ITO5145 Introduction to data science
Faculty of Information Technology
ITO5145 Introduction to data science is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2022. It needs ITO4131, ITO4133 or ITO4136.
- Credit points
- 6
- Offered in 2022
- Other periods
- Monash Online
- Assessment
- No exam
- 4 tasks
This is the 2022 handbook entry. See the 2027 entry.
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Requisites
Before ITO5145
Prerequisites
Pass these before you enrol.
Prohibitions
You can't enrol if you have passed any of these.
After ITO5145
No unit lists ITO5145 as a prerequisite in the 2022 handbook.
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit looks at processes, case studies and simple tools to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed. Basic curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| Teaching period 4 | Monash Online | Online |
| Teaching period 5 | Monash Online | Online |
| Teaching period 6 | Monash Online | Online |
Assessment
- Data science exercisesOther20%
- Business and data case studyOther40%
- QuizOther30%
- Video presentation10%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Analyse the role of data in organisations, including curation and management issues;
- 2
Apply basic tools for performing exploratory data analysis and visualisation;
- 3
Apply basic tools for managing and processing big data;
- 4
Apply basic predictive modeling and data analysis methods;
- 5
Determine data storage and processing requirements for a data science project;
- 6
Identify data resources and standards.
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
Technology resources
Python is free to download from https://www.python.org/downloads/
Contacts
- Chief Examiners
- Dr Guanliang Chen