42821 Data Analytics Foundations
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 2025 is available in the Archives.
Credit points: 2 cp
Result type: Grade and marks
Anti-requisite(s): 31250 Introduction to Data Analytics AND 32130 Fundamentals of Data Analytics
Description
Data analytics is the art and science of turning large quantities of usually incomprehensible data into meaningful and commercially valuable information. It is the basis of modern computer analytics and intelligence. It includes a number of IT areas, such as statistical methods for identifying patterns in data and making inferences; database technologies for managing the data sets to be mined; a range of intelligent technologies that derive automatically patterns from data; and visualisation and other multimedia techniques that support human pattern discovery capabilities.
Data Analytics Foundations introduces learners to the significance and language of data analytics for business and society and the most common approach to data mining called cross-industry standard process for data mining, known as CRISP-DM. This microcredential offers practice in the foundations of data analytics; identifying data set types and attribute types, data preparation and cluster analysis. Advanced techniques for clustering develop skills in identifying problems for cluster analysis and a range of approaches to address these limitations. Applying these data analytics techniques enables interpretation of a data set and visual data exploration.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply pre-processing, transformation and visualisation to business data sets. (D.1) |
|---|---|
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 mining techniques. The workshop sessions focus on hands-on experience in data mining and data analytics tools, and the understanding and interpretation of the results. Regular formative quizzes throughout the semester will allow learners to gauge their progress.
Content (topics)
Data analytics in business and society- data mining concepts, types of data that we collect, the data mining and knowledge discovery process:
- CRISP-DM
- Attribute types
- Data set types
- Data preparation
- Clustering; problems for cluster analysis; types of data; partitioning methods, hierarchical methods; density-based methods; k-means and related methods.
- Visual data exploration and mining: data visualisation techniques and their applicability in data mining, visual data mining methods
Assessment
Assessment task 1: Data Exploration Report
| Intent: | Data Exploration Report – data exploration and preparation. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 100% |
| Length: | 2,000 words |
Minimum requirements
In order to pass the microcredential, a learner must achieve an overall mark of 50% or more.