DS
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.
Module-wise syllabus
Tap a module to see every topic. The same syllabus is used in our classroom and live online batches.
01Data Science & AI Foundation (10 hrs)4 topics
- 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)5 topics
- 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)5 topics
- 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)3 topics
- Core Python, OOP, modules & virtual environments
- APIs, JSON, regex, web data collection
- Jupyter, Google Colab, VS Code
05Python for Data Analysis (30 hrs)4 topics
- NumPy vectorisation & broadcasting
- Pandas GroupBy, merge, pivot, apply
- Matplotlib, Seaborn, Plotly
- Project: customer sales & marketing analysis
06Statistics & Probability (30 hrs)4 topics
- 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)3 topics
- Power BI modelling, DAX, RLS, storytelling
- Tableau calculated fields, parameters & stories
- Project: management BI dashboard
08Exploratory Data Analysis (20 hrs)4 topics
- Missing values, outliers, duplicates
- Univariate, bivariate & multivariate analysis
- Feature creation & data leakage
- Project: loan applicant analysis
09Machine Learning (55 hrs)4 topics
- 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)4 topics
- 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)3 topics
- Trend, seasonality, stationarity
- Moving average, exponential smoothing, ARIMA, SARIMA, Prophet
- Projects: sales & inventory demand forecasting
12NLP (20 hrs)3 topics
- Tokenisation, stemming, lemmatisation, TF-IDF
- Word embeddings, transformers, Hugging Face, BERT
- Project: customer review sentiment analysis
13Deep Learning (30 hrs)4 topics
- 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)3 topics
- LLM concepts, tokens, context window, embeddings
- OpenAI API & Hugging Face
- Prompt engineering: zero / few-shot, chain-of-thought, structured output
15RAG Applications (20 hrs)4 topics
- Chunking, embeddings, vector databases
- Retrieval, context injection, evaluation
- LangChain, FAISS / Chroma
- Project: institute knowledge assistant
16Agentic AI (20 hrs)3 topics
- Agents, tools & function calling
- Memory, planning, multi-agent systems, human-in-the-loop
- AI sales, resume-screening & MIS reporting agents
17Deployment & MLOps (25 hrs)3 topics
- Streamlit, Flask / FastAPI, Gradio
- Docker basics
- Model versioning, experiment tracking, MLflow, monitoring
18Cloud & Big Data Basics (15 hrs)2 topics
- AWS / Azure / GCP concepts
- Spark, PySpark, ETL & data pipelines
19Git / GitHub + Portfolio (10 hrs)3 topics
- Git, branches, commits, pull requests
- README & project documentation
- A GitHub portfolio of 10–15 projects
20Capstone Projects (40 hrs)3 topics
- 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)3 topics
- 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
Programmes that include Data Science & AI
| Programme | Duration | Fees |
|---|---|---|
| Professional Data Science & AI (GenAI, ML & Agentic AI) | 10–12 months | ₹75,000 + GST |
Frequently asked questions
Can I download the Data Science & AI syllabus as a PDF?
Yes. Use the “Download syllabus PDF” button on this page — enter your name and mobile number and the PDF opens instantly.
How long does the Data Science & AI course take?
It is taught inside our programmes: Professional Data Science & AI (GenAI, ML & Agentic AI) (10–12 months, ₹75,000 + GST). Module-wise batches are also possible — ask the counsellor.
Is it online or classroom?
Both. Classroom batches run at our Dwarka, Uttam Nagar and Janakpuri branches, and live online batches are available.