BA218 - Data Analytics and Business Intelligence

Credit Points: 15 credit points

Workload: 36 hours

Prerequisite: BN110 Information Systems Fundamentals

Co-requisite: N/A

Aims & Objectives

BA218 Data Analytics and Business Intelligence develops student’s knowledge and skills for analysing data to improve business decision making. Specifically, students will gain knowledge and skills to understand and apply different business analytical models. In addition, students will use data mining techniques to interpret, analyse and learn from the data. These techniques also will allow students to identify data errors and relationships and trends in the data. Finally, since business analytics is about making better decisions, students also will learn how decision analysis can be used to develop an optimal strategy. The unit takes a practical approach with many up-to-date datasets used for demonstration in class.
 
Unit topics include:

  • Introduction to Modeling and Decision Analysis
  • Introduction to Optimization and Linear Programming.
  • Modeling and Solving LP Problems in a Spreadsheet.
  • Sensitivity Analysis and the Simplex Method.
  • Goal Programming and Multiple Objective Optimization.
  • Data Mining
  • Decision Analysis.

Learning Outcomes

The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: www.mit.edu.au
At the completion of this unit students should be able to:
a. Demonstrate an understanding of business and data Intelligence for organizational decision-making.
b. Analyse data and information using business analytical models. 
c. Generate solutions to problems using data mining techniques.
d. Analyse data and information using decision analysis techniques to develop optimal strategy.

Assessment

Assessment Task Due Date A B Unit Learning Outcomes
1. Formative Assessment Week 2 0% 0% a
2. Contribution and Participation Weeks 1-12 - 10% a-d
3. Assignment [Individual] Week 6 - 10% a,b
4. Report [Group] Week 10 30% - b,c
5. Case Study Analysis [Individual] (3 hours) TBA - 50% a-d
TOTALS   30% 70% 100%

Task Type: Type A: unsupervised, Type B: supervised.

Contribution and Participation (5%)

This unit has class participation as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules-policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Teaching Methods

NOTE: All School of Business units 3-hour workshops Flipped Classroom Mode. 

Textbook and Reference Materials

Note: Students are required to purchase the prescribed text book and have it available each week in class.

Prescribed Text Book

  • David R. Anderson; Dennis J. Sweeney; Thomas A. Williams; Jeffrey D. Camm; James J. Cochran; Michael J. Fry; Jeffrey W. Ohlmann (2018) An Introduction to Management Science: Quantitative Approaches to Decision Making, 15th Edition, Cengage Learning.

Other recommended references

  • Cliff T. Ragsdale., (2018). Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics (8th Edition). South Melbourne: Cengage Learning Australia.
  • Jeffrey D. Camm; James J. Cochran; Michael J. Fry; Jeffrey W. Ohlmann; David R. Anderson; Dennis J. Sweeney; Thomas A. Williams., (2019). Business Analytics, 3rd Edition. South Melbourne: Cengage Learning Australia.
  • Wayne L. Winston, S. Christian Albright., (2016). Data Analysis and Decision Making, 6E. South Melbourne: Cengage Learning Australia.
  • Ramesh Sharda, Dursun Delen, Efraim Turban. (2014). Business Intelligence and Analytics: Systems for Decision Support (10th Edition). Pearson Publication. ISBN: 978-0133050905.

Journal articles

  • Sun, Z., Sun, L., & Strang, K. (2018). Big Data Analytics Services for Enhancing Business Intelligence. Journal Of Computer Information Systems, 58(2), 162-169. doi:10.1080/08874417.2016.1220239.
  • Heller, M. (2017). 10 hot data analytics trends and 5 going cold: Big data, machine learning, data science - the data analytics revolution is evolving rapidly. Cio, 9-15.
  • Calof, J., Richards, G., & Santilli, P. (2017). Integration of business intelligence with corporate strategic management. Journal of Intelligence Studies In Business, 7(3), 62-73.

Adopted Reference Style: APA can be found in MIT library referencing.

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.