UnitLevel 5Postgraduate

ITO5149 Applied data analysis

Faculty of Information Technology

ITO5149 Applied data analysis is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2023. It needs ITO5197.

Credit points
6
Offered in 2023
Other periods
Monash Online
Assessment
No exam
3 tasks

This is the 2023 handbook entry. See the 2027 entry.

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Requisites

Before ITO5149

Prohibitions

You can't enrol if you have passed any of these.

Prerequisites

Pass these before you enrol.

After ITO5149

No unit lists ITO5149 as a prerequisite in the 2023 handbook.

Equivalent units

The same content under another code. Only one of them counts.

Overview

This unit aims to provide students with the necessary analytical and data modelling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modelling theory. Students will need to interpret the results and the suitability of the algorithms.

Offerings in 2023

Teaching periodCampusMode
Teaching period 6Monash OnlineMo

Assessment

  • Assessment task 1Other
    20%
  • Assessment task 2Other
    40%
  • Assessment task 3Other
    40%

Learning outcomes

When you finish this unit, you should be able to:

  1. 1

    Analyse data sets with a range of statistical, graphical and machine-learning tools;

  2. 2

    Evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;

  3. 3

    Design solutions to real world problems with data analytics techniques;

  4. 4

    Assess the results of an analysis;

  5. 5

    Communicate the results of an analysis for both specific and broad audiences.

Workload and teaching

  • Workshops12 hours
  • Teaching approachOnline learning

A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.

Learning resources

Required resources

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning

Zhao, Y (2012) ​ R and Data Mining: Examples and Case Studies. Academic Press

Technology resources

Students will need R/Python, which is freely available software that can be download from the Internet.

Contacts

Chief Examiners
Dr Lan Du

Common questions

What are the prerequisites for ITO5149?

You need ITO5197 before you enrol.

When is ITO5149 offered?

ITO5149 has no offerings listed in the 2023 handbook.

Does ITO5149 have an exam?

No. ITO5149 has 3 assessment tasks and no exam.

More details

Credit points
6
Level
5
Study level
Postgraduate
Faculty
Faculty of Information Technology
Type
Coursework
EFTSL
0.125
Student contribution
SCA Band 2
Study abroad
Not available
Handbook years
202220232024202520262027