DATA423-22S2 (C) Semester Two 2022

Data Science in Industry

15 points

Details:
Start Date: Monday, 18 July 2022
End Date: Sunday, 13 November 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 31 July 2022
  • Without academic penalty (including no fee refund): Sunday, 2 October 2022

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.

Timetable 2022

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 09:00 - 10:00 Rehua 101 Lectorial
18 Jul - 28 Aug
12 Sep - 23 Oct
Lecture B
Activity Day Time Location Weeks
01 Wednesday 10:00 - 11:00 Jack Erskine 340
18 Jul - 28 Aug
12 Sep - 23 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 14:00 - 15:00 Ernest Rutherford 464 Computer Lab
18 Jul - 28 Aug
12 Sep - 23 Oct

Course Coordinator / Lecturer

Nicholas Ward

Lecturer

Phil Davies

Textbooks / Resources

There is no prescribed textbook.

Indicative Fees

Domestic fee $1,051.00

* 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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