ECONS205-20A (HAM)

Data Analytics with Business Applications

15 Points

Edit Header Content
Division of Management
School of Accounting, Finance and Economics

Staff

Edit Staff Content

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)

: 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.
Edit Staff Content

Paper Description

Edit Paper Description Content

NOTE: This Paper Outline was updated on 3 April 2020, to reflect changes as a response to Covid-19 lock-down in New Zealand. The update has resulted in changes to the format and/or dates of the assessment and the schedule of topics. A document outlining the changes to assessments for the course as well as course calendar is available on Class Moodle. These changes have been approved by the Head of the School of Accounting, Finance and Economics.

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, 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.

Edit Paper Description Content

Paper Structure

Edit Paper Structure Content

This paper is taught through lectures (3 hours per week), and computer labs (2 hours per week). Attendance at lectures and 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.

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. We will also use Xorro-Q for in-class quizzes. To participate, students will need an internet capable device (e.g. laptop, smartphone, tablet). The lecture theatres are all Wi-Fi enabled and there are no data charges for accessing the Xorro-Q website on campus. Instructions on how to register with and use Xorro-Q will be provided in class.

Note:

As a response to the Covid-19, this paper will be taught fully on-line from March 30th onwards. We hope to offer interactive live “face-to-face” lectures and tutorials via Zoom video conference (Panopto as a backup). Try to keep Thursdays 12 – 1 and Fridays 9 – 11 available for live lectures and Tuesdays 11 – 1 for live lab/tutorial session. These sessions will be recorded and will be uploaded onto Moodle. Details on how to join the Zoom sessions will be posted on Moodle.

Starting from March 30 onward, instead of the in-class participation and surprise quizzes, five computer based-online quizzes, will be assigned throughout the semester in Moodle. These quizzes will consist of a set of questions which will be randomly generated when you log on. Each quiz will be a set of multiple choice questions based on topics covered over the previous lectures. These quizzes will consist of a set of question which will be randomly generated when you log on. Each quiz will be live on Moodle for 7 days.

Edit Paper Structure Content

Learning Outcomes

Edit Learning Outcomes Content

Students who successfully complete the course should be able to:

  • 1. Interpret business and economic data
    Linked to the following assessments:
  • 2. Explain how data analytics theory applies to business decision making
    Linked to the following assessments:
  • 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
    Linked to the following assessments:
  • 4. Make inference on population means, difference between means for business decision making
    Linked to the following assessments:
  • 5. Use regression analysis and critically appraise the merits and shortcomings of using regression methods to analyse empirical data
    Linked to the following assessments:
  • 6. Evaluate evidence to inform decision making
    Linked to the following assessments:
  • 7. Demonstrate proficiency in using Microsoft Excel as a statistical and analytical tool
    Linked to the following assessments:
Edit Learning Outcomes Content
Edit Learning Outcomes Content

Assessment

Edit Assessments Content

Assessment Components

Edit Assessments Content

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
24 Apr 2020
9:00 AM
25
  • Online: Submit through Moodle
2. On-line Quizzes
5
  • Online: Submit through Moodle
3. Problem Sets
15
  • Online: Submit through Moodle
4. Submitted Computer Lab Work
5
  • Hand-in: In Lab
  • Online: Submit through Moodle
5. Empirical Project
3 Jun 2020
No set time
15
  • Presentation: In Lab
6. Final Test
22 Jun 2020
6:00 PM
35
  • Other: At final test venue (PWC)
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
Edit Assessments Content

Required and Recommended Readings

Edit Required Readings Content

Required Readings

Edit Required Readings Content

Camm, J., Cochran, J., Fry, M., Ohlmann, J., Anderson, D., Sweeney, D., and T. Williams (2019) Business Analytics, 3rd edition, Cengage Learning (On Course Reserve).

Edit Required Readings Content

Recommended Readings

Edit Recommended Readings Content

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

Koop, G (2013) Analysis of Economic Data, Wiley (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)

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/

Edit Recommended Readings Content

Other Resources

Edit Other Resources Content

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, 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/

Edit Other Resources Content

Online Support

Edit Online Support Content

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

Edit Online Support Content

Workload

Edit Workload Content
As per assessment components and lecture timetable.
Edit Workload Content

Linkages to Other Papers

Edit Linkages Content
Note any linkages to other papers where the linkage is of importance.
Edit Linkages Content

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

Edit Linkages Content