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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 thetheoretical. We will use R as the language of choice. Much of the material involving R and shinywill involve a degree of self learning especially in the early part of the course.
There is an emphasis on three main themes.1. Best statistical practiseWe 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.”2. Communication through visualisationWe 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.3. Problems typical of the “real” worldReal 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.
Subject to approval of the Head of Department of Mathematics and Statistics.
Students must attend one activity from each section.
Phil Davies
There is no prescribed textbook.
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 .