Programming for Data Science
list 10 sequences
assignment Level : Introductive
chat_bubble_outline Language : English
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Key information

credit_card Free access
verified_user Fee-based Certificate
timer 80 hours in total

About the content

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 and our innovative ProcessingJS Workspace application 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 ProcessingJS 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 develop your understanding of big data in the world around you.

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

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Syllabus

Section 1: Creative code - Computational thinking
Understanding what you can do with ProcessingJS 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 ProcessingJS 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.
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Instructors

Katrina Falkner
Head - School of Computer Science
University of Adelaide

​Claudia Szabo
Senior Lecturer, School of Computer Science
University of Adelaide

Nick Falkner
Associate Professor, School of Computer Science
University of Adelaide

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Content designer

University of Adelaide
University of Adelaide
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Platform

Edx

Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with EdX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

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