Programming for Data Science
Master fundamental programming concepts and data analysis techniques using Processing to solve real-world data science challenges.
Master fundamental programming concepts and data analysis techniques using Processing to solve real-world data science challenges.
This comprehensive course introduces students to programming fundamentals and data science concepts through practical, hands-on learning. Using Processing as the primary tool, students develop essential coding skills while learning data visualization, algorithm design, and computational thinking. The course covers key programming concepts including data selection, iteration, functional decomposition, and data abstraction. Through problem-based assignments working with real-world datasets, students gain practical experience in solving data science challenges and understanding big data applications.
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What you'll learn
Master fundamental programming concepts and syntax
Develop skills in data analysis and visualization using Processing
Learn algorithm design and computational thinking principles
Understand data abstraction and organization techniques
Gain practical experience with real-world datasets
Develop problem-solving skills for data science challenges
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 10 modules in this course
The course provides a foundation in programming and data science through hands-on learning with Processing. Students learn essential programming concepts including data visualization, algorithm design, and computational thinking. The curriculum progresses from basic coding principles to advanced topics like data abstraction and organization. Through practical assignments and real-world datasets, participants develop skills in solving data science problems and understanding big data applications.
Creative code - Computational thinking
Module 1
Building blocks - Breaking it down and building it up
Module 2
Repetition - Creating and recognising patterns
Module 3
Choice - Which path to follow
Module 4
Testing and Debugging
Module 6
Arranging our data
Module 7
Functions - Reusable code
Module 8
Data Science in practice
Module 9
Where next?
Module 10
Fee Structure
Instructors
2 Courses
A Distinguished Leader in Computer Science Education and Network Systems
Nick Falkner serves as Associate Professor in the School of Computer Science at the University of Adelaide, where he has established himself as an award-winning educator and researcher in computer science education and network systems. After earning his PhD, he has built an impressive career combining educational innovation with technical research in network topology, blockchain technologies, and the Internet of Things. His research spans multiple areas including information management, network security, privacy preservation, and educational strategies focused on student motivation and retention. As a recognized leader in learning and teaching, he pioneered new approaches to course delivery and assessment while conducting groundbreaking research into social networks' role in forming strong learning communities. His work has garnered over 4,500 citations, with significant contributions to the Internet Topology Zoo and puzzle-based learning approaches. Through his leadership in computer science education, he continues to develop innovative teaching methods that emphasize time management, motivation, and supportive learning environments while maintaining active research in network systems and information management.
1 Course
A Distinguished Leader in Computer Systems and Computer Science Education
Claudia Szabo serves as Associate Head of Learning and Teaching in the School of Computer and Mathematical Sciences at the University of Adelaide, where she has progressed from Associate Lecturer to Associate Professor since 2011. After earning her PhD in Computer Science from the National University of Singapore and earlier education from the University POLITEHNICA of Bucharest, she has built an impressive career combining systems research with educational innovation. Her research spans complex systems, cloud computing, and model-driven engineering, with over 3,700 citations reflecting her significant impact in the field. Her educational research focuses on curriculum design, cognitive load theories, and software engineering best practices, contributing to groundbreaking work in introductory programming education and gender equity in computer science. As Program Manager of Incubate Adelaide and through various leadership roles, she has championed innovative approaches to computer science education while maintaining active research in distributed systems and performance anomaly detection. Her work bridges theoretical computer systems research with practical educational applications, particularly in understanding component interactions and system-wide behavior.
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Frequently asked questions
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