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MODULE DESCRIPTOR
Module Title
Introduction to Business Analytics
Reference CM1706 Version 3
Created January 2023 SCQF Level SCQF 7
Approved June 2019 SCQF Points 30
Amended June 2023 ECTS Points 15

Aims of Module
To introduce the data analytics lifecycle (clean, transform, analyse and visualise data), and provide an understanding of the statistical techniques involved in business analytics.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Develop awareness of the organisation's data strategy and governance.
2 Use different methods for preparing data for analysis.
3 Apply statistical techniques to a variety of datasets.
4 Develop a simple data science solution and communicate results through appropriate visualisation.
5 Develop awareness of the professional, ethical and legal issues within data analysis.

Indicative Module Content
Enterprise data strategy, governance and stewardship. Data preparation and cleaning methods. Data exploration, summarisation, transformation and visualisation techniques. Introduction to and use of Python libraries to process and analyse a range of data types. Statistical techniques for data analysis: hypothesis testing; standard deviation; regression; correlation; sample size determination; experimental design. Professional, ethical and legal issues within data analysis; data bias.

Module Delivery
The module is delivered in Blended Learning mode using structured online learning materials/activities and directed study, facilitated by regular online tutor support. Workplace Mentor support and work-based learning activities will allow students to contextualise this learning to their own workplace. Face-to-face engagement occurs through annual induction sessions, employer work-site visits, and modular on-campus workshops.

Indicative Student Workload Full Time Part Time
Contact Hours 30 N/A
Non-Contact Hours 30 N/A
Placement/Work-Based Learning Experience [Notional] Hours 240 N/A
TOTAL 300 N/A
Actual Placement hours for professional, statutory or regulatory body 240  

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4, 5
Description: This coursework will consist of a practical data analytics exercise, and a discussion on business data strategies and professional, legal and ethical issues within the workplace.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The module is assessed on a pass/unsuccessful basis. The Module Grade is based on performance in Component 1.
Module Grade Minimum Requirements to achieve Module Grade:
Pass Pass in Component 1.
Fail Fail in Component 1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None, in addition to course entry requirements.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 JAMES, G. et al., 2013. An introduction to statistical learning: with applications in R.New York, NY: Springer.
2 MCKINNEY, W., 2013. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly.
3 PROVOST, F. and FAWCETT, T., 2013. Data science for business. Beijing, China: O'Reilly.
4 LANE, D.M. et al., n.d. Online statistics education: an interactive multimedia course of study. [online]. Houston, TX: Rice University. Available from: http://onlinestatbook.com/ [Accessed 5 March 2019].
5 PADMANBHAN, T.R., 2016. Programming with Python. Singapore: Springer.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781