ECONS205-20B (TGA)

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)

: uwt@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: clive.wilkinson@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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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, problem sets, lab sessions and student presentations. Students will get hands-on experience working with data in 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, and marketing 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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This paper will be taught in online lectures, and there will also be a two hour lab each week. Attendance at computer labs is strongly encouraged. 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.

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Learning Outcomes

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Students who successfully complete the course 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, the 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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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
18 Aug 2020
6:00 PM
25
  • Online: Submit through Moodle
2. Problem sets
12
  • Online: Submit through Moodle
3. Computer Labs Attendance & Submitted Computer Lab Work
10
  • Online: Submit through Moodle
4. Group Empirical Project and Presentation
6 Oct 2020
10:00 AM
20
  • Presentation: In Lab
5. Final Test
16 Oct 2020
6:00 PM
33
  • 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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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 (2019) Business Analytics, 3rd edition, Cengage Learning.

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Recommended Readings

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

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

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Other Resources

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University of Waikato Student Learning: Maths & Stats Resources

In addition to the required textbook, students are encouraged to read widely including the business section of newspaper,s 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:

http://fivethirtyeight.com/

https://www.gapminder.org/

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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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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

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