Curriculum
OUTLINE

Course Outline for Beginners - 14 months
Module name Module type Duration (months)
Advanced SQL & AI for Data Professionals Core 2
Excel & Dashboarding with AI Storytelling Core 1
Python Foundations + AI Coding Assistants Core 1
Data to Decisions: Product Analytics with AI Core 1
Generative AI for Data Analytics & Automation Core 1
Interview Readiness Core 1
Advanced Analytics with Python, Pandas & AI Workflows Core 1
Statistics, Experimentation & AI for Data-Driven Decisions Core 1
Advanced Product Analytics Core 1
Domain analytics Elective 1
Cloud & Data Analytics on AWS Elective 1
AI For everyone Core 2
Mathematical Foundations for AI Core 1
Machine Learning with AI Workflows Core 1
Supervised Machine Learning with AI Elective 1
Un-supervised Machine Learning with AI Elective 1
Time Series and Recommender Systems with AI Workflows Elective 1
MLOps & AI Deployment Elective 1
Deep Learning & Neural Networks Elective 1
AI for Computer Vision Elective 1
AI for Natural Language Processing Elective 1
AI engineering and Agentic AI Elective 1.5
Course Outline for Intermediate - 11.5 months
Module name Module type Duration (months)
Advanced Analytics with Python, Pandas & AI Workflows Core 1
Statistics, Experimentation & AI for Data-Driven Decisions Core 1
Advanced Product Analytics Core 1
Mathematical Foundations for AI Core 1
Machine Learning with AI Workflows Core 1
Supervised Machine Learning with AI Core 1
Un-supervised Machine Learning with AI Core 1
Time Series and Recommender Systems with AI Workflows Core 1
MLOps & AI Deployment Core 1
Deep Learning & Neural Networks Core 1
AI engineering and Agentic AI Core 1.5

A Deep Dive into
the Curriculum

Note: Listed below is the detailed insight into the Data Science and Machine Learning curriculum

Beginner Module - The Basics (21.5 months)

Topics Covered:
01 Advanced SQL & AI for Data Professionals
  • Data Foundations & SQL Introduction
  • Schema & SQL Basics
  • Data Filtering with SQL
  • NULLs & Aggregation
  • GROUP BY Metrics
  • Applied SQL Lab 1: GroupBy Problems
  • CASE WHEN and Conditional Aggregation
  • JOINs 1: INNER JOIN
  • JOINs 2: LEFT + FULL OUTER + CROSS
  • JOINs 3: SELF JOIN + NON-EQUI JOINS
  • Subqueries 1: Non-Correlated
  • Subqueries 2: Correlated + EXISTS
  • CTEs + UNION / UNION ALL
  • Applied SQL Lab 2: JOIN-heavy Problems
  • Window Functions 1: ROW_NUMBER & Aggregates
  • Window Functions 2: Advanced Ranking & Frames
  • Window Functions 3: Navigation & Trend Analysis
  • Date & Time SQL 1: Core
  • Applied SQL Lab 3: Windows + Dates
  • MySQL Setup & DDL, DML Commands
  • Advanced Concepts 1: Views, Indexing & Partitioning
  • Advanced Concepts 2: Functions & Stored Procedures
  • Funnel Analytics in SQL
  • Cohorts & Retention Analysis
  • Applied SQL Lab 4: Final Revision
02 Excel & Dashboarding with AI Storytelling
  • Excel 1: Cleanup + Structure
  • Excel 2: Core Formulas
  • Excel 3: Lookups + Reporting
  • Excel 4: Pivot Tables + Mini Dashboard
  • Live Business Case: Excel Reporting Case
  • Tableau 1: Viz Fundamentals + KPIs
  • Tableau 2: Filters + Calculations
  • Tableau 3: Dashboard Lab 1
  • Tableau 4: LOD (Practical)
  • Tableau 5: Table Calcs + Analytics
  • Tableau 6: Dashboard Creation
  • Live Business Case 4: Dashboard Storytelling
03 Python Foundations + AI Coding Assistants
  • Python 1: Data Types + Variables + I/O
  • Python 2: Control Flow + Loops
  • Python 3: Functions + Lists + Strings
  • Python 4: Data Structures
  • Python 5
  • Python 6
  • Python Problem Solving & Debugging Masterclass
  • Pandas 1: Read + Filter + Select
  • Pandas 2: Groupby KPIs
  • Pandas 3: Merge + Cleaning
  • Pandas 4: EDA & Data Quality Checks
  • Pandas 5: Complete EDA
04 Data to Decisions: Product Analytics with AI
  • Product Analytics 1: Metrics Foundations (DAU, MAU, Retention, Funnels)
  • Product Analytics 2: Event Tracking & Data Instrumentation Basics
  • Product Analytics 3: User Segmentation & Behavioral Analysis
  • Product Analytics 4: Funnel Analysis (Conversion & Drop-offs)
  • Product Analytics 5: Cohort Analysis & Retention Deep Dive
  • Product Analytics 6: Growth Metrics (Activation, Engagement, Retention)
  • Product Analytics 7: A/B Testing Fundamentals for Product Decisions
  • Product Analytics 8: Experiment Design & Common Pitfalls
  • Product Analytics 9: Revenue Metrics (AOV, LTV, CAC Basics)
  • Product Analytics 10: Feature Adoption & Product Usage Analysis
  • Product Analytics 11: AI for Product Analytics (Insight Generation & Hypothesis)
  • Applied Product Lab: End-to-End Growth Case + AI-Assisted Insights
05 Generative AI for Data Analytics & Automation
  • GenAI 1: Introduction to AI, LLMs & Analyst Workflows
  • GenAI 2: Prompt Engineering for Data Tasks (SQL, Python, Insights)
  • GenAI 3: Using AI for SQL Query Generation & Debugging
  • GenAI 4: Using AI for Python & Pandas Automation
  • GenAI 5: AI for Data Cleaning & EDA
  • GenAI 6: AI for Dashboard Insights & Storytelling
  • GenAI 7: Building AI-Assisted Reports & Business Narratives
  • GenAI 8: Automating Repetitive Analysis using AI
  • GenAI 9: Evaluating AI Outputs (Hallucination, Bias, Errors)
  • GenAI 10: Using AI APIs (OpenAI) for Data Workflows
  • GenAI 11: Building Mini AI Tools for Analysts (End-to-End)
  • Applied GenAI Lab: Solve a Business Case using AI + Analytics
06 Interview Readiness
  • Interview Prep 1: SQL Basics & Pattern Recognition
  • Interview Prep 2: SQL Intermediate (JOINs, Aggregations)
  • Interview Prep 3: SQL Advanced (Window Functions, CTEs)
  • Interview Prep 4: SQL Case-Based Problem Solving
  • Interview Prep 5: Python Basics for Interviews
  • Interview Prep 6: Pandas-Based Interview Questions
  • Interview Prep 7: Product Case Interviews (Metrics & Business Thinking)
  • Interview Prep 8: Estimation & Guesstimates for Analysts
  • Interview Prep 9: Resume Building (Projects, Impact, Storytelling)
  • Interview Prep 10: LinkedIn & Portfolio Optimization
07 Advanced Analytics with Python, Pandas & AI Workflows
  • Python Refresher and Data Foundations
  • Data Manipulation & Chaining
  • Groupby Patterns & Transformations
  • Merge, Join & Multi-Table Data Handling
  • Handling Large Datasets
  • Data Cleaning at Scale (Edge Cases)
  • NumPy Foundations for Analytics
  • Advanced Numpy for Analytics
  • Data Visualization & Feature Engineering
  • Automation for Analysts (Scripts & Scheduling)
  • AI Workflows 1: Using AI for Analysis & Code Generation
  • AI Workflows 2: Building End-to-End AI-Assisted Data Solutions
08 Statistics, Experimentation & AI for Data-Driven Decisions
  • Statistics 1: Probability Foundations for Analytics
  • Statistics 2: Distributions (Normal, Binomial, Real-world Context)
  • Statistics 3: Sampling Techniques & Bias
  • Statistics 4: Central Limit Theorem (Intuition + Applications)
  • Statistics 5: Hypothesis Testing Fundamentals
  • Statistics 6: p-values, Confidence Intervals & Errors
  • Statistics 7: A/B Testing (Design & Setup)
  • Statistics 8: A/B Testing (Analysis & Interpretation)
  • Statistics 9: Experimentation Pitfalls (Bias, Peeking, Simpson’s Paradox)
  • Statistics 10: Metric Design for Experiments
  • Statistics 11: AI-Assisted Statistical Interpretation & Validation
  • Applied Experiment Lab: End-to-End A/B Test Case
09 Advanced Product Analytics
  • 1: Advanced Funnel Analysis (Multi-step, Multi-channel)
  • 2: Retention Models & Cohort Deep Dive
  • 3: Customer Lifetime Value (LTV) Modeling
  • 4: Customer Acquisition Cost (CAC) & Payback
  • 5: Growth Loops & Product-Led Growth
  • 6: Segmentation Strategies (Behavioral & Value-based)
  • 7: Churn Analysis & Retention Strategies
  • 8: Predictive Analytics (Intro for Product)
  • 9: Recommendation Systems (Conceptual)
  • 10: Decision Frameworks for Product Teams
  • 11: AI for Product Strategy & Insights
  • Applied Product Capstone: End-to-End Product Analytics Case
10 Domain analytics
11 Cloud & Data Analytics on AWS
  • Cloud 1: Introduction to Cloud & Modern Data Stack
  • Cloud 2: AWS Fundamentals (S3, IAM Basics)
  • Cloud 3: Data Storage with S3 (Data Lakes Concept)
  • Cloud 4: Querying Data with Amazon Athena
  • Cloud 5: Data Warehousing with Amazon Redshift
  • Cloud 6: ETL Basics with AWS Glue
  • Cloud 7: Data Pipelines (End-to-End Flow)
  • Cloud 8: Connecting Tableau to Cloud Data Sources
  • Cloud 9: Performance & Cost Optimization Basics
  • Cloud 10: Intro to Databricks & Lakehouse Concept
  • Cloud 11: Real-world Architecture for Data Analytics
  • Applied Cloud Lab: Build End-to-End Data Pipeline on AWS
12 AI For everyone
  • Evaluating AI Tools: A Comparative Framework
  • Model Selection: Matching AI to Your Needs
  • AI Failure Modes & How to Mitigate Them
  • Voice-Driven Analytical Workflows
  • No-Code Process Automation for Data
  • AI Integration with Org Data & Knowledge Bases
  • Building a Reusable Prompt Library
  • Breaking Projects into AI-Executable Tasks
  • AI Collaboration: Delegation vs. Augmentation
  • Workflow Mapping: The Three-Tier Framework
  • No-Code Prototyping: From Reports to Tools
  • Applied No-Code: Building Data Tools
  • Structured Prototyping: Avoiding Pitfalls
  • Visual Design for AI-Built Prototypes
  • Prototype Handoff & Capstone Planning
  • Autonomous AI: Architecture & Boundaries
  • Orchestrating Multi-Step AI Pipelines
  • Applied Agent Dev: Data-Monitoring Workflow
  • From Analysis to Narrative
  • AI-Assisted Presentations & Visuals
  • Communicating AI-Assisted Work Responsibly
13 Mathematical Foundations for AI
  • Linear Algebra 1 - The ML context
  • Linear Algebra 2 - Dot products and hyperplanes
  • Linear Algebra 3 - Halfspaces and distance
  • Linear Algebra 4 - Loss minimization in classification
  • Linear Algebra 5 - Problem solving
  • Optimization 1 - The need for calculus in ML
  • Optimization 2 - Towards gradient descent
  • Optimization 3 - Gradient descent in action
  • Optimization 4 - Constrained optimization
  • Optimization 5 - Principal component analysis
14 Machine Learning with AI Workflows
  • ML: Intro to Machine Learning
  • ML: Linear Regression-1
  • ML: Linear Regression-2
  • ML: Linear Regression-3
  • ML: Linear Regression-4
  • ML: Polynomial Regression, Bias-Variance
  • ML: Regularisation, Cross Validation
  • ML: Logistic Regression-1
  • ML: Logistic Regression-2
  • ML: Classification Metrics (CM)
  • ML: CM contd. + Imbalanced Data
15 Supervised Machine Learning with AI
  • ML: k Nearest Neighbors-1
  • ML: k Nearest Neighbors-2
  • ML: Decision Trees-1
  • ML: Decision Trees-2
  • ML: Bagging and Random Forest
  • ML: Boosting-1
  • ML: Boosting-2
  • ML: Other Ensemble Techniques
  • ML: Naive Bayes 1
  • ML: Naive Bayes 2
  • ML: SVM-1
  • ML: SVM-2
  • ML: Supervised ML Wrap-up (Optional)
16 Un-supervised Machine Learning with AI
  • Dimensionality Reduction using PCA
  • Dimensionality Reduction using tSNE
  • Dimensionality Reduction using tSNE contd + UMAP
  • Clustering using KMeans
  • Clustering using KMeans++
  • Clustering using GMM
  • Clustering using Hierarchical Clustering
  • Clustering with DBSCAN
  • Anomaly Detection
  • ML: End to End Business Case - 1
  • ML: End to End Business Case - 2
17 Time Series and Recommender Systems with AI Workflows
  • ML: Intro to Time Series and Forecasting
  • ML: Time Series Analysis - 1
  • ML: Time Series Analysis - 2
  • ML: Time Series Analysis - 3
  • ML: Time Series Analysis - 4
  • ML: Time Series Analysis - 5
  • ML: Recommender Systems-1
  • ML: Recommender Systems-2
  • ML: Recommender Systems-3
  • ML: Recommender Systems-4
  • ML: Recommender Systems-5
  • ML: End-to-end Practical Session
18 MLOps & AI Deployment
  • Git & GitHub: Setup for MLOps
  • Building Cars24 ML tool using Streamlit
  • Develop Web APIs using Flask
  • Containerization - Docker & DockerHub
  • Deploying APIs on AWS using ECS
  • GitHub Actions - Setting up CI pipelines
  • GitHub Actions - Setting up CD pipelines
  • Experiment Tracking & Data Management using MLFlow
  • ML System Design - 1
  • ML System Design - 2
  • Building ML pipelines with AWS Sagemaker
  • Processing large scale data using Apache Spark
19 Deep Learning & Neural Networks
  • NN: Intro to Neural Networks
  • NN: Forward and Back Propagation
  • N-Layer Neural Network - 1
  • N-Layer Neural Network - 2
  • N-Layer BackPropagation
  • Tensorflow and Keras - 1
  • Tensorflow and Keras - 2
  • Optimizers for NNs
  • Hyper Parameter Tuning for NNs
  • Autoencoders
  • Practical aspects of designing MLPs and debugging
  • Model interpretability: LIME
20 AI for Computer Vision
  • Introduction to Computer Vision (CNN)
  • Revisiting CNN: Deal with Overfitting
  • CNN under the hood
  • Introduction to Transfer Learning
  • Image similarity: Understanding Embeddings
  • CNN for medical diagnosis
  • Object Localisation and Detection - 1
  • Object Localisation and Detection - 2
  • Object Segmentation
  • Siamese networks
  • Generative Models & GANs Introduction
21 AI for Natural Language Processing
  • NLP: Intro to NLP
  • NLP: Text Representation
  • NLP: Word Embedding - Word2Vec
  • NLP: Language Modeling
  • NLP: Topic Modeling
  • NLP: RNNs
  • NLP: LSTM
  • NLP: NER
  • NLP: Attention
  • NLP: Transformers
  • NLP: BERT
22 AI engineering and Agentic AI
  • Introduction to AI Engineering
  • Refresher: Modern Text & Image AI Model Architectures
  • Getting started with Text Generation LLM APIs (OpenAI API)
  • Designing Eval Pipelines for AI Apps and Observability
  • Prompt Engineering: Introduction
  • Retrieval-Augmented Generation (RAG) Introduction
  • Embeddings Deep Dive
  • Multimodal & Tabular RAG
  • Prompt Engineering: Security
  • Agents: Foundations & Planning
  • Advanced Agent Concepts
  • Agent: Frameworks and Protocols
  • Fine Tuning Existing Models
  • Audio, Image, Video, Expressions: AI Modalities
  • Scaling AI Applications
  • Model Quantization Techniques
  • Advanced Fine Tuning Techniques
  • Dataset Engineering & Inference Optimization

Intermediate Module - The Basics (11.5 months)

Topics Covered:
01 Advanced Analytics with Python, Pandas & AI Workflows
  • Python Refresher and Data Foundations
  • Data Manipulation & Chaining
  • Groupby Patterns & Transformations
  • Merge, Join & Multi-Table Data Handling
  • Handling Large Datasets
  • Data Cleaning at Scale (Edge Cases)
  • NumPy Foundations for Analytics
  • Advanced Numpy for Analytics
  • Data Visualization & Feature Engineering
  • Automation for Analysts (Scripts & Scheduling)
  • AI Workflows 1: Using AI for Analysis & Code Generation
  • AI Workflows 2: Building End-to-End AI-Assisted Data Solutions
02 Statistics, Experimentation & AI for Data-Driven Decisions
  • Statistics 1: Probability Foundations for Analytics
  • Statistics 2: Distributions (Normal, Binomial, Real-world Context)
  • Statistics 3: Sampling Techniques & Bias
  • Statistics 4: Central Limit Theorem (Intuition + Applications)
  • Statistics 5: Hypothesis Testing Fundamentals
  • Statistics 6: p-values, Confidence Intervals & Errors
  • Statistics 7: A/B Testing (Design & Setup)
  • Statistics 8: A/B Testing (Analysis & Interpretation)
  • Statistics 9: Experimentation Pitfalls (Bias, Peeking, Simpson’s Paradox)
  • Statistics 10: Metric Design for Experiments
  • Statistics 11: AI-Assisted Statistical Interpretation & Validation
  • Applied Experiment Lab: End-to-End A/B Test Case
03 Advanced Product Analytics
  • 1: Advanced Funnel Analysis (Multi-step, Multi-channel)
  • 2: Retention Models & Cohort Deep Dive
  • 3: Customer Lifetime Value (LTV) Modeling
  • 4: Customer Acquisition Cost (CAC) & Payback
  • 5: Growth Loops & Product-Led Growth
  • 6: Segmentation Strategies (Behavioral & Value-based)
  • 7: Churn Analysis & Retention Strategies
  • 8: Predictive Analytics (Intro for Product)
  • 9: Recommendation Systems (Conceptual)
  • 10: Decision Frameworks for Product Teams
  • 11: AI for Product Strategy & Insights
  • Applied Product Capstone: End-to-End Product Analytics Case
04 Mathematical Foundations for AI
  • Linear Algebra 1 - The ML context
  • Linear Algebra 2 - Dot products and hyperplanes
  • Linear Algebra 3 - Halfspaces and distance
  • Linear Algebra 4 - Loss minimization in classification
  • Linear Algebra 5 - Problem solving
  • Optimization 1 - The need for calculus in ML
  • Optimization 2 - Towards gradient descent
  • Optimization 3 - Gradient descent in action
  • Optimization 4 - Constrained optimization
  • Optimization 5 - Principal component analysis
05 Machine Learning with AI Workflows
  • ML: Intro to Machine Learning
  • ML: Linear Regression-1
  • ML: Linear Regression-2
  • ML: Linear Regression-3
  • ML: Linear Regression-4
  • ML: Polynomial Regression, Bias-Variance
  • ML: Regularisation, Cross Validation
  • ML: Logistic Regression-1
  • ML: Logistic Regression-2
  • ML: Classification Metrics (CM)
  • ML: CM contd. + Imbalanced Data
06 Supervised Machine Learning with AI
  • ML: k Nearest Neighbors-1
  • ML: k Nearest Neighbors-2
  • ML: Decision Trees-1
  • ML: Decision Trees-2
  • ML: Bagging and Random Forest
  • ML: Boosting-1
  • ML: Boosting-2
  • ML: Other Ensemble Techniques
  • ML: Naive Bayes 1
  • ML: Naive Bayes 2
  • ML: SVM-1
  • ML: SVM-2
  • ML: Supervised ML Wrap-up (Optional)
07 Un-supervised Machine Learning with AI
  • Dimensionality Reduction using PCA
  • Dimensionality Reduction using tSNE
  • Dimensionality Reduction using tSNE contd + UMAP
  • Clustering using KMeans
  • Clustering using KMeans++
  • Clustering using GMM
  • Clustering using Hierarchical Clustering
  • Clustering with DBSCAN
  • Anomaly Detection
  • ML: End to End Business Case - 1
  • ML: End to End Business Case - 2
08 Time Series and Recommender Systems with AI Workflows
  • ML: Intro to Time Series and Forecasting
  • ML: Time Series Analysis - 1
  • ML: Time Series Analysis - 2
  • ML: Time Series Analysis - 3
  • ML: Time Series Analysis - 4
  • ML: Time Series Analysis - 5
  • ML: Recommender Systems-1
  • ML: Recommender Systems-2
  • ML: Recommender Systems-3
  • ML: Recommender Systems-4
  • ML: Recommender Systems-5
  • ML: End-to-end Practical Session
09 MLOps & AI Deployment
  • Git & GitHub: Setup for MLOps
  • Building Cars24 ML tool using Streamlit
  • Develop Web APIs using Flask
  • Containerization - Docker & DockerHub
  • Deploying APIs on AWS using ECS
  • GitHub Actions - Setting up CI pipelines
  • GitHub Actions - Setting up CD pipelines
  • Experiment Tracking & Data Management using MLFlow
  • ML System Design - 1
  • ML System Design - 2
  • Building ML pipelines with AWS Sagemaker
  • Processing large scale data using Apache Spark

10 Deep Learning & Neural Networks
  • NN: Intro to Neural Networks
  • NN: Forward and Back Propagation
  • N-Layer Neural Network - 1
  • N-Layer Neural Network - 2
  • N-Layer BackPropagation
  • Tensorflow and Keras - 1
  • Tensorflow and Keras - 2
  • Optimizers for NNs
  • Hyper Parameter Tuning for NNs
  • Autoencoders
  • Practical aspects of designing MLPs and debugging
  • Model interpretability: LIME
11 AI engineering and Agentic AI
  • Introduction to AI Engineering
  • Refresher: Modern Text & Image AI Model Architectures
  • Getting started with Text Generation LLM APIs (OpenAI API)
  • Designing Eval Pipelines for AI Apps and Observability
  • Prompt Engineering: Introduction
  • Retrieval-Augmented Generation (RAG) Introduction
  • Embeddings Deep Dive
  • Multimodal & Tabular RAG
  • Prompt Engineering: Security
  • Agents: Foundations & Planning
  • Advanced Agent Concepts
  • Agent: Frameworks and Protocols
  • Fine Tuning Existing Models
  • Audio, Image, Video, Expressions: AI Modalities
  • Scaling AI Applications
  • Model Quantization Techniques
  • Advanced Fine Tuning Techniques
  • Dataset Engineering && Inference Optimization