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MODULE DESCRIPTOR
Module Title
Introduction to Big Data and Data Science
Reference CMM727 Version 3
Created February 2024 SCQF Level SCQF 11
Approved April 2017 SCQF Points 15
Amended April 2024 ECTS Points 7.5

Aims of Module
To provide a general overview of how big data and data science related technologies are used to change business thinking. To provide a deeper understanding of how these technologies can be used to enhance marketing and support decision making.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Construct a comparative study of state-of-the-art Big Data and Data Science concepts and Technologies.
2 Make informed judgements on technology choices to solve real-world Big Data and Data Science problems.
3 Prepare a plan to conceptualise and integrate Big Data and Data Science technologies to solve a given business problem.
4 Produce conclusions, insights and recommendations to a wider audience by crafting project solutions at different levels of detail.

Indicative Module Content
Big Data: types of data and characteristics of big data. Overview of Big Data technologies: Hadoop eco-system, NoSQL Databases. Overview of big data architectures: parallel processing architectures, cloud computing and map reduce concepts. Data Science and Data Science case studies: Computer Vision, Natural Language Understanding, Social Network Analysis, Bio Engineering, Intelligent Sensing and Internet of Things. Data Science and Business Strategy: data-driven business, acquiring and sustaining competitive advantage via data science, curation of data science capability.

Module Delivery
This is a lecture based course enhanced through interactive tutorials, practical sessions, presentations, directed reading and case studies covering a sample of different business domains relevant to the local context.

Indicative Student Workload Full Time Part Time
Contact Hours N/A 48
Non-Contact Hours N/A 102
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL N/A 150
Actual Placement hours for professional, statutory or regulatory body    

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
Description: Coursework submission worth 100% of total module assessment.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in Component 1.
B The student needs to achieve a B in Component 1.
C The student needs to achieve a C in Component 1.
D The student needs to achieve a D in Component 1.
E The student needs to achieve an E in Component 1.
F The student needs to achieve an F in Component 1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 PROVOST, F. and FAWCETT, T., 2013. Data Science for Business: What You Need to Know About Data Mining and Data Analytic Thinking. O'Reilly Media.
2 O'NEIL C. and SCHUTT R., 2015. Doing Data Science: Straight Talk from the Frontline. O'Reilly Media.
3 FOREMAN J.W., 2013. Data Smart: Using Data Science to Transform Information into Insight 1st Edition. Wiley.
4 EMC EDUCATION SERVICES, 2015. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. 1st ed. Wiley.

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