DATA420-25S1 (D) Semester One 2025 (Distance)

Scalable Data Science

15 points

Details:
Start Date: Monday, 17 February 2025
End Date: Sunday, 22 June 2025
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 2 March 2025
  • Without academic penalty (including no fee refund): Sunday, 11 May 2025

Description

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 both university and cloud based clusters 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 computing resources 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.

Learning Outcomes

  • Concrete learning outcomes will include:

  • Demonstrate your knowledge of the principles of distributed computing by using open source frameworks to perform distributed data processing.
  • Perform distributed data analysis on data using Spark, including visualisation and reporting.
  • Connect concepts of statistical modeling and machine learning and identify how these can be applied to scalable data science problems.
  • Develop practical solutions for real world data science problems that require data analysis, statistical modeling, and machine learning.

Prerequisites

Subject to approval of the Head of Department of Mathematics and Statistics.

Course Coordinator

James Williams

Textbooks / Resources

No textbook required.

Course links

LEARN
Postgraduate

Additional Course Outline Information

Assessment and grading system

• Labs / exercises 10%
• Quizzes 10%
• Assignment 1 40%
• Assignment 2 40%

Indicative Fees

Domestic fee $1,176.00

* 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 .

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