Data science lifecycle: problem definition through to actionable insight · Types of data, sources, public APIs, and open datasets · Ethics in data science: bias, fairness, privacy, and GDPR compliance · Tools setup: Python, Jupyter, Anaconda, and VS Code · SQL for data retrieval: SELECT, JOINs, GROUP BY, and subqueries

Data collection: APIs, web scraping, and public datasets · Data cleaning: missing values, duplicates, outliers, and imputation · Exploratory data analysis: distributions, correlations, and patterns · Feature engineering: encoding, scaling, and transformation · Pandas advanced: groupby, merge, pivot, apply, and method chaining

Descriptive statistics and common probability distributions · Inferential statistics: hypothesis testing, p-values, and effect sizes · Confidence intervals, power analysis, and sample size calculation · Bayesian statistics: Bayes’ theorem, prior/posterior, and MCMC intro · A/B testing: design, analysis, and interpretation

Regression: linear, polynomial, ridge, lasso, and elastic net · Classification: logistic regression, KNN, decision trees, and SVM · Ensemble methods: random forest, gradient boosting, XGBoost, LightGBM · Model evaluation: accuracy, precision, recall, F1, ROC-AUC · Hyperparameter tuning: Grid Search, Random Search, and Optuna

Clustering: K-Means, DBSCAN, hierarchical, Gaussian mixture models · Dimensionality reduction: PCA, t-SNE, UMAP, and autoencoders · Anomaly detection: isolation forest and one-class SVM · Recommender systems: collaborative and content-based filtering · Deep learning introduction: ANNs, CNNs, RNNs with TensorFlow/Keras

Matplotlib and Seaborn: publication-quality statistical visualisations · Plotly and Dash: interactive web-based dashboards · Tableau or Power BI: executive-level business intelligence dashboards · Storytelling with data: choosing the right chart and building a narrative · Presenting findings to non-technical audiences with clarity and impact

Model serialisation: pickle, joblib, and ONNX formats · Building ML APIs with FastAPI: endpoints, validation, and async · Deploying models: Hugging Face Spaces, AWS SageMaker, or GCP Vertex AI · Model monitoring: data drift, concept drift, and performance degradation · MLflow: experiment tracking, model registry, and reproducibility

End-to-end project on a real-world dataset of your choice · Problem definition, EDA, feature engineering, modelling, and evaluation · Deployed interactive dashboard or REST API endpoint · Technical report: methodology, results, limitations, and recommendations · Panel presentation with Q&A from data science reviewers

Execute the full data science lifecycle from collection to model deployment · Apply supervised and unsupervised ML to real-world datasets · Build and deploy ML APIs and interactive dashboards · Communicate insights compellingly to technical and non-technical audiences · Achieve a credential benchmarked against ACM, IBM, and Google Data Analytics standards

Complete all 8 modules, deploy an end-to-end data science project with an interactive dashboard or API, and submit a technical report (minimum 75%).

Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, AI Researcher, Quantitative Analyst. Average starting salary: $75,000–$120,000 USD.