42822 Advanced Data Analytics
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Credit points: 2 cp
Result type: Grade and marks
Requisite(s): 42821 Data Analytics Foundations
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.
Advanced Data Analytics develops skills in data classification and prediction by practical activities in decision tree induction, classification by support vector machine, ensemble methods and random forest, classification accuracy and identifying issues in prediction. Building from Data Analytics Foundations, the microcredential enables an exploratory data visualisation and evaluation of results.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply data mining and pattern discovery, and analysis skills in predictive analytics of 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)
Classification and Prediction:
- problems for classification and prediction;
- classification by decision tree induction;
- classification by support vector machine;
- ensemble methods and random forest;
- classification accuracy;
- issues in prediction;
- applications in /enterprises.
Assessment
Assessment task 1: Data mining in action
| Intent: | Data mining in action- classification and prediction |
|---|---|
| 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.