Computational Thinking and Big Data
Learn the core concepts of computational thinking and how to collect, clean, and consolidate large-scale datasets.
About this course
Computational thinking is an invaluable skill that can be used across every industry, as it allows you to formulate a problem and express a solution in such a way that a computer can effectively carry it out.
In this course, part of the Big Data MicroMasters program, you will learn how to apply computational thinking in data science. You will learn core computational thinking concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking.
You will also learn about data representation and analysis and the processes of cleaning, presenting, and visualizing data. You will develop skills in data-driven problem design and algorithms for big data.
The course will also explain mathematical representations, probabilistic and statistical models, dimension reduction, and Bayesian models.
You will use tools such as R and Java data processing libraries in associated language environments.
What you’ll learn
- Understand and apply advanced core computational thinking concepts to large-scale data sets
- Use industry-level tools for data preparation and visualization, such as R and Java
- Apply methods for data preparation to large data sets
- Understand mathematical and statistical techniques for attracting information from large data sets and illuminating relationships between data sets
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
Lewis is a lecturer in applied mathematics at the University of Adelaide. His research focusses on large-scale methods for extracting useful information from online social networks, and on statistical techniques for inference and prediction using these data. He works on building tools for real-time prediction of events like disease outbreaks, elections, and civil unrest.
Markus is a research-focused lecturer in the School of Computer Science at the University of Adelaide. He is passionate about teaching and research, working actively on a range of projects from foundational courses to complex software engineering. Markus’ research spans theoretical investigations that show the impact of design choices on optimization algorithms for quick problem solving (heuristic), theory-motivated algorithm engineering, and also the real-world applications of heuristic optimization.
Simon is a lecturer in statistics in the School of Mathematical Sciences at the University of Adelaide. His research focuses on statistical modeling of network data in particular methods to access a model’s fit. Simon is also an applied statistician with consulting experience in fields as diverse as predicting when the Maori’s arrived in New Zealand to estimating if cattle walk less after castration.
Gavin is a research associate and tutor in the School of Computer Science at the University of Adelaide. He has been developing courses for the Big Data MicroMasters program and tutoring students in several first and second-year computer science courses, including Introduction to Programming, Object-Oriented Programming, Algorithm Design, and Data Structures, and Problem Solving and Software Development.
- Lectures 10
- Quizzes 0
- Duration 100 hours
- Skill level All levels
- Language English
- Students 20186
- Certificate No
- Assessments Yes
Section 1: Data in R
Section 2: Visualising relationships
Section 3: Manipulating and joining data
Section 4: Transforming data and dimension reduction
Section 5: Summarising data
Section 6: Introduction to Java
Section 7: Graphs
Section 8: Probability
Section 9: Hashing
Section 10: Bringing it all together