Data Science
Extract meaningful insights from raw data using statistical analysis and programming.
Pandas & NumPy
The two libraries every Python data workflow rests on. NumPy arrays, broadcasting and vectorisation first, then pandas Series and DataFrames for loading, filtering, grouping, merging and reshaping real datasets.
Data Visualization
Choosing the right chart and building it correctly. Line, bar, scatter, histogram and box plots in Matplotlib, statistical plots and heatmaps in Seaborn, plus multi-chart layouts, styling and time-series visualisation.
Python for Data Science
A path into data science for people who are new to Python itself: language fundamentals and file handling first, then NumPy, pandas and Matplotlib applied to CSV, Excel and JSON datasets.
R Programming
R for statistics and analysis: vectors, matrices and data frames, then dplyr for manipulation and ggplot2 for plotting, followed by hypothesis testing, regression, time series and exploratory data analysis in RStudio.
Data Cleaning
Most analysis time goes on cleaning, not modelling. Handle missing values, duplicates and outliers, standardise text and dates, validate assumptions, and turn ad-hoc fixes into repeatable pipelines using pandas and SQL.
Jupyter Notebooks
Get productive in the notebook environment most data work happens in: cells and Markdown documentation, pandas and NumPy analysis, inline charts, interactive widgets, extensions, and sharing or exporting finished notebooks.