Programming for Data Science
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 recognized 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 organization. In addition to this, you will learn how to perform simple data visualizations 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.
What you’ll learn
- How to analyze data and perform simple data visualizations using ProcessingJS
- Understand and apply introductory programming concepts such as sequencing, iteration, and selection
- Equip you to study computer science or other programming languages
A Course Sponsored By AdelaideX
Free online courses from the University of Adelaide
The University of Adelaide is one of Australia’s leading research-intensive universities and is consistently ranked among the top 1% of universities in the world. Established in 1874, it is Australia’s third oldest university and has a strong reputation for excellence in research and teaching. The University is known for its dedication to the discovery of new knowledge and preparing the educated leaders of tomorrow. It has over 100 Rhodes Scholars, including Australia’s first Indigenous winner, and five Nobel Laureates among its alumni community. Currently, there are more than 25,000 students from over 90 countries.
Meet your Instructors
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’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. In computer systems, she is interested in understanding the effects that interactions between system components have on the behavior of the system as a whole.
Nick loves teaching and does most of his education research into the areas of motivation, time management and effective teaching delivery. He also looks at the role of social networks in forming strong communities for learning, to identify positive and supportive behavior that will lead to better outcomes for everyone.
- Lectures 10
- Quizzes 0
- Duration 100 hours
- Skill level All levels
- Language English
- Students 40926
- Certificate No
- Assessments Yes
Section 1: Creative code - Computational thinking
Section 2: Building blocks - Breaking it down and building it up
Section 3: Repetition - Creating and recognising patterns
Section 4: Choice - Which path to follow
Section 5: Repetition - Going further
Section 6: Testing and Debugging
Section 7: Arranging our data
Section 8: Functions - Reusable code
Section 9: Data Science in practice
Section 10: Where next?