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This course will introduce students to core topics in scalable data science based on distributed-computing techniques. This is a very practical course, with students learning by experimenting on a computer cluster.
This course will introduce students to topics in scalable data science based on distributed computing techniques. We will look at principles of distributed computing, topics in statistical modelling, and applications of distributed machine learning to find scalable solutions for real problems. This is a very practical course, and students will learn by experimenting on the university data science cluster with large datasets. All computing resources are available online using remote desktop and there will be interactive computer labs online for students who are not on campus. Enrolled students who take this course will have ongoing access to the data science cluster to pursue additional projects. The intent of the course is to provide an environment that is very similar to what you will experience in a data science position in industry. You will need to understand the theory underlying common solutions to data science problems and how to implement these using a distributed computing framework such as Spark.
Concrete learning outcomes will include the following:Demonstrate knowledge of the need and use cases for distributed computing and the early development of Hadoop, MapReduce, and HDFS.Demonstrate knowledge of the MapReduce programming model.Demonstrate knowledge of statistical modeling and machine learning algorithms and how they can be applied to scalable data science problems.Demonstrate knowledge of applications involving scalable data science in general.Implement basic data processing using the MapReduce programming model.Implement basic data analysis in Spark using the Spark Python API and the Spark SQL API.Develop data analysis and modeling pipelines using Spark.Develop practical solutions for real world data science problems that require data analysis, statistical modeling, and machine learning.
Subject to approval of the Head of Department of Mathematics and Statistics.
Students must attend one activity from each section.
James Williams
No textbook required.
School of Mathematics and Statistics Postgraduate Handbook General information for students Library portal LEARN
Domestic fee $1,110.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Mathematics and Statistics .