Data Mining Project

The College of Arts and Sciences is a fast-growing entity at the University of Diploma Printing in Romanigstan. Within this College, the Department of Arts consists of twelve full-time faculty, six adjunct instructors, and approximately fifteen graduate teaching assistants. Many faculties are nationally and internationally renowned and are well-established professional artists and designers with numerous exhibitions, commissions, and awards to their credit. The Department currently has 240 undergraduate majors within its BA and BFA programs spanning a number of areas including art of walking, retail floristry, puppetry, adventure recreation, fishing sciences, ant nesting habits, coin slot technology, and turfgrass management. It offers approximately 50 different courses to art and non-art majors alike. In addition, art education students enroll in a significant number of art courses for their Bachelor of Science in Art Education degree offered cooperatively through the Department of Learning, Teaching, and Curriculum. You, as a new faculty in the Department of Arts, were assigned to the huge task of reviewing and redesigning the BFA curriculum. Your help is needed for the following reasons: Stagnant enrollment in the last five years Changes in student demographics and interests. Shifts in disciplinary approaches The main tasks of curriculum redesign are Reducing the number of credit hours from 39 to 33 Defining concentration areas for elective courses Reducing the number of offered electives To evaluate the opportunity of grouping elective classes in concentration areas, the chair of the committee provided you with the following pieces of information (click the links to access the data) List of current courses offered List of all student enrollments for the last three years Project Problem What elective courses should be retained in the course offerings? Recommend sets of three or four courses that can be grouped in concentration areas. Notes Your answers should be based on analyzing the provided data. Explain your answer and support them by results and/or screen captures. Try to follow the Guidelines for Data Mining Experiments from your term project. Structure Section 1: Objectives of the experiment, problem definition Section 2: Description the dataset and initial preprocessing if any Section 3: Preprocessing, including an explanation of the necessity of the preprocessing task and quantitative report on the result of each step. Section 4: Modeling, including a brief description and the motivation of the application of each algorithm Section 5: Evaluation, including graphs of experiments, result tables, and explanation of the results Section 6: Summary of results, including discussion of the overall performance, limitations, and difficulties encountered. Also, add a paragraph about conclusions. Particularly good discussions may result in 2% extra credit. Keys to Success Spend some time to understand the subject and the data. Follow the steps in the CRISP method as described in the first lesson. Look for patterns in the data. Formulate hypotheses and try to prove or disprove them through experiments. Make sure you follow citation style and avoid plagiarizing other sources. Use charts to support your claims; do not provide screenshots of experiments. Explain in depth each chart/figure used. The software we are using is KNIME can you help on this?