCE-IISc offers courses suitably designed to meet the requirements of various target groups - aspiring research scientists, graduate engineers, and young professionals, enabling them to grow into competent managers of technology-intensive organizations. For more information, visit http://cce.iisc.ac.in.

About TalentSprint