Sept 2015: This unit is core in the BCS Data Science stream in 2016.

Data Science is a rapidly expanding field in industry and many leading universities in the USA and the UK are starting data science degrees and units. Monash FIT trialed a data science unit for the undergraduate business IT course, FIT3152, in 2013, which is continuing, mostly focusing on applied data analysis and use of software. This Modelling for Data Analysis unit is being trialed as the starting unit for a full Bachelors in Data Science being proposed for start in 2016, and a masters variant of this unit for start in the proposed Monash-Pearson Alliance online Master of Data Science in 2015. By starting this unit in 2015, we ease the burden of later introductions.

Sept 2015: Updated for course architecture programs. Effective 2016.

July 2014: Add MAT2003 as an alternate prerequisite to MAT1841 and MTH1030 after discussion with chief examiner.

30 March 2017: Added reasons for Change on behalf of Chief Examiner. Examination hours updated to meet new University Examination Procedures. Effective S2 2017.

26 April 2017: Updated based on experience with FIT5197 and undergraduate expectations.

12/06/2017: Admin - updating location of offering to reflect actual campus offerings at the ADE's request.

June 2017: Add MTH1035 (advanced version of MTH1030 for advanced science students) as alternate prerequisite to MTH1035/MAT1841

07/12/2017 - Updating prerequisites to include the new programming unit.

24/9/2019: Admin - updating exam duration to include additional 10 minutes as per University requirement.

01/10/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

This unit is a core unit in the BCS Data Science stream from 2016.

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

1. Perform exploratory data analysis with descriptive statistics on given datasets
2. Construct models for inferential statistical analysis
3. Produce models for predictive statistical analysis
4. Perform fundamental random sampling, simulation and hypothesis testing for required scenarios
5. Implement a model for data analysis through programming and scripting
6. Interpret results for a variety of models

020399

This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. It covers:

• Data: collection and sampling, data quality.
• Analytic tasks: statistical hypothesis testing, exploratory and confirmatory analysis.
• Probability distributions: dependence and independence, multivariate Gaussian, Poisson, Dirichlet, random number generation and simulation of distributions, simulation of samples (bootstrap).
• Predictive models: linear and logistic regression, and Bayesian classification.
• Estimation: parameter and function estimation, maximum likelihood and minimum cost estimators, Monte Carlo estimators, inverse probabilities and Bayes theorem, bias versus variance and sample size effects, cross validation, estimation of model performance.

Ross, S.M. (2014) Introduction to Probability and Statistics for Engineers and Scientists, 5th ed. Academic Press.

Technological requirements

Students are required to regularly check Moodle for announcements regarding the subject.

(For Clayton Campus ONLY) Please note: This is a bring your own device unit. You will be expected to bring a web-connected device (i.e., laptop or tablet) to class to access specialist software. The applications for your class can be accessed at the website move.monash.edu

on-campus

Lecture and/or tutorials or problem classes

This teaching and learning approach helps students first encounter the information at lectures, discuss and explore them at length during tutorials, tests them via quizzes, and enables them to practice in a hands-on environment during labs.

Examination (2 hours and 10 minutes): 50%; In-semester assessment: 50%

1. Assignment 1: 10% - ULO ?
2. Assignment 2: 20% - ULO ?
3. Assignment 3: 20% - ULO ?
4. Examination: 50% - ULO ?

Minimum total expected workload equals 12 hours per week comprising: (a) Contact hours for on-campus students:

• Two hours lectures
• Two hour studio

• A minimum of 8 hours of independent study per week for completing lab and project work, private study and revision.

FIT

FIT1045 or FIT1053 and MAT1830 and one of MAT1841 or MAT2003 or MTH1030 or MTH1035