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Computational Thinking (CT) is a problem-solving process that includes some characteristics and dispositions. It can be used in any industry. It helps you to formulate a problem and to express solutions for that problem in a way that you can use a computer to solve the problem.

The [course_title] course covers the concepts of Big Data MicroMasters program. The core computational thinking concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking will be explained in the course.

Then the course focuses on data representation and analysis and the processes of cleaning, presenting, and visualising data.

Upon completion, you will be able to use computational thinking in data science.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: University of Adelaide.

Course Curriculum

Welcome to CompX Introduction 00:02:00
Lesson 1
What’s data in R all about? 00:05:00
Guided example of components in RStudio 00:02:00
The debrief 00:01:00
Summarising data: Mean, standard deviation etc. 00:02:00
From Jane to you 00:01:00
Lesson 2
Visualising relationships 00:01:00
C vs C: Marginal and conditional frequencies 00:03:00
Q vs Q: Strength, linearity, outliers, direction 00:03:00
C vs Q: Shape, location, spread, outliers 00:03:00
Lesson 3
Manipulating and joining data 00:01:00
Narrowing data down 00:03:00
Introduction to the dataset 00:01:00
Lesson 4
Transforming data and dimension reduction 00:01:00
Why does it work? 00:03:00
Mathematics of PCA – Matrices 00:03:00
Mathematics of PCA – Covariance matrices 00:05:00
Term Frequency – Inverse Document Frequency (TF-IDF) 00:01:00
Lesson 5
Population, parameters, samples, and statistics 00:01:00
Explanation of the concept 00:03:00
Estimating the population location and population spread 00:05:00
What is confidence? 00:03:00
The slow way 00:01:00
About k-mers in a genome assignment 00:01:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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