Computational Thinking and Big Data
Learn to analyze large datasets using R and Java while mastering computational methods for data processing, visualization and statistical analysis.
Learn to analyze large datasets using R and Java while mastering computational methods for data processing, visualization and statistical analysis.
This comprehensive course, part of the Big Data MicroMasters program, teaches essential computational thinking skills for data science. Students learn core concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking. The curriculum covers data representation, cleaning, visualization, and analysis using industry-standard tools like R and Java. Topics include mathematical representations, statistical models, dimension reduction, and Bayesian models. Through practical applications, students develop skills in data-driven problem design and big data algorithms, preparing them for real-world data science challenges.
Instructors:
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What you'll learn
Apply advanced computational thinking concepts to large-scale datasets
Master data preparation and visualization using R and Java
Implement mathematical and statistical techniques for data analysis
Develop skills in dimension reduction and statistical modeling
Create effective data visualizations and transformations
Understand and apply probabilistic models to big data
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 focuses on applying computational thinking to data science, covering both theoretical concepts and practical implementation. Students learn data representation, analysis, and visualization using R and Java. The curriculum progresses from basic data manipulation to advanced topics like dimension reduction and Bayesian models. Key areas include data cleaning, statistical analysis, algorithm design, and big data processing. The course emphasizes hands-on practice with real-world datasets and industry-standard tools.
Data in R
Module 1
Visualising relationships
Module 2
Manipulating and joining data
Module 3
Transforming data and dimension reduction
Module 4
Introduction to Java
Module 6
Graphs
Module 7
Probability
Module 8
Hashing
Module 9
Bringing it all together
Module 10
Fee Structure
Instructors
2 Courses
A Distinguished Expert in Computer Science Education and Big Data Analytics
Gavin Meredith serves as Research Associate in the School of Computer Science at the University of Adelaide, where he has established himself as a key contributor to the Big Data MicroMasters program. His teaching portfolio spans foundational computer science courses including Introduction to Programming, Object Oriented Programming, Algorithm Design and Data Structures, and Problem Solving and Software Development. As an instructor in the Big Data MicroMasters program, he helps students develop skills in data analysis, programming, and computational thinking. Through his role as both researcher and educator, he continues to advance computer science education while maintaining active involvement in course development and student mentoring in the School of Computer Science.
2 Courses
A Dedicated Educator Advancing Computer Systems and Network Education
Ian Knight serves as Lecturer in the School of Computer Science at the University of Adelaide, where he combines teaching excellence with systems development expertise. After transitioning from his role as Computer Science Tutor to Lecturer, he has established himself as a committed educator teaching Networks, Operating Systems, Web Computing, and Computer Systems. His contributions to education include developing innovative approaches to group work assessment and student collaboration tools, as evidenced by his successful implementation of the FeedbackFruits Group Member Evaluation tool in computer science courses. Through his role coordinating courses like COMP SCI 2000 (Computer Systems), he helps students understand complex concepts from basic hardware gates through to compilers and applications. His expertise spans network optimization and system design, while maintaining a focus on creating resources that enhance student understanding and learning outcomes. Beyond his primary teaching responsibilities, he actively participates in workshops and provides specialized tuition to support students' programming and networking skills development.
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Frequently asked questions
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