BUSAN205-23VA (VTN)

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)

: tmaicam@waikato.ac.nz
: trangn@waikato.ac.nz
: tthinguy@waikato.ac.nz

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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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 hands­on work with data and Microsoft Excel. Weekly computer­based 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.

TOPICS TO BE COVERED

  1. Introduction: Data and Data Analytics
  2. Descriptive Statistics: Numerical summarize and Visualization
  3. Basic Probability and Random Variable
  4. Confidence Interval and Hypothesis Testing
  5. Correlation and Regression
  6. Forecasting Time series
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How this paper will be taught

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This paper will be taught using lectures (3 hours) and tutorial (2 hours), in 12 weeks.

In lectures, we will carefully develop the basic ideas and tools and provide some examples of the way they can be used. The exercises provide an opportunity for students to apply these ideas and tools.

Lecture and tutorial times are:

DayTimeRoom
Monday1pm to 4pmA2-801
  • Tutorials (all in Hanoi time):
DayTimeRoom
Tuesday3:30pm to 5:30pmA2-801
Thursday3:30pm to 5:30pmA2-814
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Required Readings

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[1] 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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You will need to have

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[2] Duignan, J. (2014) Quantitative Methods for Business Research Using Microsoft Excel, Cengage Learning.

[3] Hyndman, R. and Athanasopoulos, G. (2018) Forecasting: Principles and Practice, 2nd ed., OTexts: Melbourne, Australia.

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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Interpret business and economic data
    Linked to the following assessments:
  • Explain how data analytics theory applies to business decision making
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  • Identify and apply 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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  • Make inference on population means, difference between means for business decision making
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  • Use regression analysis and critically appraise the merits and shortcomings of using regression methods to analyse empirical data
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  • Evaluate evidence to inform decision making
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  • Demonstrate proficiency in using Microsoft Excel as a statistical and analytical tool
    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. Participation
10
2. Midterm Test
10 Apr 2023
6:30 PM
20
  • In Class: In Lecture
3. Group Empirical Project and Presentation
20
  • Hand-in: In Tutorial
  • Presentation: In Lab
4. Microsoft Excel Quizzes
20
  • Online: Submit through Moodle
5. Final Test
30
  • 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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