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.
| Paper title | Advanced Data Science |
|---|---|
| Paper code | INFO304 |
| 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 |
- Prerequisite
- INFO 204
- Restriction
- INFO 324
- Schedule C
- Arts and Music, Commerce, Science
- Contact
- Teaching staff
- Associate Professor Peter Whigham
- Paper Structure
The paper is presented as a series of lectures (2 per week), a tutorial (1 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 both assignments and a final exam.
- 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
- 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.
Timetable
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.
| Paper title | Advanced Data Science |
|---|---|
| Paper code | INFO304 |
| 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. |
- 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 (2 per week), a tutorial (1 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 both assignments and a final exam.
- 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
- 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.
