| Unit name | Applied Research Methods in Business Analytics |
|---|---|
| Unit code | MGRCM0020 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Han |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
Central to executing an applied research project in business analytics is learning how to frame a research question, conduct a literature review, use qualitative research methods and, most of all, statistical methods and data science applications for research. There are, in general, three roles that statistics and data science play in business analytics – the study of variation, for example, that of the co-variation between a stimulus and business performance; the study of populations, for example, to extract common features from multiple companies; and the study of data reduction, for example, to search among hundreds of thousands of variables for a small subset that is predictive of annual revenue. Leveraging these principles and tools, the students will be able to develop the necessary skills to address real-world business problems through analytical lenses.
How does this unit fit into your programme of study
The methods and tools learned in this Unit, when coupled with quantitative and qualitative knowledge learned from other Units, will prepare students for the Applied Research Project proposal assessed within this Unit and their final Applied Research Project.
An overview of content
The course will teach the fundamentals of how to conduct a literature review to identify research objectives and questions, how to justify research methods for the analysis of research objectives and questions, including qualitative and quantitative research methods. The unit will provide specialist training in advanced quantitative research methods and cover three fundamental goals of statistical learning: analysis of variance (including analysis of co-variance) (G1), the study of populations (including analysis of repeated measures from a single company or individual and data from multiple companies or individuals) (G2), and data reduction (including feature selection and dimension reduction via, for example, projection) (G3). The course will de delve into further, more refined topics in statistical methods and data science applications. These include correlation and network analysis, multivariate regression analysis, generalized linear model, and generalized estimation equation (related to G1); parameter estimation, confidence interval estimation, and population- and subject-specific models (related to G2); variable selection, principal component analysis, independent component analysis, and t-SNE (related to G3).
How will students, personally, be different as a result of the unit
This is half a unit - teaching will consist of five weeks - 3 hours x 5 = 15 hours. Week 1 focuses on explaining the research process, literature review and how to identify a research question; Week 2 aims at explaining different ontologies/philosophies of research, how to justify different research methods, and qualitative research methods; Weeks 3-5 cover the quantitative/statistical research methods.
More specifically, upon completion of this Unit, the students will be able to:
(1) identify a research gap in the literature and formulate coherent and achievable research objectives and questions;
(2) select and justify appropriate research methods for the analysis of research objectives and questions with due consideration of ethical issues including research ethics process and principles;
(3) choose supervised vs. unsupervised models to analyse business data;
(4) find patterns, such as clusters or networks, of multivariate business features;
(5) develop basic statistical models to identify a subset of variables that are associated with and predictive of business outcomes;
(6) perform basic feature selection or data reduction when facing multivariate or high-dimensional data;
(7) make a business inference (e.g., confidence interval estimation), assessment (e.g., score prediction), and (longitudinal) forecast leveraging findings from real-world data using (3)-(7).
Learning Outcomes
ILO 1 – Conduct a critical review of the literature and formulate a set of research objectives and questions
ILO 2 - Justify and apply appropriate research methods in the collection and analysis of data to address a research problem with appropriate considerations of ethical issues including research ethics process and principles.
ILO3 – Develop a coherent and feasible applied research project proposal.
A total of 15 hours of teaching will be provided over 5 weeks (bi-weekly; three hours for each teaching week). These will comprise a combination of lecture talks (one-hour long), group discussions in class, and computer lab sessions for practice (two-hour long).
The amount of time necessary to spend in the self-directed study depends on individual learning objectives, but 60 – 85 hours over the course are expected, in principle, to form a basic understanding of the taught materials. All learning materials, apart from the core textbooks, will be available on Blackboard. You can also explore the discussion board to form peer support and discussion and expand on issues that are not discussed in detail in class. At the beginning of each week, you will review the forthcoming tasks and materials for the week and make study plans in advance. Early and timely planning will allow one to learn efficiently and catch up with unfamiliar concepts; additionally, it may, to a certain extent, relieve one’s stress and anxiety.
Tasks that help you learn and prepare you for summative tasks (formative):
Students will receive feedback on their understanding of the unit contents through in-class discussions of academic and practice-oriented reading that students will have to prepare before each session. Students will receive feedback on their ability to formulate research questions/objectives and justify and apply research methods through small in-class practical exercises (ILOs 1-2). Students will receive feedback on the development of their Applied Research Project proposal (ILOs 1-3).
Tasks that count towards your unit mark (summative):
There will be bi-weekly (once every two weeks) practical exercises and case studies (20%) (ILOs 1-2)
A 2,000-word Individual Applied Research Project proposal (80%). The proposal will cover the topic, questions, rationale, scoping of the literature, and research methods of students’ final applied research project (ILOs 1-3).
When assessment does not go to plan
If the assessment does not go to plan, then the re-sit will consist of (re-writing) the 2,000 words Individual Applied Research Project.
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. MGRCM0020).
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 University 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. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
