LII:Big Data Fundamentals

Title: Big Data Fundamentals

Author for citation: Neumann et al.

License for content: Unknown

Publication date: 2021

This is a University of Adelaide course that is released on the edX platform. The ten-week course is designed to provide greater "understanding of the various applications of big data methods in industry and research." The course is free to take, with a Verified Certificate of completion available for $199. This course is also part of Adelaide's Big Data MicroMasters program. The course requires on average eight to ten hours a week of effort.

The edX course description:

"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.

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."

"What you'll learn:

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"

About the authors

Four instructors are affiliated with this course in some fashion. To learn more about each instructor, go to the edX course page and click on the name of each instructor.

General layout and contents of the course

The pre-enrollment syllabus outlines the course over the ten-week period. The first week provides an introduction to the course and to big data. Weeks two and three delve into big data as it relates to the web and social media. Week four takes a closer look at the web, specifically Google search and PageRank, while week five addresses how MapReduce can compute PageRank, among other topics. Week six and seven get into similar and frequent data sets, respectively. Week eight gets into using tools to process recommendations, while week nine looks at Google's AdWords to segue into online matching algorithms. The final week discusses mining and filtering big data streams.