Module 1 — Foundations of Data Science
Understand what data science is, how it works, and why every major company runs
on it.
🔬 Core Topics
Overview of Data Science and its importance
Data Science workflow and stages
Core elements: gathering, cleaning, analysis, visualisation, ML,
deployment
Real-world applications across industries
🤖 AI Integration NEW
Use ChatGPT to explore data science career paths and create a study
roadmap
Perplexity AI for real-time case studies of data science in any industry
Use AI to compare data science roles: analyst vs scientist vs engineer
Gemini to summarise research papers and extract key data science
concepts
TOOLS:ChatGPTPerplexity AIGoogle GeminiNotion AI
Module 2 — Setting Up Your Environment
Install and configure everything — Python, Jupyter, Anaconda, and the core
libraries.
⚙️ Core Topics
Installing Python, Jupyter Notebook, and Anaconda
Configuring NumPy, Pandas, Matplotlib, Seaborn
Google Colab setup and cloud notebooks
Version control basics with Git and GitHub
🤖 AI Integration NEW
GitHub Copilot setup in VS Code and Jupyter Notebooks
Google Gemini in Colab — AI assistance directly inside your notebook
Use ChatGPT to debug environment setup errors instantly
AI to generate requirements.txt and environment config files
TOOLS:GitHub CopilotGemini in
ColabChatGPTCodeium
Module 3 — Python for Data Science
Master the Python skills that every data scientist uses — lists, dicts,
functions, and Pandas DataFrames.
🐍 Core Topics
Python data structures: lists, dicts, tuples
Functions, loops, and list comprehensions
NumPy arrays and vectorised operations
Pandas DataFrames: creation, indexing, operations
🤖 AI Integration NEW
GitHub Copilot to auto-complete Pandas operations as you type
ChatGPT Code Interpreter to explain any Python error in plain English
Use AI to generate Pandas cheat sheets and operation examples on demand
Codeium for faster Python coding with AI-powered autocomplete
TOOLS:GitHub CopilotChatGPTCodeiumGemini in Colab
Module 4 — Data Collection
Gather data from the web, APIs, and databases — the foundation of every data
science project.
🌐 Core Topics
Web scraping with BeautifulSoup and Selenium
Consuming REST APIs with Python requests
Loading CSV, Excel, JSON, and SQL data
Kaggle datasets and open data sources
🤖 AI Integration NEW
Use ChatGPT to generate web scraping scripts from any URL description
GitHub Copilot to write API call and data extraction code instantly
AI to generate data collection pipelines from natural language
requirements
Prompt Perplexity AI to find the best open datasets for any project idea
TOOLS:ChatGPTGitHub CopilotPerplexity AICodeium
Module 5 — Data Cleaning & Preprocessing
Real data is messy — learn to handle missing values, outliers, and feature
engineering like a pro.
🧹 Core Topics
Managing missing data: imputation strategies
Outlier detection and treatment methods
Feature engineering and encoding
Normalisation, standardisation, and scaling
🤖 AI Integration NEW
ChatGPT Code Interpreter — upload a CSV, get a full cleaning report
instantly
Julius AI for one-click AI-powered data profiling and missing value
analysis
GitHub Copilot to write complete data preprocessing pipelines
Use AI to generate feature engineering suggestions for any domain
TOOLS:ChatGPT Code InterpreterJulius
AIGitHub Copilot
Module 6 — EDA & Statistics
Find patterns before modelling — statistical thinking and exploratory data
analysis done right.
📊 Core Topics
Descriptive statistics and data distributions
Correlation analysis and feature relationships
Hypothesis testing basics
EDA workflow with Pandas and Matplotlib
🤖 AI Integration NEW
ChatGPT to interpret statistical test results in plain English
Julius AI — natural language EDA: "show me correlations" on any dataset
Gemini in Colab for inline AI explanations of statistical patterns
Use AI to auto-generate EDA reports and data quality summaries
TOOLS:Julius AIChatGPTGemini in ColabNoteable AI
Module 7 — Data Visualisation
Communicate insights visually — from basic Matplotlib to interactive Plotly
dashboards.
📈 Core Topics
Matplotlib: line, bar, scatter, histogram, boxplot
Seaborn: heatmaps, pairplots, categorical charts
Plotly for interactive visualisations
Storytelling with data — design principles
🤖 AI Integration NEW
Julius AI — describe any chart in plain English, get the code instantly
ChatGPT to generate Plotly dashboards from dataset descriptions
GitHub Copilot for auto-completing Seaborn and Matplotlib chart code
Use AI to write insight narratives for any visualisation
TOOLS:Julius AIChatGPTGitHub CopilotNoteable AI
Module 8 — Intro to Machine Learning
Understand how machines learn — types of ML, the ML pipeline, and Scikit-learn
fundamentals.
🤖 Core Topics
Supervised, unsupervised, reinforcement learning
The ML pipeline: data → model → evaluation → deploy
Train/test split and cross-validation
Scikit-learn API and model selection
🤖 AI Integration NEW
ChatGPT to explain any ML algorithm with visual analogies
GitHub Copilot to generate complete Scikit-learn pipelines
Use AI to compare model options for any given data problem
Gemini in Colab for inline ML concept explanations while coding
TOOLS:ChatGPTGitHub CopilotGemini in ColabCodeium
Module 9 — Supervised Learning
Regression and classification — predict outcomes and classify data with real
datasets.
📐 Core Topics
Linear and logistic regression
Decision Trees and Random Forests
SVM and K-Nearest Neighbours
Model evaluation: accuracy, F1, ROC-AUC
🤖 AI Integration NEW
ChatGPT to generate hyperparameter tuning code for any Scikit-learn
model
GitHub Copilot to write feature importance analysis and model comparison
code
Use AI to interpret model evaluation metrics in business-friendly
language
Weights & Biases AI for experiment tracking and model versioning
TOOLS:ChatGPTGitHub CopilotW&B AICodeium
Module 10 — Unsupervised Learning
Find hidden patterns without labels — clustering, dimensionality reduction, and
anomaly detection.
🔍 Core Topics
K-Means clustering and cluster evaluation
Hierarchical and DBSCAN clustering
PCA for dimensionality reduction
Anomaly detection basics
🤖 AI Integration NEW
ChatGPT to explain cluster analysis results in business context
Julius AI for instant AI-generated clustering visualisations
GitHub Copilot to generate PCA and t-SNE visualisation code
Use AI to write customer segmentation reports from clustering results
TOOLS:ChatGPTJulius AIGitHub Copilot
Module 11 — Deep Learning Intro
Neural networks, TensorFlow, Keras — understand how AI systems actually learn.
🧠 Core Topics
Neural network architecture and forward propagation
Backpropagation and gradient descent explained
TensorFlow and Keras: build your first neural network
CNNs for image classification basics
🤖 AI Integration NEW
ChatGPT to explain neural network concepts with step-by-step analogies
GitHub Copilot to generate Keras model architectures from descriptions
Hugging Face for pre-trained models and transfer learning
Use AI to debug training loops and interpret loss curves
TOOLS:ChatGPTGitHub CopilotHugging FaceGemini in Colab
Module 12 — Capstone Project
Apply everything — collect, clean, analyse, model, and present a complete data
science project.
🎯 Core Topics
End-to-end project: problem definition to deployment
Project documentation and GitHub README
Model deployment with Streamlit or Flask
Portfolio presentation and LinkedIn showcase
🤖 AI Integration NEW
ChatGPT to write full project documentation and README files
GitHub Copilot for building Streamlit deployment code
Use AI to generate LinkedIn project write-ups and portfolio descriptions
AI to prepare mock interview answers about your capstone project
TOOLS:ChatGPTGitHub CopilotGeminiNotion AI