Due to COVID-19 restrictions, a selection of on-campus papers will be made available via distance and online learning for eligible students.
Find out which papers are available and how to apply on our COVID-19 website
Introductory theory and methods for performing data-driven decision making. Measuring data quality, integration of data sources, learning algorithms, enabling behavioural change through data science, and ethical considerations.
The importance of data science, and data analytic thinking, is becoming increasingly
important in modern business environments. Businesses are relying upon data-driven
decision making at an ever-increasing rate, so individuals with a mind towards data
science thinking have a competitive advantage in industry. The role of data scientist
has been referred to as "The Sexiest Job of the 21st Century", and there are currently
many vacancies both in New Zealand and abroad seeking candidates with data science
In addition to being a core topic of Information Science, the concepts discussed in this paper would be of interest to a wide range of specialties, including: computer science, marketing, management, statistics and finance.
|Paper title||Introduction to Data Science|
|Teaching period||Semester 2 (On campus)|
|Domestic Tuition Fees (NZD)||$1,110.75|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- 36 points from BSNS 106, BSNS 112, COMP 101, COMP 120, COMP 150, COMP 151, COMP 160, COMP 161, COMP 162, INFO 130, STAT 110, or STAT 115
- INFO 213
- Schedule C
- Arts and Music, Commerce, Science
- Teaching staff
A/Prof. Jeremiah Deng email@example.com
Dr. Brendon Woodford firstname.lastname@example.org
- Paper Structure
- Two 1-hour lectures per week
- One 1-hour tutorial per week
- One 2-hour lab per week
Andreas C. Mueller and Sarah Guido: Introduction to Machine Learning with Python, O’Reilly, 2017
Recommended Reading - José Unpingco: Python for Probability, Statistics, and Machine Learning, Springer, 2016
(Both these texts are available online).
- Course outline
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Scholarship, Critical thinking, Ethics, Communication,
View more information about Otago's graduate attributes.
- Learning Outcomes
Students who successfully complete the paper should be able to
- Define data science as a field that integrates concepts from information technology and statistical/machine learning and combines with organisational context
- Describe the basic strengths and weaknesses of decision making based upon data science methodologies
- Explain the ethical and behavioural impacts and opportunities for innovation that data science methods can introduce within small and large businesses
- Perform basic data engineering for various data domains
- Apply basic data-driven modelling techniques to solve classification and regression problems
- Use appropriate visualisation and reporting techniques to convey knowledge acquired through data science processes.