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 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.

20/9/2019: Admin - updating exam duration to include additional 10 minutes as per University requirement.

07/06/2017: Admin - adding reasons for change history back in.

02/06/2017: Updating exam hours from 3 to 2 hours as required by new University Examination procedures, effective S1 2018.

15/09/2015: Name changed (from Data Analysis) for course architecture. Effective 2016.

02/10/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

This unit will be an elective unit in the existing bachelors courses offered through FIT in 2015. This, and a Masters variant of it, would be core units in later Masters and Bachelors being proposed. There are no existing units offering the same material as this unit. Data Science, FIT3152, is related but more focused in presenting software and applications of a range of analytic methods. The proposed FIT3154 is more technical, looking at algorithms and underlying theory.

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

1. describe what machine learning is;
2. differentiate kinds of statistical learning models and algorithms;
3. evaluate a machine learning algorithm in typical contexts;
4. describe and apply the major models and algorithms for statistical learning;
5. identify the most competitive algorithms for typical contexts;
6. compare and contrast the differences between big data applications and regular applications of algorithms;
7. describe the theoretical limits of learning.

020119

This unit introduces the problem of machine learning and the major kinds of statistical learning used in data analysis. Learning and the different kinds of learning will be covered and their usage discussed. Evaluation techniques and typical application contexts will presented. A series of different models and algorithms will be presented in an exploratory way: looking at typical data, the basic models and algorithms and their use: linear and logistic regression, support vector machines, Bayesian networks, decision trees, random forests, k-means and clustering, neural-networks, deep learning, and others. Finally, two specialist topics will be covered briefly, statistical learning theory and working with big data.

on-campus

Lecture and/or tutorials or problem classes

Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%

1. Assignment 1: 20% - ULO ?
2. Assignment 2: 20% - ULO ?
3. Examination: 60% - ULO ?

Minimum total expected workload equals 12 hours per week comprising:

(a) Contact hours for on-campus students:

• Two hours lectures
• Two hours laboratories

(b) Additional requirements (all students):

• A minimum of 8 hours of personal study per week for completing lab/tutorial activities, assignments, private study and revision.

FIT

FIT2086 or related statistical background