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Computational Thinking and Big Data
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CERTIFICATE

Computational Thinking and Big Data

This course is part of Big Data.

Course Cost

₹ 21,292

Beginner

Skill Level

10 Weeks

Self-paced lessons

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.

Gavin Meredith

Gavin Meredith

Ian Knight

Ian Knight

English

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English

Gavin Meredith

Gavin Meredith

Ian Knight

Ian Knight

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

  • Design and implement efficient data processing algorithms

Skills you'll gain

Computational Thinking
Big Data Analysis
R Programming
Java
Data Visualization
Statistical Modeling
Data Processing
Algorithm Design
Pattern Recognition
Dimension Reduction

This course includes:

PreRecorded video

Graded assignments, Exams

Access on Mobile, Tablet, Desktop

Limited Access access

Shareable certificate

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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

Summarising data

Module 5

Introduction to Java

Module 6

Graphs

Module 7

Probability

Module 8

Hashing

Module 9

Bringing it all together

Module 10

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Big Data

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Gavin Meredith

2

Courses

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.
Ian Knight

2

Courses

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.
Markus Wagner

1

Course

Markus Wagner currently serves as Associate Professor at Monash University's Department of Data Science and AI, following his impactful tenure at the University of Adelaide. After completing his PhD in bio-inspired algorithms at the University of Adelaide and Max Planck Institute for Informatics, he has built an extraordinary career combining theoretical and applied optimization research. His work spans multiple areas including mathematical runtime analysis, theory-guided algorithm design, and applications in software engineering and renewable energy, with over 200 publications garnering 4,700+ citations. As the first Sustainability Officer for ACM SIGEVO and founder of the IEEE CIS Task Force on Computational Intelligence in the Energy Domain, he champions sustainable computing practices while maintaining active industry collaborations with companies like Google and Facebook. His achievements include four best paper awards, a HUMIES Gold Award, and the 2024 Biannual German IT Security Award. Through his leadership roles, including serving as General Chair of GECCO'22 and co-founding the field of Data-Driven Search-Based Software Engineering, he continues to advance both theoretical understanding and practical applications of optimization algorithms while maintaining his passion for sustainability since his high-school days.
Simon Tuke

2

Courses

Simon (Jono) Tuke serves as Senior Lecturer in Statistics at the University of Adelaide's School of Mathematical Sciences, where he has established himself as an expert in statistical bioinformatics and network analysis. His unique career path began as a veterinarian before transitioning to mathematics and statistics, ultimately earning his PhD. His research spans multiple disciplines, with significant contributions to population genetics, medical statistics, and natural language processing. His collaborative work has led to groundbreaking discoveries, including research that demonstrated Aboriginal Australians' 50,000-year connection to country through genetic data. As an applied statistician, he has contributed to diverse projects ranging from analyzing ancient DNA to developing methods for social media trend analysis. His expertise extends to biostatistics and bioinformatics, where he develops statistical methods to model random networks and assess model fit. Through his work at the Adelaide Data Science Centre and as part of the ARC Centre of Excellence for Mathematical and Statistical Frontiers, he continues to bridge the gap between theoretical statistics and practical applications, from predicting Maori arrival patterns in New Zealand to analyzing post-operative behavior in cattle.
Lewis Mitchell

2

Courses

Lewis Mitchell serves as Professor of Data Science at the University of Adelaide, where he has progressed from Lecturer to Professor since joining in 2014. After earning his PhD from the University of Sydney in 2012, he has established himself as a leading expert in computational social science and mathematical modeling of information flow across social networks. His research combines applied mathematics with data science to understand how information and misinformation spread online, developing tools for monitoring population-level trends and predicting real-world events. His work has attracted significant funding, including an ARC Discovery Project on mathematical modeling of information flow and an NHMRC Ideas Grant for improving medical device safety. As Chief Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers, he has contributed to groundbreaking research in social media analysis, disease outbreak prediction, and civil unrest forecasting. His innovative work includes developing open online tools for social media trend analysis and creating predictive models using Bayesian network approaches. Beyond research, Mitchell actively engages in science communication through media interviews, outreach events, and mentorship programs, while supervising numerous PhD students and postdoctoral researchers in areas ranging from healthcare analytics to illegal wildlife trade analysis.
Computational Thinking and Big Data

Beginner

Skill Level

10 Weeks

Self-paced lessons

Course Cost

₹ 21,292

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.