| CODE | LAS3016 | ||||||
| TITLE | Data Science for Business | ||||||
| UM LEVEL | H - Higher Level | ||||||
| MQF LEVEL | 6 | ||||||
| ECTS CREDITS | 4 | ||||||
| DEPARTMENT | Centre for the Liberal Arts and Sciences | ||||||
| DESCRIPTION | The business world is turning into a hive of data. The analysis of these same large data sets is what ultimately turns a business into a competitive one. Most data science courses being offered require a good understanding of the various technical and mathematical skills. This does not help certain business leaders, who have no such background, but who still need to understand the world of data and apply it accordingly to their business. Therefore, this Unit acknowledges the shortage of business leaders and managers with a detailed working knowledge of data analytics and will assume very little technical and mathematical knowledge. It introduces participants to the basics of data analytics using industry-standard tools and provides them with a deep understanding of the industrial and scientific relevance of advanced analytics and their application in strategic and operational decision-making. Targeted towards managers, decision makers and c-level staff, the Unit will take participants through the data analysis process with a particular emphasis on various analytical skills and understanding of the results to aid in the decision making. In doing so, this short course will provide a blend of data analytics, business acumen and advanced management skills. This Unit aims to provide an adequate understanding of the importance of applying such tools within all stages of the business development. It will also help underscore the importance of data science within the business, managerial, economic and financial industry, with the help of application in real-life situations. Finally, it will help equip the students with the necessary business decision making skills using business intelligence tools and big data, to apply them in various business disciplines. These various aspects of the Unit will allow participants to become more conversant in the field and understand their role within the large volumes of data they may be sitting on. It will also provide an understanding of the structure of the data science pipeline, the goals at each stage and an understanding of the common challenges that may derail a data science project. Learning Outcomes: 1. Knowledge & Understanding: By the end of the Unit the student will be able to: - Explain the the main BI tools; - Demonstrate knowledge of the major methods of customer data collection used by companies and understand how this data can inform business decisions; - Understand how such tools are used to predict customer behavior and identify the appropriate uses for each tool; - Communicate key ideas about customer analytics and how the field informs business decisions. 2. Skills: By the end of the Unit the student will be able to: - Use BI systems and technology to support decision making; - Understand BI applications based on users’ needs; - Identify business and technical requirements for a BI solution; - Apply relevant theories, concepts and techniques to solve real-world BI problems; - Perform data analyses; - Visualize and explain the results of data analyses. Main Text/s and any supplementary readings: - John W. Foreman, Data Smart: Using Data Science to Transform information into insight, 2013. - Cindi Howson, Successful Business Intelligence: Unlock the Value of BI & Big Data, 2013. - Daniel Covington, Analytics: Data Science, Data Analysis and Predictive Analytics for Business, 2016. - Russell Walker, From Big Data to big Profits: Success with Data and Analytics, 2015. |
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| STUDY-UNIT TYPE | Lecture | ||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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