DATA423-23S2 (C) Semester Two 2023

Data Science in Industry

15 points

Details:
Start Date: Monday, 17 July 2023
End Date: Sunday, 12 November 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 30 July 2023
  • Without academic penalty (including no fee refund): Sunday, 1 October 2023

Description

In this course we will address core topics in the application of data science in industry.

This course is taught by a practising Data Scientist and attempts to teach real-life issues that will not be found in text books. The course will cover topics deemed central for a career in Data Science.

This course is heavily focused on the “applied” side of data science rather than the
theoretical. We will use R as the language of choice. Much of the material involving R and shiny
will involve a degree of self learning especially in the early part of the course.

Learning Outcomes

  • There is an emphasis on three main themes.

  • Best statistical practise
    We will progressively look at each stage of analysing data and producing a model of it.
    Best practise is mainly about doing the right things in the order right. In particular we look at the vexing issue of “data leakage.”

  • Communication through visualisation
    We will employ “Shiny” to visualise our data science. Shiny is built upon R and enables you to write an interactive web page employing dynamic visualisations. This is a great way to “sell” your work to your “clients” through a clear message that non-technical decision makers can relate to.

  • Problems typical of the “real” world
    Real life data is not like the numerous data sets that are available in the public domain. Real life data sets are messy; they have: ambiguity, missing data, useless variables, units, data-gaps, measurement uncertainty, correlation, near-zero variance, too many variables, unbalanced categories etc.

Prerequisites

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

Course Coordinator

Nicholas Ward

Lecturer

Phil Davies

Textbooks / Resources

There is no prescribed textbook.

Indicative Fees

Domestic fee $1,079.00

* 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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