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Python & Data Science
Python for Data & AI
Stop guessing what your data means. Learn Python from the ground up and immediately apply it to real data analysis, visualisation, and machine learning. No prior coding experience required — just bring curiosity and a dataset. 10 practical modules take you from your first variable to a fully deployed machine learning pipeline. Every module ships working Python code and a project you can add to your portfolio.
Course Curriculum
Conditional statements — if, elif, else
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Loops — for, while, and loop control
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List and dictionary comprehensions
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Functions — parameters, return values, and scope
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Module 2 Project: Grade classifier
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Lists — ordered, mutable sequences
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Tuples and when to use them
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Dictionaries — key-value lookups
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Sets — unique collections
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Module 3 Project: In-memory student database
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Reading and writing files
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CSV and JSON — the two most common data formats
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Exception handling — try, except, else, finally
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Modules and the standard library
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Module 4 Project: CSV data processor
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NumPy arrays — creation and fundamentals
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Vectorised operations and boolean indexing
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Indexing, slicing, and reshaping
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Statistical functions and Module 5 Project
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DataFrames and Series — the Pandas data model
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Loading, saving, and exploring data
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Selecting and filtering data
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Cleaning messy data
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Groupby, aggregation, and Module 6 Project
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Matplotlib fundamentals — figures and axes
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Line, bar, and scatter charts
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Histograms and Seaborn statistical charts
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Module 7 Project: Sales dashboard
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The machine learning workflow
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Preparing data for machine learning
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Classification — Logistic Regression and Random Forest
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Regression and cross-validation
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Module 8 Project: Student pass predictor
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HTTP requests with the requests library
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Error handling for network requests
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Automating file and email tasks
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Module 9 Project: Automated data report pipeline
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Project structure, config, and requirements
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Building the modular pipeline
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Machine learning on a real public dataset
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Capstone showcase and next steps
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