| Unit name | Data Analytics in Business |
|---|---|
| Unit code | EFIMM0141 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Essien |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
no |
| Units you must take alongside this one (co-requisite units) |
no |
| Units you may not take alongside this one |
n/a |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
This unit will introduce students to the basic, intermediate and advanced concepts and methodologies of data analytics with application in business. Both theories and practices are covered in the unit. On one hand, this unit looks at a collection of concepts relevant to business analytics (e.g., big data, predictive analytics, data mining, machine learning etc.) and explains basic technologies to collect and manage data. On the other hand, this unit introduces standard analytical methods to examine the descriptive, explanatory and predictive nature of data. A special focus is given to the use of statistical and machine learning techniques such as regression, classification, and clustering to perform data mining and forecasting. The aim of this unit is to equip students with strong conceptual understanding of business analytics as well as the underlying principles behind predictive analytics of data. The unit will also provide students with the foundations of technical know-how to be able to make effective use of the latest analytics tools in further advanced modules. At the end of course, students will appreciate the substantial opportunities that exist in the business analytics and also learn the techniques to explore these opportunities.
On completion of this unit, students will be able to:
ILO 1: Discuss the concepts and methods of data analytics using relevant and appropriate terminologies.
ILO 2: Design a data analytics project with a critical assessment of the data mining process and techniques involved in collecting, managing and modelling actionable data.
ILO 3: Use a range of descriptive analytics techniques to discover, visualise and interpret patterns in a large amount of data.
ILO 4: Apply predictive analytics to predict future outcomes and model scenarios to address a range of business problems.
ILO 5: Evaluate and communicate insights derived from data to a critical audience and make them effective in actual business decision-making.
The unit will be taught in 10*3-hour lectorial sessions. The class will be highly interactive with analytical exercises, guest talks, discussions on case studies and other activities. Students will be directed to a wide range of academic papers and industrial reports to collect the latest information about data analytics techniques and practices. In practical sessions, students will be offered hands-on analytical exercises. Web-based learning support and electronic resources will be provided.
Formative assessment
There will be weekly exercise to be completed by students either individually or collectively to improve analytical skills. Answers and feedback will be available to students for self-assessment. A formative individual presentation will be scheduled in the halfway through to check students’ progress and formative feedback will be provided to help students with the final assessment.
Summative assessment
Individual coursework (100% weighting, ILO 1 – 5)
The assignment will focus on quantitative analyses of data for a forecasting task. Students will work individually to analyse a data set using appropriate data mining approaches to make predictions about future outcomes. The report (3,000 words) should describe the methodologies and results of data exploration and analyses. Students will also have to read relevant academic and practitioner literature to explain the managerial relevance of their work.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EFIMM0141).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the Faculty workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
