Overview
Computational methods for visualising, transforming, modelling and assessing data to allow informed decision making, prediction and knowledge construction.
Data Science and Data Analytics are a fundamental aspect of all business decision making. This paper will give the student a solid foundation in the concepts and methods for this field. Emphasis will be made on how this relates to business processes and the use of modelling and visualisation in supporting and delivering decision making.
About this paper
| Paper title | Advanced Data Science |
|---|---|
| Subject | Information Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,318.20 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- INFO 204
- Restriction
- INFO 324
- Schedule C
- Arts and Music, Commerce, Science
- Contact
- Teaching staff
- Paper Structure
The paper is presented as a series of lectures (two per week), a tutorial (one per week) and a laboratory session (one 2-hour session per week). This will allow the conceptual approaches to be presented and explored (lectures and tutorial) followed by hands-on experience using the R programming environment (lab). Assessment will be written and programming assignments.
- Textbooks
"An Introduction to Statistical Learning", by G.James, D. Witten, T. Hastie & R. Tibshirani (available online through the Library).
- Course outline
- View the most recent Course Outline
- Graduate Attributes Emphasised
- Lifelong learning, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
On successful completion of INFO 304, students should be able to:
- Identify the activities of prediction, optimisation, and adaptation that exist within a business process
- Assess the suitability of data sources with respect to the requirements of modelling and decision making
- Apply a range of suitable methods to perform prediction and modelling for a range of data types within the context of Data Science and Analytics
Overview
Computational methods for visualising, transforming, modelling and assessing data to allow informed decision making, prediction and knowledge construction.
Data Science and Data Analytics are fundamental aspects of all business decision-making. This paper will give the student a solid foundation in the concepts and methods of this field. Emphasis will be placed on how this relates to business processes and the use of modelling and visualisation in supporting and delivering decision-making.
About this paper
| Paper title | Advanced Data Science |
|---|---|
| Subject | Information Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- INFO 204
- Restriction
- INFO 324
- Schedule C
- Arts and Music, Commerce, Science
- Contact
- Teaching staff
- Paper Structure
The paper is presented as a series of lectures (two per week), a tutorial (one per week) and a laboratory session (one 2-hour session per week). This will allow the conceptual approaches to be presented and explored (lectures and tutorials), followed by hands-on experience using the R programming environment (lab). Assessment will be written and programming assignments.
- Textbooks
"An Introduction to Statistical Learning", by G.James, D. Witten, T. Hastie & R. Tibshirani (available online through the Library).
- Course outline
- View the most recent Course Outline.
- Graduate Attributes Emphasised
- Lifelong learning, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
On successful completion of INFO 304, students should be able to:
- Identify the activities of prediction, optimisation and adaptation that exist within a business process
- Assess the suitability of data sources with respect to the requirements of modelling and decision making
- Apply a range of suitable methods to perform prediction and modelling for a range of data types within the context of Data Science and Analytics