INFO204 Introduction to Data Science

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.

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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

A/Prof. 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

1. Define data science as a field that integrates concepts from information technology and statistical/machine learning and combines with organisational context
2. Describe the basic strengths and weaknesses of decision making based upon data science methodologies
3. Explain the ethical and behavioural impacts and opportunities for innovation that data science methods can introduce within small and large businesses
4. Perform basic data engineering for various data domains
5. Apply basic data-driven modelling techniques to solve classification and regression problems
6. Use appropriate visualisation and reporting techniques to convey knowledge acquired through data science processes.

^ Top of page

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.

^ Top of page

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:

1. Define data science as a field that integrates concepts from information technology and statistical/machine learning and combines with organisational context
2. Describe the basic strengths and weaknesses of decision making based upon data science methodologies
3. Explain the ethical and behavioural impacts and opportunities for innovation that data science methods can introduce within small and large businesses
4. Perform basic data engineering for various data domains
5. Apply basic data-driven modelling techniques to solve classification and regression problems
6. Use appropriate visualisation and reporting techniques to convey knowledge acquired through data science processes.

^ Top of page