25930 Data Analytics for Entrepreneurs
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Subject handbook information prior to 2017 is available in the Archives.
Credit points: 3 cp
Subject level:
Postgraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Description
This subject, partially delivered online, develops statistical knowledge and skills for entrepreneurs and innovators. The topics covered provide a basic understanding of statistical concepts to help entrepreneurs to better understand, analyse and interpret data. Students learn how to understand data using descriptive statistics, monitor processes using control charts and to explore relationships between variables using regression analysis. These statistical methodologies are useful for developing data driven start-ups that make evidence-based decisions.
Subject learning objectives (SLOs)
| 1. | Apply relevant quantitative analytical techniques to qualify, support and evaluate business data |
|---|---|
| 2. | Apply statistical methods to support business decisions making |
| 3. | Interpret and communicate results of quantitative data analysis |
| 4. | Use software to establish basic statistical analysis |
Contribution to the development of graduate attributes
This subject is designed to provide students with the basic understanding of statistical techniques and tools and how they are applied to analysing and evaluating data. The subject helps students understand the relationship between relevant business factors, develop models and analyse data related to early stage enterprises.
This subject contributes to the development of the following graduate attributes:
- Attitudes and values
This subject also develops the following Program Learning Objective for the Master of Business Administration in Entrepreneurship:
- Research and synthesise data in order to identify and assess business opportunities (1.1)
- Use data and analytical tools to evaluate businesses (1.2)
Teaching and learning strategies
The subject is offered in a blended learning mode with a combination of face to face class room meetings and online interaction. Delivery of teaching and learning materials, quizzes, lectures, webinars and discussions are facilitated using online learning and communication tools and the UTS learning management system. The face to face classes help students understand the study and learning requirements and to reflect upon and revise the content.
Content (topics)
- Descriptive statistics
- Probability and statistical distributions
- Monitoring processes with p, c, and X-bar charts
- Hypothesis testing
- Linear regression
- Text Analytics
- Tree based analytics
- Model selection
Assessment
Assessment task 1: Quizzes (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 |
|---|---|
| Weight: | 50% |
Assessment task 2: Case Study (Individual)
| Objective(s): | This addresses subject learning objective(s): 3 and 4 |
|---|---|
| Weight: | 50% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Recommended texts
Black, Ken, John Asafu-Adjaye, Paul Burke, Nazim Khan, Gerard King, Nelson Perera, and Carl Sherwood. Australasian business statistics. 3rd Edition, John Wiley & Sons, 2013. ISBN 9780730310204
References
Other resources
Students are required to use online collaboration software including UTSOnline: http://online.uts.edu.au
