Question

write reflection about data mining theory and applications course

Answer 1

The field of Knowledge Discovery and Data Mining (KDDM) has emerged in response to the practical need to analyse huge quantities of data collected in commerce and industry. KDDM has evolved as an interdisciplinary field at the intersection of machine learning, statistics, artificial intelligence, and database systems. To fully utilize the power of data mining, industry and commerce need a steady supply of highly trained data mining analysts. The universities in Australia and around the world have responded to this need by creating specialized units or complete courses devoted to knowledge discovery and data mining. The purpose of the Emerging Issues in Computing unit was to introduce students to new areas of computing. The content of the unit could vary from year to year, depending on which emerging area was being taught. In 2003 the whole unit was devoted to data mining. In the following year, the author created a new unit named Knowledge Discovery and Data Mining (KDDM). The unit was designed as an elective unit for the Software Engineering major in the coursework masters degree Master of Computing. Masters students from other majors also were allowed to enroll, provided that they satisfied the prerequisite requirements.

One important issue to consider when designing a data mining unit is the technical level of the unit. A good understanding of data mining algorithms requires a solid background in mathematics, statistics and algorithms and data structures. Such background knowledge is normally provided in computer science degree programs but unfortunately not in many information systems and information technology programs. For more technical disciplines such as computer science and engineering, the data mining units should include technical details of data mining concepts and algorithms as well as projects involving industrial or scientific applications of data mining. Computer science students could also be given programming tasks to implement some of the data mining algorithms. Employment opportunities in data mining range from technical positions requiring knowledge of statistics, computer programming, machine learning and artificial intelligence to non-technical positions in business analysis. There is clearly a need for data mining units and courses targeted to students from different fields.