BI410 – Data Mining, Visualization and Decision Making
Master of Science in Business Intelligence and Data Analytics
Core Course
BI410 – Data Mining, Visualization and Decision Making
Course Unit Code: BI410
Type Of Unit: Core
Level of Course Unit: Second cycle
Year of Study: First
Semester: On demand
Number of ECTS Credits: 6
Class Contact Hours: 28
Mode of Delivery
Face to Face
Prerequisites
None
Course Objectives
Data is considered as the oil of the 21st century and precisely for this reason many companies and organizations are investing a lot of resources on data mining/analytics techniques in order to discover non-trivial patterns or correlations hidden in the data they collect. These findings could be used to enhance their decision making capabilities and improve their business operations. In this course, students will be taught the state-of-the-art techniques applied in data science for mining, analysis, visualization and interpretation of data. Both statistical and machine-learning based techniques will be taught with emphasis on the application of programmable solutions, visualization, interpretation and communication of the results obtained from the application of such techniques. In addition, the students will understand the uncertainty hidden in their results due to the probabilistic nature of the statistical and machine-learning techniques.
Learning Outcomes
- Understand and appreciate the value of data and data- driven decision making via data mining/analytics and how this improves business decisions.
- Demonstrate ability in data mining techniques in order to individuate regularities, discover anomalies, correlations and patters in complex datasets that suit business applications.
- Understand fundamental machine-learning techniques such as supervise, unsupervised and semi-supervised learning and demonstrate ability to apply techniques such as clustering and classification that suit business applications.
- Visualize effectively the results obtained from data analysis using a scripting programming language such as Python.
- Understand the uncertainty hidden behind the results of a statistical analysis by understanding the associated reliability metrics; accuracy, false positive/negative rates, true positive/negative rates, recall, precision.
- Increase their capabilities as managers to think in a more statistical and data-driven way and acquire skills to provide leadership in statistical methods for the staff in their area of responsibility.
Course Content
Course Features
Planned learning activities and teaching methods
lectures, group work, lab work, role playing, project-based learning, homework
Assessment methods and criteria
10% Class participation
40% Group Assignments & Class Participation
50% In-class examination
Language of Instruction
English
Work Placement(s)
Not applicable
Readings
Required Reading:
1. Sebastian Raschka. Python Machine Learning. Packt Publishing, 2015.
2. Simon Rogers and Mark Girolami. A first course in machine learning. CRC Press, 2011.
Recommended Reading:
Textbooks
3. Jared Dean. Big Data, Data Mining: Value Creation for Business Leaders and Practitioners. Wiley, 2014.
4. Wayne Winston, Christian Albright and Christopher Zappe. Data Analysis and Decision Making. Cengage Learning (4th Edition), 2009.
Research Articles
5. Jeffrey Steinhoff and Terry Carnahan. Smart Use of Data Mining is Good Business and Good Government. Journal of Governmental Financial Management, Spring 2012, Vol. 61 Issue 1, p 16-22, 2012.
6. Peter Mouncey. Creating Value with Big Data analytics: making smarter marketing decisions. International Journal of Market Research, Vol 58(5), 2016.
7. Carl Carande, Paul Lipinski and Traci Gusher. How to Integrate Data and Analytics into Every Part of your organization. Harvard Business Review, 2017.
8. Steve Lavalle, Eric Lesser, Rebecca Shockley, Michael Hopkins and Nina Kruschwitz. Big data, analytics and the path from insights to value. MIT Sloan Management Review 52(2), 2011.
English (UK) 
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