BUSAN205-23B (HAM)

Data Analytics with Business Applications

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)

Lecturer(s)

Administrator(s)

: denise.martin@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: em.pooley@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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What this paper is about

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Paper Description

Business analytics offers a unique opportunity to learn how to make use of business data. This course covers the maths 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. While the paper is mostly focused on business analytics, it also allows students to undertake projects that are more crafted for social factors, such as exploring Maori knowledge of the land, economy and environment. Students are encouraged to start thinking early about social projects, including data and information on the Maori economy and communities.

This course uses a combination of lectures, lab sessions, and online discussions in Moodle forum. 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. 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 through a group project.

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How this paper will be taught

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Paper Structure

Lecture and office hour:

There will be three hours of lecture each week, which will also be available in panopto recording and uploaded in Moodle. In addition, the paper convenor will hold a one-hour office hour per week on Monday from 11 am to 12 pm or any other time by appointment. My office is at MSB2.22 and my email is gmhassan@waikato.ac.nz

Lab:

A two-hour lab will be held each week on campus. Two Friday labs each week will be three-hour sessions each for those students who need extra time to catch up. Lab materials are based on lecture contents. You will be given a set of questions and exercises to complete in the lab using Microsoft Excel and then hand in a hard copy to the tutors. Your lab marks depend on whether you submitted the hard copy in the lab session or emailed it to the tutor.

Drop-in-sessions:

There will be six drop-in sessions held by both the lecturer and the tutors during the whole term. Three sessions will be held before the teaching recess and three afterwards. The drop-in sessions will occur within the campus, and the time and venue will be informed during the term via Moodle. We cannot ensure whether drop-in sessions will be Zoomed as it depends on the availability of a room with a camera which can't be decided in advance. It is not compulsory, but you are requested to RSVP in Moodle so that we may book an appropriate size room.

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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 will also be suitable and it is available on course reserve at the library )

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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 70:30. There is no final exam. The final exam makes up 30% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 70:30 or 0:0, whichever is more favourable for the student. The final exam makes up either 30% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Computer Labs
10
  • Hand-in: In Lab
2. Midterm Test
15 Aug 2023
10:00 AM
25
  • In Class: In Lecture
3. Group Project Including Māori Business
13 Oct 2023
11:30 PM
20
  • Online: Submit through Moodle
4. Quiz
15
  • Online: Submit through Moodle
5. Exam
30
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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