DATA420-20S2 (C) Semester Two 2020

Scalable Data Science

15 points

Details:
Start Date: Monday, 13 July 2020
End Date: Sunday, 8 November 2020
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 24 July 2020
  • Without academic penalty (including no fee refund): Friday, 25 September 2020

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 new computational methods used in data science. We will look at methods for data from a range of contexts, including scalable methods used for big data and distributed computing. We will cover topics primarily in cloud computing, distributed
computing, and machine learning. This is a very hands on course, with students learning and experimenting on the School data science cluster. We will work in the computer lab, and students will have access to the cluster at any time to pursue additional projects.

The intent of the course is to provide an environment that is similar to what you will experience in a data science position in the real world, and to teach you to think carefully and to apply the appropriate tool for the task at hand.

Learning Outcomes

  • Concrete learning outcomes will include:
  • familiarity with map-reduce algorithms for processing big-data, including its robust clean-up via regular expressions
  • basic skills to extract, transform and load data into distributed file systems such as hadoop
  • working with structured data using dataframes and dynamic querying in sparkSQL on catalyst
  • basic applications of some of the standard learning algorithms in Spark's machine learning and distributed graph processing libraries
  • basic data science analytics pathways for the following common data types:
     - structured text data (logs generated by machines, tabular data from various open data sources)
     - geospatial data (and their integration with other types of data)
     - unstructured text data (a collection of text documents)
     - social media data

    Students will be encouraged to show-case their completed labs (which will have plenty of opportunities for extending the basic labs in creative ways even after the course is completed) by publishing them in public GitHub repositories in order to directly appeal to their potential employers.

Pre-requisites

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

Timetable 2020

Semester 1 teaching will be conducted online, please refer to LEARN for online options.

In the meantime, please stay home, stay safe and take care of yourself and others.

Kia kaha!

Course Coordinator

James Williams

Lecturer

James Williams

Textbooks / Resources

No textbook required.

Indicative Fees

Domestic fee $1,022.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

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