ITAO2009
Data Analytics for Business
Academic Year 2024-2025
Module Description
Increasingly, organisations are relying on data analysis to interpret corporate information when making business decisions. Indeed, timely and appropriate use of data analytics is considered a crucial component among organisations that are committed to achieving business success. This module explores basic methods and concepts in data analytics for analysing and interpreting data. The module takes both a theoretical and practical approach to the use of data analytics in practice.
A highlight of the module is the use of KNIME software to analyse data for decision making and evaluative purposes. Students who successfully complete the module will be able to signal to potential employers that they have the theoretical, practical plus industry-standard software skills to compete.
Module Content
The module is taught in two types of lectures, class lectures and computer sessions. The first type (i.e., class lectures) will take place in class where the theoretical background on data analytics will be covered. It takes a holistic approach to understanding data analytics - the maturity of data analytics in industry; uncover where, when, and how it is being used; and identify whether or not its use results in greater effectiveness, efficiency and performance returns.
Indicative class lectures contents include:
• Small and Big Data - Case study (e.g., Netflix, Facebook etc.)
• Descriptive and inferential analytics
• Applications of data analytics in business
• The concept of confidence intervals and hypothesis testing
• Simple and multiple linear regression analysis
Computer sessions focus on data analysis, covering both descriptive and predictive analytics, emphasising on methods, such as correlation analysis and regression analysis. Computer sessions will be taught through instructor led computer workshops using KNIME software.
Indicative computer sessions contents include:
• Introduction to KNIME
• Descriptive analytics and visualisation
• Correlation analysis
• Performance of linear regressions
Learning Outcomes
On successful completion of this module students will be able to:
Subject Specific
1. Demonstrate an understanding of the role and impact of data analytics in dealing with a variety of business problems.
2. Demonstrate an ability to summarise, analyse and present data effectively to others.
3. Employ statistical techniques to draw well founded inferences from quantitative data.
4. Demonstrate an ability to use appropriate software.
5. Demonstrate an ability to understand the scope and limitations of quantitative methods.
6. Identify sources of published analytics, understand their context and report on their wider relevance.
7. Interpret and disseminate research results and findings.
General
1. Apply critical analytical skills and problem-solving skills to a variety of different situations.
2. Synthesize, analyse, interpret and critically evaluate information from a variety of different sources.
3. Work effectively as an individual and as part of a team.
Course Schedule
This module is taught in class lectures and computer sessions, and it will include group work, lectures, and computer practical. Classes will be a combination of the traditional lecture, discussion, and interactive student-led sessions. It is imperative that students undertake preparatory work before coming to each class. The itinerary for each session is provided in Table 1 of this document. Computer sessions will focus on the practical implementation of marketing analytics using KNIME software.
TABLE 1: ITAO2009 DATA ANALYTICS FOR BUSINESS SCHEDULE 2024/25
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Lecture 1
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Topic / Activity
• Introductions
• Discussing the module’s Outline and Assessments
• Explain how the module’s assessments are meticulously aligned with the module’s learning outcomes
• A brief introduction to Data Analytics for Business
Main Textbook
• Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 1).
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Lecture 2
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Topic / Activity
• Introduction to Business Analytics
Main Textbook
• Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 1).
• Koole, G. (2019). An Introduction to Business Analytics. Lulu. com. (Chapter 1).
Other Suggested Reading
• Chahal, H., Jyoti, J., & Wirtz, J. (2019). Business analytics: Concept and applications. Understanding the Role of Business Analytics: Some
Applications, 1-8.
• Power, D. J., Heavin, C., McDermott, J., & Daly, M. (2018). Defining business analytics: an empirical approach. Journal of Business Analytics, 1(1), 40-53.
• Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12.
• Schläfke, M., Silvi, R., & Möller, K. (2012). A framework for business analytics in performance management. International Journal of Productivity and
Performance Management, 62(1), 110-122.
• Yin, J., & Fernandez, V. (2020). A systematic review on business analytics. Journal of Industrial Engineering and Management (JIEM), 13(2), 283-295.
• Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of
business analytics on innovation. European Journal of Operational Research, 281(3), 673-686.
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Computer
Session
1
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Topic / Activity
• Introduction to Knime
Main Textbook
• Knime training manual and lecture slides issued by course instructors.
Other Suggested Reading
• Acito, F. (2023). Introduction to KNIME. In Predictive Analytics with KNIME: Analytics for Citizen Data Scientists (pp. 21-52). Cham: Springer Nature
Switzerland.
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Lecture 3
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Topic / Activity
• CRISP-DM and Data (Small and Big)
Main Textbook
• Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 2 & 3).
• Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media. (Chapter 1 & 2).
Other Suggested Reading
• Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181,
526-534.
• Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Kull, M., Lachiche, N., ... & Flach, P. (2019). CRISP-DM twenty years later:
From data mining processes to data science trajectories. IEEE transactions on knowledge and data engineering, 33(8), 3048-3061.
• Kitchin, R., & Lauriault, T. P. (2015). Small data in the era of big data. GeoJournal, 80, 463-475.
• Hand, D. J., Daly, F., McConway, K., Lunn, D., & Ostrowski, E. (1993). A handbook of small data sets. cRc Press.
• Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In 2013
international conference on collaboration technologies and systems (CTS)
(pp. 42-47). IEEE.
• Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National science review, 1(2), 293-314.
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Computer
Session
2
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Topic / Activity
• Visualisation in Knime
Main Textbook
• Knime training manual and lecture slides issued by course instructors.
Other Suggested Reading
• https://www.knime.com/blog/visual-data-exploration-in-three-steps
• https://www.knime.com/blog/data-visualizaton-101-five-easy-plots-to-get-to- know-your-data
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