🐍
Module 1: Python for Data Science — 20 Hours
Python fundamentals: variables, data types, loops, functions. Lists, tuples, dictionaries, sets, file operations. NumPy: arrays, vectorised operations, broadcasting. Pandas: Series, DataFrames, indexing, filtering, merging, grouping. Handling missing values and duplicates. Matplotlib, Seaborn and Jupyter Notebook.
🗄️
Module 2: SQL & Database Management — 15 Hours
Relational database concepts, ER diagrams, normalisation. SELECT, WHERE, ORDER BY, GROUP BY, HAVING. All join types: inner, left, right, full outer, self, cross. Subqueries and Common Table Expressions. Window functions: ROW_NUMBER, RANK, LEAD, LAG. Connecting SQL databases to Python.
📐
Module 3: Statistics & Probability for Data Science — 15 Hours
Descriptive statistics: mean, median, variance, standard deviation. Probability fundamentals and data distributions. Sampling methods and the Central Limit Theorem. Hypothesis testing: p-values, confidence intervals, t-tests, chi-square, ANOVA. Correlation and regression basics. A/B testing in a business context.
🧹
Module 4: Data Cleaning & Exploratory Data Analysis — 12 Hours
Data quality assessment and profiling. Handling missing values: deletion, imputation, flagging. Outlier detection and treatment. Feature creation and binning. Univariate, bivariate and multivariate analysis. Correlation heatmaps and structuring an EDA report.
🤖
Module 5: Machine Learning (Supervised & Unsupervised) — 25 Hours
Linear and logistic regression. Decision trees, random forests and ensemble methods. Support Vector Machines and K-Nearest Neighbours. K-Means clustering, hierarchical clustering and PCA. Model evaluation: accuracy, precision, recall, F1, ROC-AUC. Cross-validation, hyperparameter tuning and pipelines.
🧠
Module 6: Deep Learning & Neural Networks — 15 Hours
Perceptrons, activation functions and backpropagation. TensorFlow and Keras basics. Feed-forward neural networks and regularisation. Convolutional Neural Networks (CNN) for images. Recurrent Neural Networks (RNN) and LSTMs for sequences. Transfer learning and model fine-tuning.
💬
Module 7: Natural Language Processing (NLP) — 10 Hours
Text preprocessing: tokenisation, stemming, lemmatisation. Bag of Words, TF-IDF and word embeddings. Sentiment analysis and text classification. Named Entity Recognition (NER). Topic modelling with LDA. Introduction to transformers and BERT.
📈
Module 8: Power BI & Tableau for Data Science — 15 Hours
Power BI: connecting to Excel, SQL, CSV and web sources. Data modelling, relationships and DAX measures. Dashboard design, filters, slicers and drill-through. Tableau: dimensions, measures, calculated fields. Filters, groups, sets, parameters and dashboards. Publishing dashboards for business stakeholder review.
✨
Module 9: Generative AI, LLMs & Prompt Engineering — 10 Hours
Prompt engineering for data tasks. Generating and debugging SQL and Python with AI assistance. Copilot in Excel and Power BI. AI-assisted insight summaries and report drafting. Verifying AI output: hallucinations, wrong aggregations, false confidence. Data privacy and what should never be pasted into a public AI tool.
☁️
Module 10: MLOps & Model Deployment — 8 Hours
Model versioning and experiment tracking with MLflow. Packaging models with Flask/FastAPI. Deploying models to cloud (AWS/GCP/Azure basics). Monitoring model performance and drift. CI/CD basics for ML pipelines. Retraining strategies and data versioning.
🚀
Module 11: Capstone Projects & Career Preparation — 10+ Hours
End-to-end capstone project on a real business dataset. Building a GitHub portfolio and dashboard showcase. Data science resume writing and LinkedIn optimisation. SQL and case-study interview practice. Mock interviews with feedback.
