Professional Data Science & AI — GenAI, ML & Agentic AI — Syllabus 2026

A 21-module path from Excel and SQL through Python, statistics, machine learning, deep learning, Generative AI, RAG and Agentic AI to deployment — with 12–15 projects and 3 major capstones.

21 modules74 topicsBeginner → Job-ready · 10–12 months · 300–350 hrs

01Data Science & AI Foundation (10 hrs)

Data analyst vs data scientist vs ML engineer
AI vs ML vs deep learning vs GenAI
Data science lifecycle & CRISP-DM
Turning a business problem into a data science problem

02Advanced Excel + AI for Analytics (20 hrs)

XLOOKUP, INDEX-MATCH, SUMIFS, dynamic arrays
Power Query, Pivot Tables, Power Pivot, Solver
Regression & forecasting in Excel
Copilot / GenAI workflows; generating VBA with AI
Project: sales & profitability dashboard

03SQL + Database + AI (35 hrs)

Joins, CASE, subqueries, views
CTEs, recursive CTEs, window functions, LEAD / LAG
Running totals, moving averages, cohort analysis
AI-generated SQL & natural language → SQL
Project: e-commerce customer & sales analytics

04Python Programming (35 hrs)

Core Python, OOP, modules & virtual environments
APIs, JSON, regex, web data collection
Jupyter, Google Colab, VS Code

05Python for Data Analysis (30 hrs)

NumPy vectorisation & broadcasting
Pandas GroupBy, merge, pivot, apply
Matplotlib, Seaborn, Plotly
Project: customer sales & marketing analysis

06Statistics & Probability (30 hrs)

Descriptive statistics, IQR, z-score, correlation
Probability, Bayes theorem & distributions
Sampling, confidence intervals, hypothesis tests (t, z, chi-square, ANOVA)
A/B testing & campaign analysis

07Data Visualisation — Power BI + Tableau (20 hrs)

Power BI modelling, DAX, RLS, storytelling
Tableau calculated fields, parameters & stories
Project: management BI dashboard

08Exploratory Data Analysis (20 hrs)

Missing values, outliers, duplicates
Univariate, bivariate & multivariate analysis
Feature creation & data leakage
Project: loan applicant analysis

09Machine Learning (55 hrs)

Supervised vs unsupervised; train / test, cross-validation, bias / variance
Linear, ridge, lasso & elastic-net regression
Logistic regression, KNN, Naive Bayes, decision tree, random forest, SVM
Bagging & boosting: XGBoost, LightGBM, CatBoost

10Advanced ML & Model Evaluation (25 hrs)

K-Means, hierarchical clustering, DBSCAN, PCA, anomaly detection
MAE, RMSE, R², MAPE; precision, recall, F1, ROC-AUC
Hyperparameter tuning, feature selection, explainability
Project: customer segmentation

11Time Series & Forecasting (15 hrs)

Trend, seasonality, stationarity
Moving average, exponential smoothing, ARIMA, SARIMA, Prophet
Projects: sales & inventory demand forecasting

12NLP (20 hrs)

Tokenisation, stemming, lemmatisation, TF-IDF
Word embeddings, transformers, Hugging Face, BERT
Project: customer review sentiment analysis

13Deep Learning (30 hrs)

Neural networks, backpropagation, optimisers
TensorFlow, Keras, PyTorch
CNNs, transfer learning, RNN, LSTM, GRU
Project: image classification or churn neural network

14Generative AI & LLMs (30 hrs)

LLM concepts, tokens, context window, embeddings
OpenAI API & Hugging Face
Prompt engineering: zero / few-shot, chain-of-thought, structured output

15RAG Applications (20 hrs)

Chunking, embeddings, vector databases
Retrieval, context injection, evaluation
LangChain, FAISS / Chroma
Project: institute knowledge assistant

16Agentic AI (20 hrs)

Agents, tools & function calling
Memory, planning, multi-agent systems, human-in-the-loop
AI sales, resume-screening & MIS reporting agents

17Deployment & MLOps (25 hrs)

Streamlit, Flask / FastAPI, Gradio
Docker basics
Model versioning, experiment tracking, MLflow, monitoring

18Cloud & Big Data Basics (15 hrs)

AWS / Azure / GCP concepts
Spark, PySpark, ETL & data pipelines

19Git / GitHub + Portfolio (10 hrs)

Git, branches, commits, pull requests
README & project documentation
A GitHub portfolio of 10–15 projects

20Capstone Projects (40 hrs)

Business data science: SQL + Python + statistics + ML + Power BI
AI / ML: churn prediction platform with explainability & Streamlit
GenAI: enterprise RAG + AI agent web application

21Interview & Placement Preparation (20 hrs)

SQL, Python, statistics, ML & Power BI question banks
Case studies & mock interviews (technical, HR, managerial)
ATS resume, LinkedIn, GitHub & Kaggle profile

Projects & practice

Sales forecastingCustomer churn predictionCustomer segmentationSentiment analysisRAG chatbotAI MIS report generator

Programme, duration & fees

Programme	Duration	Fees
Professional Data Science & AI (GenAI, ML & Agentic AI)	10–12 months	₹75,000 + GST

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