Data Science Course

Build strong SQL and data-engineering foundations for analytics, Machine Learning and AI applications.

• Relational databases, data modelling and normalization
• Advanced SQL joins, subqueries, CTEs and window functions
• Data cleaning, transformation and quality validation
• SQL query optimisation and execution-plan fundamentals
• SQL-based feature engineering and business analytics
• Connecting SQL databases with Python
• AI-assisted SQL generation and query validation

Hands-on Project: E-commerce Revenue and Customer Analytics

Assessment & Interview Preparation Sessions

Develop production-quality Python programs for data analysis, Machine Learning and AI applications.

• Python fundamentals, clean coding and modular programming
• Modern Python features, type hints and dataclasses
• Object-Oriented Programming for reusable AI pipelines
• NumPy and Pandas for data processing and analytics
• Data preprocessing, transformation and validation
• Working with APIs, SDKs and external data sources
• Plotly and Dash for interactive analytics applications
• Poetry, uv, Pydantic and Pandera
• AI-assisted code generation, debugging and refactoring

Hands-on Project: Automated Data Cleaning and Validation Pipeline

Assessment & Interview Preparation Sessions

Build, evaluate and explain Machine Learning models for real-world business problems.

• Translating business problems into Machine Learning solutions
• End-to-end Scikit-learn pipelines
• Linear, regularised and tree-based regression
• Logistic regression, decision trees and random forests
• Gradient boosting and XGBoost
• K-Nearest Neighbours and Support Vector Machines
• K-means clustering and customer segmentation
• PCA and dimensionality reduction
• Feature engineering and feature selection
• Cross-validation and hyperparameter optimisation
• Handling imbalanced datasets
• Business-focused model evaluation
• Explainable AI using feature importance and SHAP
• Bias, fairness and responsible Machine Learning
• Time-series forecasting, trend and seasonality analysis

Industry Projects:

• Customer Churn Prediction
• Credit Risk or Fraud Detection
• Customer Segmentation
• Sales and Demand Forecasting

Assessment & Interview Preparation Sessions

Develop Deep Learning and Natural Language Processing applications using modern neural-network architectures.

• Neural networks, tensors and computational graphs
• Forward propagation and backpropagation
• Loss functions, optimisers and training loops
• Building neural networks using PyTorch
• Regularisation, dropout and early stopping
• Convolutional Neural Networks
• Transfer learning using pretrained models
• Text preprocessing and text embeddings
• Attention and transformer fundamentals
• Working with pretrained NLP models
• Model inference, validation and evaluation

Hands-on Project: Image Classification andor Text Classification Application

Build enterprise-ready Generative AI applications powered by Large Language Models and private data.

• Generative AI, foundation models and LLM architecture
• Tokens, embeddings and context windows
• Open-source and closed-source LLMs
• Zero-shot, one-shot and few-shot prompting
• Role prompting, context prompting and prompt templates
• Structured outputs and JSON responses
• Calling LLM APIs using Python
• Introduction to function and tool calling
• Document ingestion and basic chunking
• Vector databases using FAISS or ChromaDB
• Semantic search and context construction
• Citation generation and grounded responses
• Introduction to hallucination, safety and privacy
• Overview of RAG versus fine-tuning

Hands-on Project: Basic Document Intelligence and RAG Assistant with Source Citations

Understand how AI agents use language models, tools and data to complete simple business and data-analysis tasks.

• Introduction to AI agents and agentic workflows
• Planner, memory, tools and feedback loops
• Introduction to task decomposition
• Single-agent systems
• Introduction to LangChain and LangGraph
• Custom tools, API integration and function calling
• Short-term agent memory
• Human-in-the-loop approvals
• Basic error handling and retries
• Introduction to data-analysis and SQL agents
• Introduction to Agentic RAG workflows
• Low-code AI automation using n8n
• Responsible and secure agent design

Hands-on Project: Basic AI-Powered Data Science Copilot and or Business Process Agent

Apply learned skills to solve real-world business problems and build a strong portfolio.

• Data cleaning, ETL, EDA and visualisation
• Feature engineering and model selection
• Model evaluation, optimisation and explainability
• Business insights, GitHub documentation and project presentation

Minor Projects & Case Studies

• E-commerce and Customer Analytics
• Credit Risk and Fraud Detection
• Customer Segmentation
• Sales and Demand Forecasting
• Recommendation Engine
• House Price Prediction
• Census and Demographic Analytics
• Image Classification and Object Detection

Capstone Project:

Project 1: Customer Revenue and Churn Intelligence Platform

Build an integrated solution covering:

• SQL data processing
• Exploratory Data Analysis
• Customer segmentation
• Churn prediction
• Revenue forecasting
• Power BI dashboard
• FastAPI and Docker deployment

Project 2: Enterprise Knowledge and Decision Copilot

Build a production-ready AI solution covering:

• Enterprise document ingestion
• RAG and vector search
• Source citations
• Agentic workflows
• LLM evaluation and guardrails
• API development
• Deployment and observability

• Learn to create compelling resumes and strategies for making your resume ATS compliant.
• Understand how to create a job search strategy and align your LinkedIn with target job roles.
• Take Mock Interviews with real-time hiring managers along with actionable feedback on how you can perform better.
• Get access to our network of 3,100+ hiring partners and early access to applying for open job roles at these companies.

Understand how Machine Learning models are converted into applications and prepared for basic deployment.

• Introduction to MLOps and the Machine Learning deployment lifecycle
• Machine Learning experiment tracking using MLflow
• Basic model versioning and reproducibility
• Building a simple model API using FastAPI
• Introduction to application containerisation using Docker
• Cloud deployment fundamentals
• Introduction to model performance and data-drift monitoring
• Overview of LLM and AI application observability
• Basic monitoring of prompts, tokens, latency and cost
• Responsible and secure AI deployment fundamentals

Hands-on Project: Deploy a Basic Machine Learning Model

Transform raw business data into interactive dashboards and executive-level insights.

• Advanced Excel functions, Pivot Tables and data validation
• Data transformation using Power Query
• Power BI data modelling and star-schema design
• DAX measures, calculated columns and time intelligence
• Interactive dashboards, drill-throughs and slicers
• Dashboard performance and visual-design fundamentals
• Business storytelling and executive dashboard design
• Microsoft Copilot for insight summaries and report creation

Hands-on Project: Executive complex Business Performance Dashboard