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  • MDA511 Mathematical and Statistical Methods

MDA511 Mathematical and Statistical Methods

Unit Overview

Credit Points:Prerequisite:Co-requisite:Workload:
20N/AN/ATimetabled hours for the trimester: 60 hours
(Lecture = 2 hours/week, Laboratory and Problem Based Learning (PBL) tutorial = 3 hours/week)
 

Aims & Objectives

This is a core unit out of a total of 12 units in the Master of Data Analytics (MDA). This unit addresses the course learning outcomes and complements other units in a related field by developing students’ specialised knowledge of statistical tools and technologies. For further course information refer to: Master of Data Analytics course page. This unit is part of the AQF level 9 (MDA) course.

Students will be exposed to the applied statistical methodologies from an analysis of data view point. Statistical methods play an important role in analysing data in a useful form. To understand and interpret data, it is necessary to engage students with the fundamentals of statistics, statistical data investigations, probability, and hypothesis testing. Students will gain not only statistical concepts, but also their application and interpretation using statistical tools, such as R.

This Unit includes the following topics:

  • Linear Algebra
  • Vectors, Scalars, Matrices
  • Descriptive Statistics
  • Probability and Discrete Probability Distributions
  • Continuous Probability Distributions
  • Estimation and Hypothesis Testing
  • Inference and Population Variances
  • Test of Goodness of Fit and Independence
  • Statistical Optimization Techniques
 

Learning Outcomes

Learning Outcomes

At the completion of this unit students should be able to:

  1. Develop knowledge and skills in using statistics to interpret data.
  2. Analyse and evaluate probability for reasoning in real-world situations.
  3. Compare solutions to problems using appropriate statistical tools.
  4. Analyse and interpret results from descriptive and predictive data analysis.
  5. Apply optimisation techniques for given statistical data.
     

Prescribed Books, Recommended Books & Online Resources

Textbook:

R. Peck, T. Short, Introduction to Statistics and Data Analysis, Cengage Learning, 6th Ed., USA, 2019.

 

References:

  • M. Taboga, Lectures on Probability Theory and Mathematical Statistics, CreateSpace Independent Publishing Platform, 3rd Ed., 2017
  • P. Bruce, A. Bruce, Practical Statistics for Data Scientists, 50 essential concepts, O’Reilly, 1st Ed., 2017
  • G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Application in R, Springers, 1st Ed., New York, USA, 2017
  • B. Cronk, How to use SPSS, A Step-by-Step Guide to Analysis and Interpretation, Routledge, London, 9th Ed., USA, 2017
  • L. C. Onyiah, Design and Analysis of Experiments. Classical and Regression Approaches using SAS, CRC 2016.
  • T. J. Quirk, Excel 2016 for Engineering Statistics. A Guide to Solving Practical Problems, Springer 2016.

Adopted Reference Style: IEEE

 

Assessment and Hurdle Requirement

Assessment

Assessment TaskLearning Outcomes Assessed*Assessment TypeWeighting
In-class testaB10%
Assignment 1 IndividualbA15%
Assignment 2 Groupc-dA15%
Laboratory and Problem Based Learning participation & submissiona-eA10%
Final Examinationsa-eB50%

Assessment Criteria / Hurdle Requirement

It is expected that students complete all assessments including the Final Examination. Students must obtain a mark of at least 50% in Task A (assignments, laboratories and PBL tutorials) and 40% in the final examination. In addition students must obtain at least 50 % overall to pass this Unit.

The Final Examination for this Unit is NOT an open book exam. Please note the following examination rules.

  1. Candidates must use a black or blue pen for written answers.
  2. Candidates may bring the following materials into the examination room:
    • scientific calculator (if specified) and
    • unmarked language dictionary (non-electronic)
  3. Candidates are not permitted to bring mobile phones, smart watches, portable computers or books into the examination room]

Late assignments / exercises:

Late assignments will be penalised at the rate of 10% per day, that is, an assignment is marked out of 90% for 1 day late, 80% for 2 days late, etc., and after 5 working days assignments will attract zero marks.

Special Consideration:

In the case of serious illness, loss or bereavement, hardship or trauma students may be granted special consideration. A completed Application for Special Consideration and supporting documentation must be submitted online on AMS. This application must be submitted no later than three working days after the due date of the specific piece of assessment or the examination for which the student is seeking Special Consideration. Further information is available at:

http://www.mit.edu.au/about-mit/institute-publications/policies-procedures-and-guidelines/special-considerationdeferment

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 communicateIndependent and Lifelong LearningEthicsAnalytical and Problem Solving Cultural and Global AwarenessTeam workSpecialist knowledge of a field of study
3215125

 

Legend

Levels of attainmentExtent covered
5The standard 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
4The standard 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
3The standard 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 
2The standard 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
1The standard is not considered, there is no theory or practice or activities associated with this standard

 

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