Exam information
Course code and name Semester
Assessment type
BSAN2201 Principles of Business Analytics Semester 1, 2022
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School-Based Take-home Assessment (A3)
Assessment Date and time
The assessment will be available from Monday the 13th of June from 9am. The assessment is due at 3pm on Friday the 17th of June.
Please note you will not be able to access the assessment task after this time.
Assessment Window
You have a five-day window in which you must complete this assessment. You can access the assessment and submit your answers at any time within the five-day window.
Optional: Even though you have the entire five days to complete and submit this assessment, the expectation is that it will take most students between four to six hours to complete it.
Weighting Permitted materials
Required/recommended materials
This assessment is worth 30 percent of your total mark for this course. This is an open book assessment – all materials permitted.
To complete this assessment successfully you will need a computer with the following software: Microsoft Word.
Should you have any issues about the assessment please contact the course coordinator –
Instructions
The assessment consists of ten short-answer style questions worth a total of 100 points. The questions have the same weights (10 questions × 10 points each = 100 points). Please answer all the questions and clearly label each answer with reference to the question it addresses. Submit your answers as a typed Microsoft Word document.
Assessment extension/deferral
Please begin this assessment as soon as possible within the available window. However, if you become unwell or experience exceptional circumstances while completing this assessment then submit an extension request before the due date/time.
Who to contact
Important assessment condition information
The normal academic integrity rules apply to this assessment task.
• You are not to seek outside assistance with this assessment item (other than from the course coordinator) or to give assistance to others – giving and/or seeking outside assistance will be deemed cheating and will result in disciplinary action.
• You are permitted to use your prior work in the course to help construct your answers.
By undertaking this online assessment you will be deemed to have acknowledged UQ’s academic integrity pledge to have made the following declaration: “I certify that my submitted answers are entirely my own work and that I have neither given nor received any unauthorised assistance on this assessment item”.
Institute for Teaching and Learning Innovation (ITaLI) | Last updated by UQBS: 5 May 2020 1
Question 1
Why did I ask you to read the book and/or watch the film Moneyball, and in what ways is the book/film emblematic of the analytics revolution? To be very specific, what is the outcome variable of interest and what are the likely input (or feature) variables in the predictive model used in Moneyball? (Can you think of another example – like Moneyball – that is illustrate of the potential impact of predictive analytics?)
Question 2
Compare and contrast the “stages model” and “born analytical” pathways to becoming an analytical competitor. What pathways have Amazon and Netflix followed to become analytical competitors, have they followed the same or different pathways and where do they currently sit on their analytical journeys? (What factors do you think influences the choices of firms in pursuing the stages versus the born analytical pathways?)
Question 3
What is algorithmic trading and what are the main components of algorithm trading systems? Can you offer an example of a firm that uses an algorithm trading system? Longer-term, what are some of the potential challenges for firms implementing algorithmic trading systems? (What is the alternative to using algorithmic trading, how is it changing the practice of trading financial securities?)
Question 4
What is personalisation? What are the main benefits to consumers and to firms of personalisation, and the potential costs to firms and consumers? Can you offer an example of a firm that is known for personalisation? What is one of the main challenges for firms in implementing personalisation? (What is the opposite to personalisation, how is personalisation changing the practice of marketing?)
Question 5
What is the basic role and responsibilities of the Chief Analytics Officer? Do you see the role of
Chief Analytics Office as a complement or a substitute for existing C-suite roles, and why? Outline the key progressions a graduate business analyst might make from the point of entry into the workforce through to the role of Chief Analytics Officer? (What expectations do you think firms have for graduate business analysts?)
Question 6
Briefly describe the business analytics process. What are the key steps in the business analytics process, and why are these steps the key steps? What specific activities can business analysts undertake to improve organisational and/or stakeholder “buy-in” to an analytics project? (How important is creativity in the business analytics process – where can creativity play a role?)
Question 7
What is “big data” and what are its defining characteristics? Give an example of business information that is quantitative and structured and an example of business information that is qualitative and unstructured? Develop your example – how would the data for your examples be generated and how could firms use that data to improve business processes or decision making. (Can structured, quantitative data and unstructured qualitative be combined?)
Question 8
How is the role of business analyst similar to and different from the role of data scientist? How would you expect a business analyst and a data scientist to allocate their time among the following core activities: project conceptualisation and establishing business metrics, data preparation and manipulation, data analysis, and reporting and communication? (What is needed for business analysts and data scientists to work together effectively?)
Institute for Teaching and Learning Innovation (ITaLI) | Last updated by UQBS: 5 May 2020 2
Question 9
What is machine learning? How is the machine learning approach to artificial intelligence different to traditional approaches to artificial intelligence? Why is the computer described as a universal machine? Name two methods of machine learning and briefly describe how they can be applied in business. (Can you think of applications of these methods of machine learning to fields outside of business?)
Question 10
What is the relationship of deep learning to machine learning, and of machine learning to artificial intelligence? Why is there such emphasis now on deep learning – what antecedent conditions are in place today that have ushered deep learning breakthroughs? What are some of the breakthroughs that deep learning has made possible? (Should we be excited or terrified of deep learning?)
End of exam
Institute for Teaching and Learning Innovation (ITaLI) | Last updated by UQBS: 5 May 2020 3
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