Programming for Data Science

Section 1: Creative code - Computational thinking
Understanding what you can do with Processing and apply the basics to start coding with colour; Learn how to qualify and express how algorithms work.

Section 2: Building blocks - Breaking it down and building it up
Understand how data can be represented and used as variables and learn to manipulate shape attributes and work with weights and shapes using code.

Section 3: Repetition - Creating and recognising patterns
Explain how and why using repetiton can aid in creating code and begin using repetition to manipulate and visualise data.

Section 4: Choice - Which path to follow
How to create simple and complicated choices and how to create and use decision points in code.

Section 5: Repetition - Going further
Discussing advantages of repetition for data visualisation and applying and reflecting on the power of repetitions in code. Creating curves, shapes and scale data in code.

Section 6: Testing and Debugging
Understanding why and how to comprehensively test your code and debug code examples using line tracing techniques.

Section 7: Arranging our data
Exploring how and why arrays are used to represent data and how static and dynamic arrays can be used to represent data.

Section 8: Functions - Reusable code
Understand how functions work in Processing and demonstate how to deconstruct a problem into useable functions.

Section 9: Data Science in practice
Exploring how data science is used to solve programming problems and how to solve big data problems by applying skills and knowledge learned throughout the course.

Section 10: Where next?
Understand the context of big data in programming and transform a problem description into a complete working solution using the skills and knowledge you've learned throughout the course, and explore how you can expand the skills learned in this course by participating in future courses.