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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.

Paper title Introduction to Data Science
Paper code INFO204
Subject Information Science
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $1,059.15
International Tuition Fees (NZD) $4,627.65

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Prerequisite
COMP 101 or BSNS 106
Restriction
INFO 213
Recommended Preparation
BSNS 112 or one STAT paper
Schedule C
Arts and Music, Commerce, Science
Contact
infoscience@otago.ac.nz
Teaching staff

Jeremiah Deng

Brendon Woodford

 

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

(We shall look into having both these texts 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.

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Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Computer Lab

Stream Days Times Weeks
Attend one stream from
A1 Wednesday 11:00-12:50 28-34, 36-40
A2 Wednesday 13:00-14:50 28-34, 36-40
A3 Thursday 10:00-11:50 28-34, 36-40
A4 Friday 11:00-12:50 28-34, 36-40

Lecture

Stream Days Times Weeks
Attend
L1 Monday 14:00-14:50 28-34, 36-41
Tuesday 14:00-14:50 28-34, 36-41

Practical

Stream Days Times Weeks
Attend one stream from
P1 Friday 17:00-18:50 36
P2 Friday 19:00-20:50 36

Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Thursday 12:00-12:50 28-34, 36-41
T2 Thursday 13:00-13:50 28-34, 36-41

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.

Paper title Introduction to Data Science
Paper code INFO204
Subject Information Science
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees Tuition Fees for 2020 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
COMP 101 or BSNS 106
Restriction
INFO 213
Recommended Preparation
BSNS 112 or one STAT paper
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

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Computer Lab

Stream Days Times Weeks
Attend one stream from
A1 Wednesday 11:00-12:50 28-34, 36-40
A2 Wednesday 13:00-14:50 28-34, 36-40
A3 Thursday 10:00-11:50 28-34, 36-40
A4 Friday 11:00-12:50 28-34, 36-40

Lecture

Stream Days Times Weeks
Attend
L1 Monday 14:00-14:50 28-34, 36-41
Tuesday 14:00-14:50 28-34, 36-41

Practical

Stream Days Times Weeks
Attend one stream from
P1 Friday 17:00-18:50 36
P2 Friday 19:00-20:50 36

Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Thursday 12:00-12:50 28-34, 36-41
T2 Thursday 13:00-13:50 28-34, 36-41