BDA322 - Data Science Mathematics

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA212 Statistics and Decision Models

Co-requisite: N/A

Aims & Objectives

This is an elective 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 science mathematics that support data analytics applications. 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 to plan, design, implement and evaluate advanced data science mathematical modeling and solutions in a diverse range of data analytic applications. They will gain knowledge of mathematical model design, modeling, evaluation and testing  in data analytic applications. Students will gain hands on practical experience in deploying and testing data science solutions to industrial applications that require critical analysis and mathematical modelling.

This unit will cover the following topics:

  • Data science mathematical modeling and data science applications using an appropriate programming language
  • Probability models: Bayes’ rules and random graph models
  • Monte Carlo simulations
  • Discrete parametric distribution families
  • Multivariate normal family of distributions
  • Mixture distributions
  • Predictive models
  • Trends in data science mathematics and programming languages

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 data science mathematical models and their use in data analytics applications;
b. Analyse and use appropriate data science mathematical solutions and their associated development platforms in a selected data analytics application;
c. Test and validate data science mathematical solutions in a selected data analytic application using appropriate platforms;
d. Evaluate data science mathematical solutions in consideration of business expectations and industry best practices;
e. Promote the use of appropriate data science mathematical solution in appropriate domains of data analytic applications.

Weekly Topics

This unit will cover the following content:

Week Topics
1 Overview of data science mathematics – its applications
2 Probability models: Bayes’ rules and random graph models
3 Monte Carlo methods and their applications in big data analysis
4 The family of normal distributions
5 Multivariate normal family of distributions
6 Mixture distributions
7 Calculus and optimization for data science
8 Gradient descent algorithms
9 Heuristic learning
10 Predictive modeling: parametric
11 Predictive modeling: non-parametric
12 Review and trends in data science mathematics

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%   d-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:

  • Norman Matloff, Probability and Statistics for Data Science: Math + R + Data, Chapman and Hall/CRC, Published June 20, 2019

References:

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.