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Certificate of Achievement
has completed the following course:
Big Data: Mathematical Modelling
Queensland University of Technology
In this course, the participant learnt that different applied problems can have common mathematical aims, and therefore can be addressed using similar mathematical methods. The participant applied these methods to a variety of prototypical case studies, including ranking websites, profiling leukaemia patients, and compressing selfies. The participant developed their analytic skills in practical exercises using self-contained datasets.
3 weeks, 3 hours per week
Ian Turner
Professor of Applied & Computational Mathematics
Queensland University of Technology
Steven Psaltis
Postdoctoral Research Fellow
Queensland University of Technology
Transcript
Learning outcomes
- 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
Syllabus
- Introduction to key mathematical concepts in big data analytics: eigenvalues and eigenvectors, principal component analysis (PCA), the graph Laplacian, and singular value decomposition (SVD)
- Application of eigenvalues and eigenvectors to investigate prototypical problems of ranking big data
- Application of the graph Laplacian to investigate prototypical problems of clustering big data
- Application of PCA and SVD to investigate prototypical problems of big data compression
Issued on 1st August 2017
The person named on this certificate has completed the activities in the transcript above. For more information about Certificates of Achievement and the effort required to become eligible, visit futurelearn.com/proof-of-learning/certificate-of-achievement.
This certificate represents proof of learning. It is not a formal qualification, degree, or part of a degree.