University of Technology Sydney

42895 Data Literacy

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2022 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 1 cp
Result type: Grade and marks


Data literacy is the basic ability to read, understand and represent data in a given context. It also enables professionals to describe the use case, application and resulting value and communicate to the stakeholders in a clear, appropriate way. Data literacy can be seen as learning to communicate in a new shared language of data and is now a core skill for any organisation working with data.

This subject introduces the student into the world of data and basic concepts required to work in a data-minded organization; it can act as an on-ramp to further study in data science, analytics or statistics.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Demonstrate an understanding and clearly communicate key concepts and relevant information from a data set.

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)

Teaching and learning strategies

Microcredential presentation includes weekly synchronous one-hour online workshops facilitated by an expert UTS academic(s) supporting self-study and online (LMS) learning activities. Case studies of real-world business illustrate applications of data understanding techniques. The workshop sessions focus on hands-on experience in data understanding and interpretation. Regular formative activities throughout the semester will allow learners to gauge their progress.

Content (topics)

• What data is and key characteristics of data

• The most common statistics used to describe data

• An understanding of uncertainty

• Correlation vs causation

• Some common errors and biases

• Basic ways to visualise data

• How to communicate a data set and its relevance to different groups

• How different industries are using data? – use cases


Assessment task 1: Data Exploration Report


Demonstrate ability to understand basic concepts connected with data exploration. It reinforces the hands-on skill in data exploration ad understanding.


This assessment task addresses the following subject learning objectives (SLOs):


This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):


Type: Report
Groupwork: Individual
Weight: 100%

1,000 words

Minimum requirements

In order to pass the microcredential, a learner must achieve an overall mark of 50% or more.