Programming for Data Science

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Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems.

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There is a rising demand for people with the skills to work with Big Data sets and this course can start you on your journey through our Big Data MicroMasters program towards a recognised credential in this highly competitive area.

Using practical activities you will learn how digital technologies work and will develop your coding skills through engaging and collaborative assignments.

You will learn algorithm design as well as fundamental programming concepts such as data selection, iteration and functional decomposition, data abstraction and organisation. In addition to this, you will learn how to perform simple data visualisations using Processing and embed your learning using problem-based assignments.

This course will test your knowledge and skills in solving small-scale data science problems working with real-world datasets and developing your understanding of big data in the world around you.

  • How to analyse data and perform simple data visualisations using Processing
  • Understand and apply introductory programming concepts such as sequencing, iteration and selection
  • Equip you to study computer science or other programming languages

Section 1: Creative code – Computational thinking
Understanding what you can do with Processing and applying 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 repetition 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 demonstrate 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.

Programming for Data Science

DISCLAIMER: Courses on Future Syllabus are free but subject to return to their original prices on host platforms upon coupon expiration. Enrol while they are free. To report any error, use the "Further Actions" tab.

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  1. […] Deliver faster, higher quality, and fault-tolerant products regardless of the industry using the latest in Agile, DevOps, and Data Science. […]

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