6-Week AI Bootcamp: Foundations for Data Science & AI Specializations
Designed for beginners with no prior AI or advanced math experience

Bootcamp Philosophy Recap
Hands-on first: Every concept is paired with a practical exercise.
Tool fluency: Students become comfortable with real-world tools (Python, SQL, CLI, APIs, LLM platforms, automation tools).
Conceptual clarity: Focus on intuition over equations; emphasize why before how.
Pathway-ready: Prepares students to confidently enter DS/ML/AI Engineering tracks.

Who This Course Is For
• Professionals
• Vacists
• Graduates
• Students
Designed for beginners with no prior AI or advanced mathematics background.

Learning Format
• Duration: 6 Weeks
• Mode: Physical and Online lessons available
• Structure: Weekly theory sessions paired with practical labs and mini-projects

Week 1: AI & Data Literacy + Python Foundations
Weekly Learning Outcomes
Understand the AI landscape (AI vs ML vs DL vs Data Science)
Navigate ethical issues in AI (bias, fairness, transparency)
Write basic Python scripts for data tasks
Use command line and virtual environments

Practical Skills Gained
Python syntax, data structures, functions
Terminal navigation, package management (pip, venv)
Reading/writing files, using Jupyter Notebooks

Day 1
Theory (2h)
What is AI? History, key milestones (Turing, Deep Blue, AlphaGo, Transformers)
AI vs ML vs DL vs Data Science: Venn diagram & real-world examples
Current trends: Generative AI, agents, open-source models
Practical (1h)
Install Python, VS Code / Jupyter
Hello World, variables, print, comments
Create & activate a virtual environment

Day 2
Theory (2h)
AI use cases across industries (healthcare, finance, marketing, logistics)
Responsible AI: bias in hiring algorithms, facial recognition errors
Principles of fairness, accountability, transparency
Practical (1h)
Python: lists, dictionaries, loops, conditionals
Exercise: Analyze a biased dataset (e.g., gender imbalance in job ads)

Day 3
Theory (2h)
Data lifecycle: collection → storage → processing → analysis → action
Structured vs unstructured data
Introduction to data types (numeric, categorical, text, image)
Practical (1h)
Python: reading CSV files with pandas (preview only—deep dive later)
Basic data inspection: .head(), .info()

Day 4
Theory (2h)
Linux & command line basics: file system, navigation, permissions
Why developers use CLI: reproducibility, automation, cloud workflows
Practical (1h)
Terminal exercises: ls, cd, mkdir, cp, mv, cat
Run Python script from terminal

Day 5
Theory (2h)
APIs & JSON: how apps talk to each other
REST basics: endpoints, requests, responses
Real-world example: weather API, news API
Practical (1h)
Use requests library to call a public API (e.g., JSONPlaceholder)
Parse JSON response into Python dict/list
Mini-Project Idea: Build a CLI tool that fetches and displays a random quote from an API.

Week 2: Data Handling, SQL & Statistics Intuition
Weekly Learning Outcomes
Clean and preprocess real-world datasets
Query relational databases using SQL
Interpret descriptive stats and distributions
Understand correlation ≠ causation

Practical Skills Gained
Data cleaning with pandas
Writing SQL queries (SELECT, WHERE, JOIN, GROUP BY)
Visualizing distributions with matplotlib/seaborn

Day 1
Theory (2h)
Data quality issues: missing values, duplicates, outliers, inconsistent formats
Data preprocessing pipeline: imputation, encoding, scaling (conceptual)
Practical (1h)
Load messy dataset (e.g., Titanic or sales data)
Handle missing values, drop duplicates, fix dtypes

Day 2
Theory (2h)
Relational databases: tables, keys, normalization
SQL fundamentals: SELECT, FROM, WHERE, ORDER BY
Practical (1h)
Use SQLite in Python or online SQL editor (e.g., DB Fiddle)
Query sample database (e.g., Chinook Music Store)

Day 3
Theory (2h)
Advanced SQL: JOINs, GROUP BY, aggregate functions (COUNT, AVG, SUM)
When to use SQL vs pandas
Practical (1h)
Multi-table queries: e.g., “Which customers spent most?”
Export query results to CSV

Day 4
Theory (2h)
Descriptive statistics: mean, median, mode, std dev, quartiles
Data distributions: normal, skewed, bimodal
Visualizing data: histograms, box plots
Practical (1h)
Compute stats with pandas.describe()
Plot histograms & boxplots for numeric columns

Day 5
Theory (2h)
Correlation vs causation: classic pitfalls (ice cream & drownings)
Confounding variables
Intro to probability: likelihood, events, simple rules
Practical (1h)
Compute correlation matrix (df.corr())
Visualize correlations with heatmap
Simulate coin flips/dice rolls to build probability intuition
Mini-Project Idea: Analyze a public dataset (e.g., Airbnb listings), clean it, run descriptive stats, and answer 3 business questions with SQL + Python.

Week 3: Generative AI & Prompt Engineering
Weekly Learning Outcomes
Explain how LLMs work at a high level
Design effective prompts for different tasks
Evaluate and iterate on prompt performance
Apply generative AI to text, image, and multimodal tasks

Practical Skills Gained
Using OpenAI, Anthropic, or open-source LLM APIs
Structuring prompts for reliability
Building simple generative apps