Course Content

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MODULE 1: FOUNDATIONS

1) Introduction to Data Analytics

What is Data Analytics vs Data Science vs Business Intelligence
Roles and responsibilities: Business Analyst, Data Analyst, BI Developer
Analytics workflow: Data collection → Analysis → Visualization → Insights → Action
Real-world case studies from industry (finance, retail, e-commerce, healthcare)
Career paths and salary benchmarks in analytics

2) Python Environment Setup for Analytics

Installing Python, Jupyter Notebook, VS Code
Package managers: pip, conda
Setting up virtual environments
Introduction to IDE best practices

3) Python Fundamentals for Analytics

Language fundamentals: variables, data types, operators
Control flow: if-else, loops (for, while)
String manipulation and formatting
Lists, tuples, dictionaries for data handling
Functions: defining, parameters, return values

MODULE 2: DATA MANIPULATION & EXPLORATION

4) NumPy Essentials

NumPy arrays vs Python lists: performance benefits
Array creation, indexing, slicing
Mathematical operations on arrays
Broadcasting concepts
Statistical functions (mean, median, std, percentile)
Practical: Working with multi-dimensional datasets

5) Pandas for Data Analysis (Core Module)

Series and DataFrame fundamentals
Creating DataFrames from CSV, Excel, SQL databases
Indexing and selecting data
Viewing and inspecting data (head, tail, info, describe)

6) Data Cleaning and Transformation

Handling missing values (dropna, fillna, interpolation)
Removing duplicates
Data type conversions
Handling outliers

7) Data Manipulation

Filtering and sorting
Groupby operations and aggregations
Pivot tables and crosstabs
Merging, joining, concatenating DataFrames

8) Time Series Basics

DateTime handling
Resampling and rolling windows
Trend analysis

9) Exploratory Data Analysis (EDA) Theory

Understanding data distribution
Identifying patterns and relationships
Statistical thinking for analysts
Types of analysis: univariate, bivariate, multivariate
Hypothesis formation from data exploration

MODULE 3: DATA VISUALIZATION & STORYTELLING

10) Matplotlib Fundamentals

Figure and axis objects
Line plots, bar charts, histograms, scatter plots
Customization: colors, markers, line styles
Legend, labels, titles, annotations
Grid and axis formatting
Multiple plots: subplots and figure layouts

11) Advanced Visualization with Seaborn

Statistical visualization concepts
Categorical plots: bar, box, violin, strip plots
Distribution plots: histograms, KDE plots
Relationship plots: scatter, regression plots, heatmaps
Color palettes and styling
FacetGrid for multi-dimensional analysis

12) Interactive Dashboards with Plotly & Streamlit

Plotly basics: interactive charts and hover information
Creating dashboards with Streamlit
Deploying Streamlit apps to cloud (Streamlit Cloud, Heroku)
Real-time data updates
Dashboard best practices

13) Data Storytelling

Principles of data visualization
Choosing the right chart for the message
Narrative structure: context, conflict, resolution
Presenting insights to non-technical stakeholders
Creating compelling business reports

MODULE 4: STATISTICS FOR BUSINESS DECISIONS

14) Descriptive Statistics

Central tendency: mean, median, mode
Spread: range, variance, standard deviation, IQR
Skewness and kurtosis
Outlier detection and handling (IQR method, Z-score)
Weighted statistics

15) . Probability Basics

Fundamentals: sample space, events, probability rules
Conditional probability and Bayes' theorem
Independence and dependence
Real-world application scenarios

16) Inferential Statistics

Sampling concepts and distributions
Confidence intervals: for mean and proportion
Hypothesis testing: null vs alternative hypotheses
T-tests, Z-tests, chi-square tests
P-values and significance levels
Type I and Type II errors

MODULE 5: SQL FOR DATA ANALYSTS

17) SQL Fundamentals

SELECT, WHERE, ORDER BY, LIMIT
Filtering with AND, OR, NOT, IN, BETWEEN
DISTINCT and COUNT

18) Data Aggregation & Joins

GROUP BY and aggregation functions (SUM, AVG, MAX, MIN, COUNT)
HAVING clause
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
Self-joins and multiple joins

MODULE 6: FOUNDATIONS & LLM BASICS

19) Introduction to Generative AI

What is Generative AI vs Traditional ML
GenAI applications across industries (content, code, analytics, customer service)
The GenAI landscape: LLMs, Diffusion models, Multimodal models
Business use cases and ROI analysis
Ethical considerations and responsible A

20) Fundamentals of Large Language Models

History and Evolution

From word embeddings (Word2Vec) to transformers
GPT-3 vs GPT-4 vs Claude vs Llama evolution
Understanding the transformer architecture (conceptual)

How LLMs Work

Tokens and tokenization
Attention mechanisms (high-level)
Training process: next token prediction
Context windows and token limits
Temperature, top-k, top-p sampling explained

21) Shared Foundations (Python, NumPy, Pandas, Statistics)

Python fundamentals for LLM applications
NumPy for numerical operations
Pandas for handling structured data
Statistics essentials for model evaluation
Setting up ML development environment (Jupyter, VS Code, Colab)

MODULE 7: WORKING WITH LLMs APIs & MODELS

22) LLM APIs and Platforms

Cloud-Based LLM APIs

OpenAI API (GPT-4, GPT-3.5): authentication, rate limiting, cost optimization
Google Cloud Vertex AI: Gemini models, deployment options
Anthropic Claude API: working with long context windows
Azure OpenAI: enterprise deployment

Open Source Models

Hugging Face ecosystem: model hub, model cards, licensing
Llama 2, Mistral, CodeLlama: deployment and usage
Running models locally: HuggingFace Transformers, Ollama
Quantization: reducing model size for edge deployment

23) Prompt Engineering Fundamentals

Prompt Structure

System prompts vs user prompts
Role-based prompting
Few-shot vs zero-shot prompting
Chain-of-thought prompting for complex reasoning

Advanced Techniques

Prompt injection and security
Template-based prompts for scalability
Evaluating prompt quality
Iterative prompt optimization

24) LangChain Fundamentals

Core Concepts

LLMs, Chains, Agents, Memory
Prompt templates and formatting
Output parsing and validation
Sequential chains and branching logic

Working with LangChain

Integrating with OpenAI, HuggingFace, local models
Building chatbots with conversation memory
Document loaders and text splitters
Chaining multiple LLM call

MODULE 8: RETRIEVAL-AUGMENTED GENERATION (RAG)

25) RAG Architecture & Concepts

Why RAG?

Hallucination problem in LLMs
Keeping models current without retraining
Domain-specific knowledge integration
Cost optimization vs accuracy tradeoff

RAG Workflow

Data ingestion pipeline
Chunking and preprocessing strategies
Embedding generation and semantic search
Retrieval ranking and reranking
LLM generation with context

26) Vector Databases

Introduction to Vector Databases

Why embeddings and vector similarity matter
Similarity metrics: cosine, L2, dot product
Vector database landscape: Pinecone, Weaviate, Qdrant, Milvus, Chroma

Using Vector Databases

Creating and managing vector indexes
Embedding models: OpenAI embeddings, open-source models
Storing and retrieving vectors
Metadata filtering and hybrid search
Scaling vector databases

27) Building Production RAG Systems

End-to-End RAG Pipeline

Document ingestion: PDF, web, database sources
Text preprocessing: cleaning, chunking, overlap strategies
Embedding storage and indexing
Query expansion and multi-step retrieval
Context ranking and relevance filtering

LangChain + Vector DB Integration

Retrievers in LangChain
Prompt engineering for RAG
Answer generation with source attribution
Handling multi-document retrieval

Evaluation & Optimization

Retrieval quality metrics: precision, recall, MRR, NDCG
Generation quality: semantic similarity, BLEU, ROUGE, LLM-as-judge
Latency and cost optimization
A/B testing retrieval strategies

MODULE 9: CAPSTONE PROJECT

28) End-to-End GenAI Application

Project Options:

1. RAG-based Q&A System: Build chatbot for company knowledge base

2. Fine-Tuned Customer Support Bot: Specialize model for domain-specific queries

3. Content Generation Platform: Multi-document synthesis with custom guidelines

4. Code Assistant: Fine-tuned model for specific programming context

MODULE 10: AGENTIC AI FOUNDATIONS

29) Introduction to Agentic AI

Paradigm Shift: RAG to Agents

Limitations of static RAG systems
Why autonomous agents matter in 2026
Agent vs Chatbot vs Automation: fundamental differences
Business applications: autonomous research, customer support, operations, data analysis

The Agent Architecture