Programming for Data Science
offered by The University of Adelaide

Public University
1 Campus
Estd. 1874

Programming for Data Science
at
The University of Adelaide
Overview

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

Duration	10 weeks
Total fee	Free
Mode of learning	Online
Difficulty level	Intermediate
Official Website	Explore Free Course
Course Level	UG Certificate

Programming for Data Science
at
The University of Adelaide
Highlights

Earn a Paid Certificate after completion
Doubt support sessions Available
Access to course materials

Programming for Data Science
at
The University of Adelaide
Course details

Skills you will learn

Data visualization Data analysis Software Testing Data Science Big Data

Who should do this course?

Regrettably, learners in Iran, Cuba, and Crimea, Ukraine, can't register due to U.S. sanctions. Despite efforts, some courses remain restricted.

What are the course deliverables?

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

More about this course

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.

Programming for Data Science
at
The University of Adelaide
Curriculum

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.

Programming for Data Science
at
The University of Adelaide
Faculty details

Katrina Falkner

Katrina has a strong interest in Computer Science Education Research (CSER), mainly in the areas of collaborative and active pedagogy. She has a particular interest in the use of technology to support online learning, including massive open online courses, online collaboration environments and technology-assisted education.

?Claudia Szabo

Claudia’s main research interests lie in the area of computer systems and computer science education. Her computer science education focus lies in the area of curriculum design and analysis using emerging pedagogical and cognitive theories.