Lists, tuples, sets and dictionaries for storing and organising business data.

Arrays, indexing, reshaping, vectorised maths and array-level statistics.

DataFrames, importing and exporting files, filtering, cleaning, grouping and merging datasets.

Central tendency, dispersion, position measures and frequency summary tables in Python.

Line, bar, pie, histogram, box and scatter charts using Matplotlib and Seaborn.

Database connection, CRUD queries, aggregates, joins, subqueries and Pandas-SQL data transfer.

Covariance, correlation, simple and multiple regression, accuracy metrics and time-series forecasting.

Preprocessing, train-test split, classification models, decision trees, overfitting control and model evaluation.

K-Means, hierarchical linkage methods, Ward's method, dendrograms and cluster profiling.

Transaction data preparation, association rule mining and support, confidence and lift analysis.

Rule-based agents, neural network structure, activation functions and gradient descent training.

Constraint-based optimisation and what-if scenario analysis for decision making.

Plotly interactive charts and a Streamlit dashboard connected to the SQL database.