The University of Adelaide is offering free online course on Computational Thinking and Big Data. In this course, part of the Big Data MicroMasters program, students will learn how to apply computational thinking in data science.
Applicants will learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets. The course will start on September 8, 2017.
Course At A Glance
Length: 10 weeks
Effort: 8-10 hours pw
Subject: Computer Science
Institution: University of Adelaide and edx
Certificate Available: Yes, Add a Verified Certificate for $150
Session: Course Starts on September 8, 2017
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.
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.
Students will also learn about data representation and analysis and the processes of cleaning, presenting, and visualizing data. Students will develop skills in data-driven problem design and algorithms for big data.
You will use tools such as R, MOA and data processing libraries in associated language environments.
Why Take This Course?
The course will also explain mathematical representations, probabilistic and statistical models, dimension reduction and Bayesian models.
- Understand and apply advanced core computational thinking concepts to large-scale data sets
- Use industry-level tools for data preparation and visualisation, such as R, MOA and Hadoop
- 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
Dr. Brad Alexander
Brad is a lecturer and Director of Teaching in Computer Science at the University of Adelaide. His teaching interests are algorithms, algorithmic problem solving, and research skills. Brad has a strong interest in teaching practices that encourage students to refine their own ideas and skills and to investigate problems that are relevant to their own interests.
Dr. Lewis Mitchell
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.
Dr. Simon Tuke
Simon is a lecturer in statistics in the School of Mathematical Sciences at the University of Adelaide. His research focuses on statistical modelling 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.
How To Join This Course
- Go to the course website link
- Create an edX account to SignUp
- Choose “Register Now” to get started.
- EdX offers honor code certificates of achievement, verified certificates of achievement, and XSeries certificates of achievement. Currently, verified certificates are only available in some courses.
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