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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)||$1,092.15|
|International Tuition Fees (NZD)||$5,004.75|
- COMP 101 or BSNS 106
- INFO 213
- Recommended Preparation
- BSNS 112 or one STAT paper
- 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.