BUSAN302-23B (HAM)

Data-Informed Decision Making

15 Points

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The University of Waikato
Academic Divisions
Division of Management
School of Accounting, Finance and Economics

Staff

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Convenor(s)

Pei-Chi Kelly Hsiao

Pei-Chi Kelly Hsiao
6767
MSB.3.20
See Moodle
pei-chikelly.hsiao@waikato.ac.nz

Lecturer(s)

Administrator(s)

: maxine.hayward@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

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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What this paper is about

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This paper further develops students' data analytics mindset and builds competencies in quantitative analysis techniques. It focuses on data wrangling, exploration, visualisation and machine learning techniques. Students will utilise R to visualise data, detect trends and patterns, and make data-informed inferences and predictions.
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How this paper will be taught

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BUSAN302-23B (HAM) is offered in FLEXI Synchronous mode, which means some flexible elements of this paper can be completed online. Please note that you are required to engage in labs and scheduled assignments at set times.

Lectures:

  • Week 1: Class on Monday. Orientation lecture on Monday that introduces you to the course, answer any questions you may have, and demonstrate the technical tools we will use for data analysis.
  • Week 2 to Week 13: Classes on Friday only. No lectures on Monday, these are substituted for pre-recorded videos that covers the base concepts - make sure to review these prior to the labs as we will be applying those concepts.
  • Live lectures will be live streamed via Panopto, recorded, and made available to all students. It is the sole responsibility of any absentees to catch up with missed content.
  • We will be using R and Power BI Desktop throughout the course. Students should install these free software on their personal computer. Alternatively, students can remote access computer labs to use these software.

Labs:

  • Labs commence in Week 2 and all sessions are delivered on-campus face-to-face and live streamed via Zoom.
  • The lab sessions are not recorded.
  • Students are encouraged to attend the sessions on-campus as it involves group work.
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Required Readings

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There is no required textbook. Recommended readings will be indicated and available on the course Moodle page, these include:

  • Wickham, H. & Grolemund, G. (2016). R for Data Science. Available at: https://r4ds.had.co.nz/
  • Silge, J. & Robinson, D. (2017). Text Mining with R: A Tidy Approach. Available at: https://www.tidytextmining.com/
  • Boehmke, B. & Greenwell, B. (2020). Hands-On Machine Learning with R. Available at: https://bradleyboehmke.github.io/HOML/
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Demonstrate basic to advanced competencies in R and Power BI
    Linked to the following assessments:
  • Apply techniques to extract, wrangle, visualise and model data
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  • Apply appropriate statistical techniques to detect patterns and trends, make predictions, and evaluate cause-and-effect relationships
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  • Assess and interpret results from different quantitative analysis techniques
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  • Articulate the role of decision-making theory in real-work contexts and address business needs through data analytics
    Linked to the following assessments:
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Assessments

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How you will be assessed

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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. Quizzes
10
  • Online: Submit through Moodle
2. Lab Participation and Submission
14
  • In Class: In Lab
3. Class Presentation and Evaluation
8 Sep 2023
4:00 PM
13
  • In Class: In Lab
4. Individual Tasks
33
  • Online: Submit through Moodle
5. Final Test
13 Oct 2023
4:00 PM
30
  • Online: Submit through Moodle
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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