Data Science

Duration: 6 Saturdays (2pm–4pm) | Level: Beginner to Advanced | Mode: Online | Course Notes

Course Content

Module 1 — SQL & Data Wrangling

• Introduction to relational databases and SQL fundamentals
• Joins: INNER, LEFT, RIGHT, FULL OUTER joins
• Window functions: ROW_NUMBER, RANK, DENSE_RANK, PARTITION BY
• Data cleaning and transformation techniques
• Data manipulation using pandas (Python)
• Data wrangling using dplyr (R)

Module 2 — Exploratory Data Analysis (EDA) & Visualization

• Understanding data distributions and patterns
• Data summarisation and feature exploration
• Matplotlib for statistical plotting (Python)
• Seaborn for advanced visualisation
• ggplot2 for data visualization in R
• Interactive dashboards using Tableau

Module 3 — Statistical Inference & Regression

• Probability fundamentals and statistical reasoning
• Hypothesis testing and A/B testing design
• Confidence intervals and significance testing
• Linear regression modelling
• Logistic regression for classification problems
• Model assumptions and diagnostic checks

Module 4 — Machine Learning: Trees & Ensembles

• Decision trees and splitting criteria
• Random Forest algorithm and feature importance
• Gradient boosting and XGBoost
• Model evaluation metrics (accuracy, precision, recall, F1-score)
• Hyperparameter tuning techniques
• Cross-validation strategies

Module 5 — Unsupervised Learning & Introduction to Deep Learning

• Clustering techniques: K-Means clustering
• Dimensionality reduction using PCA
• Anomaly detection basics
• Introduction to neural networks
• Basic deep learning concepts and architectures

Module 6 — MLOps & Capstone Project

• Introduction to MLOps and production ML pipelines
• Model deployment using FastAPI
• Interactive applications using Streamlit
• Model versioning and monitoring basics
• End-to-end capstone project development
• Final presentation and evaluation

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Starting Date: 11th July 2026
Course Fee: £2990.00

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