Post Graduate Program in Data Science and AI

Dive into core data science techniques — explore K-means, hierarchical clustering, and unsupervised segmentation to identify patterns in raw data. Learn how to apply linear and logistic regression to real-world scenarios, diagnose model fit, and interpret key metrics like odds ratios and ROC curves. You'll also get hands-on with recommendation systems using user-item matrices.

• Module 4: Clustering K-means, hierarchical clustering, unsupervised segmentation

• Module 5: Linear Regression Part 1 Introduction to linear regression, line fitting, assumptions

• Module 6: Linear Regression Part 2 Multiple regression, multicollinearity, diagnostics

• Module 7: Logistic Regression Binary classification, odds ratios, confusion matrix, ROC

• Module 8: Collaborative Filtering Recommendation systems using user-item matrices

Live Learning sessions: Clustering hands-on and visualization, regression walkthrough, multivariable model building, classification metrics, ROC/AUC, tree-based models, user-item matrix, cold start and sparsity challenges

Apply advanced ML and optimization techniques — explore linear programming, real-world business applications, and mixed-integer and non-linear models. Learn how ad auctions and bidding strategies work, simulate large-scale supply chain optimizations, and compare regression with classification models. You’ll also work with ensemble methods like Random Forests and XGBoost, and understand fairness, bias, and ethical concerns in AI systems.

• Module 9: Linear Optimization Models Linear programming, constraints, objective function

• Module 10: Linear Optimization Models, Case Study Real-world application of LP in business/operations

• Module 11: Integer, Nonlinear, and Discrete Optimization Models Mixed integer programming, non-linear optimization

• Module 12: Sponsored, Internet Search Advertising, Case Study Ad auctions, bidding strategies, click-through modeling

• Module 13: Large-Scale Optimization, UN World Food Programme Supply chain logistics, large-scale model constraints

• Module 14: Regression and Classification Comparison of regression and classification, hybrid models

• Module 15: Ensemble Models Random forests, boosting (Ada, XGBoost)

• Module 16: Fairness and Bias Issues in Data-Driven Predictions Ethical AI, bias detection, mitigation strategies

Live Learning sessions: LP formulation and solving, Excel/Python demo, case-based applications, mixed-integer modeling, ad bidding simulation, supply chain optimization walkthrough, delivery trade-offs

Dive into deep learning and NLPs — understand how neural networks work, from perceptrons to CNNs and transfer learning. Explore model training, backpropagation, and tuning techniques. Learn text preprocessing, tokenization, and vectorization, then move into language modeling, sentiment analysis, and NLP classification techniques.

• Module 17: Neural Networks Part 1 Perceptrons, architecture of neural networks

• Module 18: Neural Networks Part 2 Backpropagation, hyperparameters, tuning

• Module 19: Neural Networks Part 3 CNNs, advanced architectures, transfer learning

• Module 20: Natural Language Processing (NLP) Part 1 Text processing, tokenization, vectorization

• Module 21: Natural Language Processing (NLP) Part 2 Language modeling, classification, sentiment analysis

Live Learning sessions: Build neural nets in TensorFlow/Keras, tune hyperparameters, visualize learning, CNN hands-on, sequence prediction with RNNs, LSTM/GRU basics

Bridge insight and impact — explore model explainability with SHAP and causality theory to drive trustworthy decisions. Learn how analytics and optimization integrate into business strategy and understand what it takes to lead AI-driven transformation with effective change management and leadership frameworks.

• Module 22: Interpretability and Causality in Models Model explainability, SHAP, causality theory

• Module 23: Data, Models, and Decisions Integration of analytics, optimization, decision science

• Module 24: Leading Digital Transformations Change management, AI strategy, transformation leadership

Live Learning sessions: Explainability walkthrough, SHAP demo, business decisions from models, transformation case studies, leadership in AI adoption, change management frameworks

Assignments will be graded by industry practitioners who support participants in their learning journeys and/or by the Emeritus grading team

This program is designed with some of the best faculty to cover relevant topics in a manner that creates positive career outcomes. Additionally, we provide a 6-month IIMJobs Pro Membership with access to job insights, recruiter actions, profile boosts, and an AI powered resume builder. Career prep modules on resumes, LinkedIn profile optimization, job navigation and interview preparation are also provided

Upon successful completion of the program, you will receive a smart digital certificate. This can be shared with friends, family, schools, or potential employers. You can use it in your cover letter and resume and/or display it on your LinkedIn profile.

You will have access to the online learning platform and all the videos and program materials for 12 months following the program end date. Access to the learning platform is restricted to registered participants per the terms of the agreement.

Yes, the qualifying mark is 75% and minimum 50% attendance in live sessions are recommended

Refund Policy

• Policy Communication:

  Emeritus’ Withdrawal, Refund and Deferral policies are communicated to all learners (both existing and prospective) via the Emeritus website and Learning Management System. It is the learner’s responsibility to review, be aware of and adhere to these policies.

• Withdrawal for Non-delivery of Course:

  Emeritus will notify learners in writing if (a) the course will not commence on the scheduled course commencement date; (b) the course will not be completed by the scheduled course completion date; or (c) the learner does not meet the course entry or matriculation requirement as set by Emeritus or the university. Within three (3) working days of such notice, Emeritus will inform learners in writing of any available alternative study arrangements.

If a learner declines the offered alternative study arrangements, if any, or desires to otherwise withdraw from the course for the reasons stated in paragraph 2 above, the learner shall request a withdrawal from Emeritus within fifteen (15) calendar days. Upon receipt of the withdrawal request and validation of eligibility, Emeritus shall refund the learner 100% of course and miscellaneous fees previously paid by the learner, except that course application fees are non-refundable and non-transferable. Emeritus shall use commercially reasonable efforts to make such refund within seven (7) working days from receipt of the withdrawal request from learner.

• Withdrawal for Other Reasons:

  If the learner wishes to withdraw from the course or program for any reason other than those stated in paragraph 2 above, the following provisions shall control all withdrawal requests:

Absent a previous approved request for deferral for the course, learners may request a full refund of all course and miscellaneous fees paid, within fourteen (14) days after course commencement. Application fees for courses are non-refundable and non-transferable. Learners who have previously been granted a course deferral are not eligible for a refund for the course. Partial (or pro-rated) refunds are not offered. Emeritus shall use commercially reasonable efforts to make a valid refund within seven (7) working days from receipt of the withdrawal request from learner.

Emeritus reserves the right, in its sole discretion, to dismiss a learner from a course or program at any time and to provide a refund to the learner pursuant to the stated refund policy in paragraph 2 above. Learners who are dismissed from a course or program due to a violation of Emeritus’ Code of Conduct are not entitled to any refund.

• How to Submit a Valid Withdrawal Request:

  All withdrawal requests must be sent in writing within the timelines specified in paragraphs 2 or 3 above to:

  admissions@emeritus.org

All refunds will be paid directly to the original payer only, unless written and signed instruction is provided by the original payer to pay the refund to an account belonging to a person other than the original payer.

• Bank Charges/Transaction Fees:

  In the event of an approved refund, Emeritus will refund the course fee collected and will not be liable to refund any foreign transaction fees, processing charges, or any other bank fees.