BDA212 - Statistics and Decision Models

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA112 Data Science Fundamentals

Co-requisite: N/A

Aims & Objectives

This is a core unit out of a total of 24 units in the Bachelor of Data Analytics (BDA). This unit addresses the BDA course learning outcomes and complements other courses in related fields by developing students’ specialised knowledge of data analytics in Information Technology. For further course information, refer to: http://www.mit.edu.au/study-with-us/programs/Bachelor-DA. This unit is part of the AQF level 7 (BDA) course.

Students will learn how to design, develop and evaluate statistical models in data analytic applications. They will gain knowledge of statistical modeling and probabilistic theories in decision support; and practical skills to construct and validate statistical models in data analytic applications.

This unit will cover the following topics:

  • Statistical modeling and probability theories
  • Principal component analysis
  • Classification and regression models
  • Statistical model validation and testing (e.g. R or Python-based platforms)
  • Statistical decision models in industry applications

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: www.mit.edu.au
4.2 Unit Learning Outcomes
At the completion of this unit, students should be able to:
a. Demonstrate the knowledge of statistical models in data analytic applications;
b. Examine and apply suitable statistical models in data analytic applications;
c. Perform classification and regression using the selected statistical models;
d. Evaluate performance of the selected statistical models according to their standards and specifications;
e. Present statistical outcomes and interpretations at different levels of formality in collaborative or autonomous manner.

Weekly Topics

This unit will cover the following content:

Week Topics
1 Introduction to statistics and decision models
2 Data analysis and information extractions
3 Probability theories in data analytics
4 Probability distributions in data analytics
5 Inferential statistics fundamentals
6 Statistical modelling in data analytics
7 Statistical model validation and testing
8 Principal component analysis
9 Classification models
10 Regression models
11 Industry applications with statistical decision models
12 Future trends in statistical decision models

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B
Week 3
Week 8
5%
10%
  a-c
Assignment 2 Week 11 25%   c-e
Laboratory participation & submission Week 2 - 11 10%   a-e
Final Examination (2 hours) End of trimester   50% a-e
TOTALS   50% 50%  

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

Class Participation and Contribution
This unit has class participation and student contribution 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.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials

Textbook:

  • M. Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong, Mathematics for Machine Learning, Cambridge University Press. 2020. 

References:

  • S. Christian Albright, Wayne L. Winston, Business Analytics: Data Analysis & Decision Making, Cengage, 2016.
  • J. R. Evans, Statistics, Data Analysis & Decision Modeling, 5th Ed., Pearson, 2013. 
  • F. J. Samaniego, Stochastic Modeling and Mathematical Statistics, CRC press 2014
  • T. A. Runkler, Data Analytics - Models and Algorithms for Intelligent Data Analysis, Springer 2016.

Adopted Reference Style: IEEE
 

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.