ITI5145 Introduction to data science
Faculty of Information Technology
ITI5145 Introduction to data science is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2024. It needs ITI9136.
- Credit points
- 6
- Offered in 2024
- Other periods
- Indonesia
- Assessment
- No exam
- 4 tasks
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2027 entry.
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Requisites
Before ITI5145
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
After ITI5145
No unit lists ITI5145 as a prerequisite in the 2024 handbook.
Enrolment rules
This unit is only available to students enrolled at the Indonesia campus.
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 2024
| Teaching period | Campus | Mode |
|---|---|---|
| Monash Indonesia term 4 | Indonesia | On campus |
Assessment
- Data Analysis with R (Coding task I)Other15%
- Propose a Data Science ProjectOther15%
- Data Analysis with Tools and Scripting (Coding task II)Other40%
- Business and data case study: Report and presentationReport30%
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 recognised standards of data science.
Workload and teaching
- Laboratories24 hours
- Lectures24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per teaching period typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
Learning resources
Technology resources
Python is available in the labs and is free to download from https://www.python.org/downloads/
Contacts
- Chief Examiners
- Dr Guanliang Chen
Common questions
What are the prerequisites for ITI5145?
You need ITI9136 before you enrol. Enrolment rules also apply.
When is ITI5145 offered?
ITI5145 has no offerings listed in the 2024 handbook.
How much work is ITI5145?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ITI5145 have an exam?
No. ITI5145 has 4 assessment tasks and no exam.