BUSAN205-22A (HAM)

Data Analytics with Business Applications

15 Points

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Division of Management
School of Accounting, Finance and Economics

Staff

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Convenor(s)

Lecturer(s)

Administrator(s)

: denise.martin@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: yilan.chen@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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Paper Description

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Please note: Due to the uncertainty caused by the pandemic, the paper outline is subject to change throughout the trimester. Any changes will be communicated through Moodle. Students should check the Moodle page for latest updates on a daily basis.

The exponential growth in the availability of data requires that students are able to make informed decisions using data, and effectively communicate their data analyses. This course covers the analytical and statistical techniques that business and management students are most likely to use in their future courses and professional careers. Students will learn different types of data analytics methods and their applications to problems in accounting, economics, finance, marketing, and business in general.

This course uses a combination of lectures, case discussions, lab sessions and student presentations. Students will have hands-on work with data and Microsoft Excel. Weekly computer-based workshops aim to enhance understanding of how the techniques introduced in lectures apply in a business context. Topics to be covered include presenting data using visual and descriptive statistics, measuring and understanding the relationship between variables, predictive analytics and prescriptive analytics tools. Empirical examples from economics, finance, accounting, marketing, supply chain and logistics will illustrate the material covered. Emphasis will be placed on understanding concepts and analysis of data. The paper will also provide opportunities for students to enhance their teamwork and communication skills with an empirical group research project and poster presentation.

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Paper Structure

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Note: BUSAN205-22A (HAM) is offered in Flexi Synchronous Mode, which means on-campus activities (lectures, labs, tests) are available for students to attend in person, however, students can complete the paper online if they choose. For those taking the paper online, you are required to engage in weekly labs and tests at specified times. More information on FlexiSync Mode will be provided on Moodle.

This paper is taught through interactive lectures (3 hours per week), and computer labs (2 hours per week). Lectures will be delivered face-to-face and also recorded on Panopto. Lab sessions however, will not be recorded. Attendance at lectures and computer labs is strongly encouraged. Experience has shown that attending lectures and computer labs in person achieves the best outcomes for students.

In lectures, we will carefully develop the basic ideas and tools, and provide some examples of the way they can be used. In labs, you will be given a set of questions and exercises to complete using Microsoft Excel.

In order to promote class participation and to provide immediate in-class feedback about specific concepts, we will use the Xorro-Q student response system. To participate, students will need an internet capable device (e.g. laptop, smartphone, tablet). The lecture theatres are Wi-Fi enabled and there are no data charges for accessing the Xorro-Q website on campus. Instructions on how to use Xorro-Q will be provided in class.

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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • 1. Interpret business and economic data
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  • 2. Explain how data analytics theory applies to business decision making
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  • 3. Identify and apply the appropriate data analytics methods to real world business issues and interpret the results, including analysis of random experiments and methods of comparing groups
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  • 4. Make inference on population means, difference between means for business decision making
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  • 5. Use regression analysis and critically appraise the merits and shortcomings of using regression methods to analyse empirical data
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  • 6. Evaluate evidence to inform decision making
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  • 7. Demonstrate proficiency in using Microsoft Excel as a statistical and analytical tool
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Assessment

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Extra credit - Reflective Learning Journal

A reflective learning journal is a record of the reflective thought and meaning you are making as you engage in a learning experience. Thinking about your thinking can help you become a better learner. In addition to reflecting on your own learning, you can also use these journals to monitor your progress and track your learning goals.

Students can earn up to 10 extra points throughout the trimester. In order to receive full credit for your journal entries, you must submit at least 5 journal entries from the topics we covered in lectures by 5pm on June 12th. The raison d'être of the reflective journal is to encourage you to be actively aware of your own learning throughout the trimester, thus, entries must be submitted at least a week apart from one another (i.e. you cannot complete all of your journal entries in the first or last week of class). While I will not be able to read every journal entry for every student, at least two of your journal entries will be read by me in order to determine your final journal grade, which will be posted to Moodle at the end of the trimester.

Details will be provided in class.

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Midterm Test
12 Apr 2022
8:00 AM
24
  • In Class: In Lecture
2. On-Line Quizzes
8
  • Online: Submit through Moodle
3. Problem Sets
15
  • Online: Submit through Moodle
4. Submitted Computer Lab Work
5
  • Online: Submit through Moodle
5. Group Empirical Project and Presentation
15
  • Presentation: In Lab
6. Final Test
33
  • In Class: In Test
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Required Readings

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Camm, J., Cochran, J., Fry, M., Ohlmann, J., Anderson, D., Sweeney, D., and T. Williams (2020) Business Analytics, 4th edition, Cengage Learning. Earlier edition (3rd edition) of the book will also be suitable and it is available on course reserve at the library. This book is also available as an e-book from the University of Waikato library.
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Recommended Readings

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Koop, G (2013) Analysis of Economic Data, Wiley (on Course Reserve)

Duignan, J. (2014) Quantitative Methods for Business Research Using Microsoft Excel, Cengage Learning (On Course Reserve)

Hyndman, R. and Athanasopoulos, G. (2018) Forecasting: Principles and Practice, 2nd ed., OTexts: Melbourne, Australia (freely available online at https://otexts.com/fpp2)

Blastland, M. and Dilnot, A. (2010) The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics and in Life, Penguin Publishing Group

Cairo, A. (2019) How Charts Lie: Getting Smarter about Visual Information, WW Norton & Company

Tipoe, E. and Becker, R. (2020) Doing Economics: Empirical Projects (freely available online at http://www.coreecon.org/doing-economics/)

Harford, T. (2021) The Data Detective: Ten Easy Rules to Make Sense of Statistics, Penguin Publishing Group

If you have a spare hour, we highly recommend watching The Joy of Stats, featuring the late Hans Rosling. His enthusiasm for statistics is infectious and his graphic data visualizations are terrific. You can stream the video here: https://www.gapminder.org/videos/the-joy-of-stats/


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Other Resources

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University of Waikato Student Learning: Maths & Stats Resources

In addition to the recommended textbook, students are encouraged to read widely including the business section of newspaper, the Economist magazine, and other similar sources. Additional paper resources will be made available on Moodle.

The following websites are examples of Data Analytics being used in practice:

World Development Report 2021: Data for Better Lives

https://ourworldindata.org/

http://fivethirtyeight.com/

https://www.gapminder.org/

https://www.stats.govt.nz/integrated-data/integrated-data-infrastructure/how-integrated-data-is-helping-new-zealanders/

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Online Support

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All course materials, plus other information of importance to students are available if via Moodle.

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Workload

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As per assessment components and lecture timetable.
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Linkages to Other Papers

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Note any linkages to other papers where the linkage is of importance.
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Prerequisite(s)

Prerequisite papers: 15 points from any ECONS, ACCTN, FINAN or STATS 100 level papers.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: ECON204, ECONS205

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