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Two practical courses for university students, taught in blended format: live classes with a teacher, plus guided work on our learning platform between sessions. More AI courses are on the way.

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Each course is built around projects, so you finish with work you can show and explain.

Introduction to Big Data with Python

No programming needed to start

University students

Start with Python, then learn to work with text, CSV and JSON files, analyse data with pandas, store and query it with SQLite and SQL, and finally process data at scale with Apache Spark and PySpark. Big Data comes after conventional data processing, so you understand the problems Spark was built to solve.

Duration
14 units
Schedule
Live sessions timetabled for each group
  • Beginner to intermediate

What you will learn

  • Python in VS Code
  • Files: text, CSV and JSON
  • Data analysis with pandas
  • Databases with SQLite and SQL
  • Distributed computing and PySpark
  • Final ETL project on an e-commerce dataset

Constraint Programming

Basic programming required

University students

Learn to solve scheduling, timetabling and configuration problems by describing what a solution looks like and letting a solver find it. You model problems in MiniZinc, learn how solvers reason through propagation and search, and use OR-Tools CP-SAT from Python.

Duration
12 weeks, 3 hours a week
Schedule
Live sessions timetabled for each group
  • Intermediate

What you will learn

  • Variables, domains and constraints
  • Global constraints
  • Propagation and search
  • Optimisation
  • Scheduling and resource problems
  • Capstone: model a real problem

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