Data Science

NumPy, Pandas, Matplotlib, and Jupyter notebooks for data manipulation and visualisation.

Descriptive statistics, probability distributions, hypothesis testing, and confidence intervals.

Vectors, matrices, derivatives, and gradients — the mathematical backbone of machine learning.

Relational databases, SQL queries, joins, and aggregations for data retrieval.

GDPR, algorithmic bias, fairness in AI, and responsible data science practices.

Regression, classification, decision trees, SVMs, and model evaluation with scikit-learn.

Ensemble methods, neural networks, deep learning with TensorFlow/PyTorch.

Tableau, Power BI, and Python visualisation libraries for communicating insights to stakeholders.

Hadoop, Spark, and cloud data warehouses (BigQuery, Snowflake) for large-scale data processing.

Text preprocessing, sentiment analysis, transformers, and large language model applications.

CNNs, object detection, image segmentation, and transfer learning.

ARIMA, LSTM, and Prophet for financial, operational, and demand forecasting.

Model versioning, containerisation, REST API serving, and monitoring in production.

Connecting data science outputs to business strategy and executive decision-making.

An end-to-end data science project solving a real-world problem with a partner organisation.