Introduction to Data Mining
Subject code: MA5810:03
We have entered the era of big data. The ability to take data, to be able to understand it, to process it, to extract value from it, to visualize visualise it and, to communicate it – will be a hugely important skill in the next decades. Data mining is a technology that discovers patterns from large volumes of data by using data analysis methods and algorithms. Data mining plays an important role in data science and has been widely applied in many practical applications such as business, medicine, science and engineering.
This subject will provide you with a range of widely used algorithms and techniques to automatically extract patterns and models from data. You will learn classic techniques for the most common descriptive and predictive tasks in data mining, including clustering, outlier detection, and classification. The algorithms and techniques will be studied both at the conceptual, as well as at the practical levels. A software package will be adopted for hands-on data mining in real data sets.
- Integrate and apply an advanced body of practical, technical, and theoretical knowledge, including understanding of recent developments and modern challenges, in Data Science and its application
- Retrieve, analyse, synthesise and evaluate complex information, concepts, methods, or theories from a range of sources
- Plan and conduct appropriate investigations of data sets by selecting and applying qualitative and quantitative methods, techniques and tools, as appropriate to the data and the application
- Analyse requirements, and demonstrate effective applications of appropriate computing languages and computational tools for data acquisition, queries, management, analysis and visualization
- Identify, analyse and generate solutions for complex problems, especially related to tropical, regional, or Indigenous contexts, by applying knowledge and skills of data science with initiative and expert judgement
- Communicate data concepts and methodologies of data science as well as the arguments and conclusions of the application of data science, clearly and coherently to specialist and non-specialist audiences through advanced written and oral English language skills and a variety of media
- Critically review ethical principles, issues of data security and privacy, and where appropriate regulatory requirements and cultural frameworks to work effectively, responsibly and safely in diverse contexts
- Reflect on current skills, knowledge and attitudes to manage their professional learning needs and performance, autonomously and/or in collaboration with others
- Apply knowledge of research principles, methods, techniques and tools to plan and execute a substantial research-based project, capstone experience and/or piece of scholarship.
Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports.Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period.
This is one of the interdisciplinary subjects studied in the online Master of Data Science.
Please note, course structure and content are subject to change. For information on all course subjects download the course guide.