Data Visualization
UndergraduateLTU-CSE2DV2023
Course information for 2023 intake View information for 2025 course intake
Enrolments for this course are closed, but you may have other options to start studying now. Book a consultation to learn more.
- Study method
- 100% online
- Assessments
- Subject may require attendance
- Entry requirements
- Part of a degree
- Duration
- 12 weeks
- Loan available
- HECS-HELP and FEE-HELP available
Data Visualization
About this subject
Write code to clean and format data in preparation for data visualisation.
Design appropriate and effective visualisations that help users gain deep insight into complex the data sets.
Create interactive data visualisations for all users to effectively explore the data.
Generate informative reports using data visualisations.
- • Working with data: From discovery to visualisation.
- • Choosing appropriate techniques for data visualisation.
- • Creating data visualisations with Tableau.
- • Creating data visualisations with Python.
- • Information design.
The ability to visualise data is one of the most useful forms of data analysis and data presentation. Data visualisation provides an accessible way to see and understand trends, outliers and patterns in data. Nowadays there are many different tools for collecting and querying data. In order to gain full benefit from the collected data we need to visualise the data. In this subject you will learn the following: how to prepare data for visualisation; the design principals for creating effective data visualisations; and how to communicate your data analysis using reports containing data visualisations. You will learn how to use state of the art software tools for generating insightful and interactive data visualisations.
- Programming assignment 1 (1000 words equivalent) (25%)
- Programming assignment 2 (1000 words equivalent) (25%)
- End of semester examination (2000 words equivalent) (50%)
For textbook details check your university's handbook, website or learning management system (LMS).
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Entry requirements
Part of a degree
To enrol in this subject you must be accepted into one of the following degrees:
Elective
- LAT-TEC-DEG-2023 - Bachelor of Information Technology
Others
Past La Trobe University students who have previously completed CSE2MLX (Machine Learning) are ineligible to enrol in this subject.
Additional requirements
No additional requirements
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.
Related degrees
Once you’ve completed this subject it can be credited towards one of the following courses
Bachelor of Information Technology
UndergraduateLAT-TEC-DEG