LII:Big Data Analytics
Title: Big Data Analytics
Author for citation: Mitchell, Tuke, and Suter
License for content: Unknown
Publication date: 2024
This is a University of Adelaide course that is released on the edX platform. The ten-week course is designed for students to "learn key technologies and techniques, including R and Apache Spark, to analyze large-scale data sets to uncover valuable business information." 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. Access to the class began in May 2024, and the free audit track is open until July 22.
The edX course description:
"Gain essential skills in today’s digital age to store, process and analyze data to inform business decisions.
In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Apache Spark and R.
Topics covered in this course include:
- cloud-based big data analysis;
- predictive analytics, including probabilistic and statistical models;
- application of large-scale data analysis; and
- analysis of problem space and data needs.
By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative."
"What you'll learn:
- How to develop algorithms for the statistical analysis of big data;
- Knowledge of big data applications;
- How to use fundamental principles used in predictive analytics;
- Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems."
About the authors
Three instructors (Mitchell, Tuke, and Suter) 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 introduces simple linear regression. Weeks two, three, and four delve into data modelling and classification. Week five compels users to use what they've learned about modeling and classification to solve prediction problems. Weeks six and seven get into sparklyr and its various applications. Week eight gets into deep learning principles, while week nine looks at how to effectively apply and scale deep learning to various applications. The final week consolidates all the prior lessons and discusses the methodologies' strengths and weaknesses.
The course
: The course can be found on the edX site, under the Computer Science category. Access to the class began in May 2024, and the free audit track is open until July 22.
