Subject description
Introduction to Data Mining, Knowledge Discovery, and Big Data with coverage of Data Structures, role of Data Quality and per-processing, Association Rules, Artificial Neural Networks, Support Vector methods, Tree Based Methods, Clustering and Classification Methods, Regression and Statistical Methods, Overfitting and Inferential issues, Evaluation, Use of Data Mining packages with … For more content click the Read More button below.
Equivalence
INFO411 - Data Mining and Knowledge Discovery
Delivery
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Engagement hours
Contact Hours:2 hr lecture & 2 hr computer lab
Learning outcomes
On successful completion of this subject, students will be able to:
1.
Identify useful relationships and important subgroups in large data sets.
2.
Suggest appropriate approaches and solutions to given data mining problems.
3.
Plan and carry out analyses of large and complex data sets.
4.
Use parametric, non-parametric, and probabilistic methods to model data in various domains.
5.
Analyse and interpret results
6.
Use data mining software such as R as well as use relevant plugins and software packages.
7.
Analyse data mining algorithms and techniques.
8.
Understand the role and challenges of methods in Big Data applications.
9.
Identify and distinguish data mining applications from other IT applications.
10.
Describe data mining algorithms.
11.
Compare the applicability of data mining applications.
Assessment details
Individual Assignment
Individual assignment
Group project
Final Exam
Work integrated learning
Embedded WIL:This subject contains elements of "Embedded WIL". Students in this subject will experience activities that relate to or simulate professional practice as part of their learning.
Textbook information
No prescribed textbooks for this subject.