The University of Adelaide is free online course on Big Data Fundamentals. In this ten weeks course, applicants will learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms.
By the end of this course, you will have a better understanding of the various applications of big data methods in industry and research. The course will start on October 24, 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 October 24, 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.
About This Course
Organizations now have access to massive amounts of data and it’s influencing the way they operate. They are realizing in order to be successful they must leverage their data to make effective business decisions.
In this course, part of the Big Data MicroMasters program, you will learn how big data is driving organisational change and the key challenges organizations face when trying to analyse massive data sets.
Why Take This Course?
Students will learn fundamental techniques, such as data mining and stream processing. You will also learn how to design and implement PageRank algorithms using MapReduce, a programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. Students will learn how big data has improved web search and how online advertising systems work.
By the end of this course, you will have a better understanding of the various applications of big data methods in industry and research.
- Knowledge and application of MapReduce
- Understanding the rate of occurrences of events in big data
- How to design algorithms for stream processing and counting of frequent elements in Big Data
- Understand and design PageRank algorithms
- Understand underlying random walk algorithms
Dr. Frank Neumann
Frank is a professor in the School of Computer Science and in his work he considers algorithmic approaches in particular for combined and multi-objective optimising problems. He focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of renewable energy, logistics and sport.
How To Join This Course
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