CEP, IIT Delhi Advanced Data Analytics in Management

• Understanding Data and Decision Making

• Data Science vs. Data Analytics vs. Business Analytics

• Descriptive, Predictive and Prescriptive Analytics

• Data Measurement and Data Cleaning

• Descriptive Analytics

• Data Visualisation

• Data Mining

• Exploratory Data Analysis (EDA)

• Predictive Analytics Overview

• Supervised vs. Unsupervised Machine Learning

• ANOVA Models

• Inferential Data Analysis & Hypothesis Testing

• Regression vs. Classification vs. Clustering

• Regression Analysis & Logistic Regression

• K-Means Clustering & Hierarchical Clustering

• Principal Component Analysis (PCA)

• Market Basket Analysis

• Prescriptive Analytics

• Optimisation Models

• Monte Carlo Simulation

• Multi-Criteria Decision Making (MCDM)

• Introduction to Artificial Intelligence in Business

• Machine Learning Lifecycle in Organisations

• Generative AI (ChatGPT, LLMs and Business Applications)

• Prompt Engineering for Analytics Tasks

• AI-Powered Analytics Automation (Insight Generation, Reporting)

• AI Agents and Workflow Automation in Business

- Marketing Analytics: Customer Segmentation, Campaign Performance, CLV, Personalisation & Recommendation Systems

- Financial Analytics: Risk-Return Analysis, Forecasting, Fraud Detection and Credit Analytics

- Operations Analytics: Demand Forecasting, Inventory Optimisation, Process Analytics, Supply Chain Analytics with AI

- HR Analytics: Attrition Prediction, Workforce Planning, Performance Analytics, AI in Hiring & Talent Analytics

• Big Data Concepts (Volume, Velocity, Variety)

• Hadoop and Spark Ecosystem

• Data Lakes vs. Data Warehouses

• Cloud Platforms Overview (AWS/GCP/Azure)

• Modern Data Stack (Snowflake, BigQuery)

• Real-Time Analytics and Streaming Basics

• Industry-based dataset and problem statement

- Domain-specific tracks: Marketing/Finance/Operations/HR

- End-to-end flow: Problem Identification > Data Preparation > Modelling > Insights > Recommendations

- Domain-specific tracks: Marketing/Finance/Operations/HR

Programming & Data Manipulation

• Python 3.x

• Jupyter

• Google Colab

• NumPy

• Pandas

• Scikit-learn

Machine Learning

• LightGBM

• XGBoost

• Scikit-learn

Dev & Collaboration

• VS Code

• GitHub

• PuLP

• Kaggle Notebooks

• OR-Tools

AI & GenAI

• OpenAI API

• ChatGPT

• Prompt Engineering

• Hugging Face

Prescriptive/Optimisation

• Prescriptive/Optimisation

• OR-Tools (Google, free)

Data Visualisation

• Power BI

• Tableau Public

• Matplotlib

• Seaborn

• Plotly

Cloud & Big Data

• BigQuery

• Snowflake

• Databricks

• AWS Free Tier

• GCP

• Apache Spark

The capstone is not a test — it is a business engagement. You will take a domain-specific, industry-grade problem and solve it end-to-end, guided by faculty, peer-reviewed, and presented as a professional deliverable.

• Problem Scoping: Define problem, data brief, domain track with mentor

• Data Collection & Cleaning: Sourcing, missing values, normalisation

• Exploratory Analysis: Visualisation, distributions, correlation mapping

• Model Building: 2–3 techniques, train/test split, evaluation metrics

• Insight Synthesis: Business recommendations, scenario analysis

• Report & Presentation: 5–8 page report + 10-slide deck

Domain tracks: Marketing Analytics · Financial Analytics · Operations Analytics · HR Analytics

Disclaimer: The list of tools and topics mentioned is indicative and may be modified as per programme requirements and at the discretion of the Programme Coordinator.