Big Data Fundamentals
Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms.
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 organizational change and the key challenges organizations face when trying to analyze massive data sets.
You 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. You 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
Prerequisites
Candidates interested in pursuing the MicroMasters program in Big Data are advised to complete Programming for Data Science and Computational Thinking and Big Data before undertaking this course.
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
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 optimizing problems. He focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of renewable energy, logistics, and sport.
Vahid Roostapour
Vahid is a Ph.D. student in the School of Computer Science at the University of Adelaide. His research focuses on bio-inspired algorithms and problems with dynamically changing constraints. He is also actively involved with lecturing and tutoring within the Mining Big Data course at the University of Adelaide.
Aneta Neumann
Aneta is currently undertaking postgraduate research in the School of Computer Science at the University of Adelaide. Her main research interest is understanding the fundamental link between bio-inspired computation and digital art.
Wanru (Kelly) Gao
Kelly is a lecturer in the School of Computer Science at the University of Adelaide. She has been teaching several introductory computer science courses and some advanced courses about algorithms and evolutionary computation. Her research interests mainly focus on the area of combinatorial optimization, diversity maximization in evolutionary algorithms, and theoretical analysis of heuristic search methods.
Course Features
- Lectures 10
- Quizzes 0
- Duration 100 hours
- Skill level Intermediate
- Language English
- Students 38918
- Certificate No
- Assessments Yes
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Section 1: The basics of working with big data
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Section 2: Web and social networks
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Section 3: Clustering big data
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Section 4: Google web search
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Section 5: Parallel and distributed computing using MapReduce
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Section 6: Computing similar documents in big data
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Section 7: Products frequently bought together in stores
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Section 8: Movie and music recommendations
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Section 9: Google's AdWordsTM System
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Section 10: Mining rapidly arriving data streams
