Overview
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 skills.
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.
About this paper
| Paper title | Introduction to Data Science |
|---|---|
| Subject | Information Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,141.35 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- 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
- Restriction
- INFO 213
- Schedule C
- Arts and Music, Commerce, Science
- Contact
- infoscience@otago.ac.nz
- Teaching staff
Associate Professor Jeremiah Deng jeremiah.deng@otago.ac.nz
Dr. Brendon Woodford brendon.woodford@otago.ac.nz
- Paper Structure
- Two 1-hour lectures per week
- One 1-hour tutorial per week
- One 2-hour lab per week
- Textbooks
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
- http://www.otago.ac.nz/info-science/study/papers/INFO204.pdf
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Scholarship, Critical thinking, Ethics, Communication, Information literacy.
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.
Timetable
Overview
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 skills.
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.
About this paper
| Paper title | Introduction to Data Science |
|---|---|
| Subject | Information Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2024 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- 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
- Restriction
- INFO 213
- Schedule C
- Arts and Music, Commerce, Science
- Contact
- infoscience@otago.ac.nz
- Teaching staff
Associate Professor Grant Dick grant.dick@otago.ac.nz
- Paper Structure
- Two 1-hour lectures per week
- One 1-hour tutorial per week
- One 2-hour lab per week
- Textbooks
No required textbooks for this paper. A set of recommended readings and other useful resources will be provided at the start of the paper.
- Course outline
- http://www.otago.ac.nz/info-science/study/papers/INFO204.pdf
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Scholarship, Critical thinking, Ethics, Communication, Information literacy.
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.