Learn how to apply selected statistical and machine learning techniques and tools to analyse big data.
- 3 weeks long
- 2 hours per week
- Learn for FREE, Ugpradable
- Taught by: Kerrie Mengersen, Professor at QUT and a Deputy Director of ACEMS.
- View Course Syllabus
Online Course Details:
Many people have big data but only some people know what to do with it. Why? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means we need new solutions for analysing data.
This course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. We also expose you to three software packages so you can develop your coding skills by completing practical exercises.
What topics will you cover?
- Introduction to the relationship between statistical inference and machine learning
- The application of methods from these areas to real world projects
- An overview of the most popular methods currently used in these fields.
- Machine learning methods used to undertake prediction and analysis of a given data set.
- Specific methods such as neural networks, decision trees, principal component analysis and clustering.
- The practical application of modern analysis tools such as R Studio and H2o.
- Identify big data application areas
- Explore big data frameworks
- Model and analyse data by applying selected techniques
- Demonstrate an integrated approach to big data
- Develop an awareness of how to participate effectively in a team working with big data experts
Who is the course for?
You will enjoy this course most and benefit from the learning experience if you have a basic understanding of statistics and mathematics at a university undergraduate level.