Foundation
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Introduction to Python
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Python basics: Lists, Functions, Packages, NumPy
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Foundation
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Data Types for Data Science
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Lists, Tuples, Dictionaries and some more advanced data containers. Dates/Times
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Foundation
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Python Data Science Toolbox (Part 1)
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User-Defined Functions, Scope, Lambda Functions and Error-Handling
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Foundation
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Python Data Science Toolbox (Part 2)
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Iterators, List Comprehension and Generators
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Foundation
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Software Engineering for Data Scientists in Python
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Modules, Classes, Maintainability
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Data Manipulation
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Importing Data in Python (Part 1)
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Importing data from files (e.g. csv, excel etc.)
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Data Manipulation
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Importing Data in Python (Part 2)
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Importing data from APIs
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Data Manipulation
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Cleaning Data in Python
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Outliers, missing values, duplicates, pivots, combining, string and pattern matching
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Data Manipulation
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Introduction to Databases in Python
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SQL queries in Python, SQLAlchemy, database creation and update
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Data Manipulation
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pandas Foundations
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Data import, exploration, quick visualisation, time series
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Data Manipulation
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Manipulating DataFrames with pandas
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Index, slice, filter, advanced indexing, pivoting/reshaping, splitting, grouping
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Data Manipulation
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Merging DataFrames with pandas
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Shared indices, concatenation, merging dataframes
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Data Manipulation
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Analyzing Police Activity with pandas
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pandas case study
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Data Visualisation
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Introduction to Data Visualization with Python
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Matplotlib, plot customisation and improvements, seaborn
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Data Visualisation
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Data Visualization with Seaborn
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seaborn recap, complex plots, mutiplots, customisation
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Data Visualisation
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Interactive Data Visualization with Bokeh
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Basic plots with bokeh, multiplots and layout, tooltips and annotations, bokeh server applications for visualisation
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Data Visualisation
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Visualizing Geospatial Data in Python
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2 layer maps, scatterplots, GeoJSON, projections and coordinate transforms, spatial joins, street layer map, geopandas and folium
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Data Visualisation
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Improving your Data Visualizations in Python
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Improving plotting and data visualisation, appropriate colour choice, showing uncertainty
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Analysis and Modelling
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Statistical Thinking in Python (Part 1)
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Summary stats and data exploration, inference and probability, probability distributions and discrete variables
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Analysis and Modelling
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Statistical Thinking in Python (Part 2)
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Finding optimal parameters, bootstrap confidence intervals, hypothesis testing
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Analysis and Modelling
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Supervised Learning with Scikit-Learn
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k-nearest neighbours, linear regression, cross-validation, regularisation, logistic regression, ROC/AUC, train/test split and holdouts, encoding and normalising data, pipelines
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Analysis and Modelling
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Unsupervised Learning in Python
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Clustering, hierarchical clustering and t-SNE, principal components analysis, dimension reduction with non-negative matrix factorisation
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Analysis and Modelling
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Machine Learning with Tree-Based Models in Python
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scikit-learn: CART, bias-variance tradeoff, cross-validation, random forests, hyperparameter tuning, boosting
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