Hong Kong Baptist University Department of Computer Science COMP3925 Data Analysis Studio 2nd Semester, 2019-20 Individual Assignment (Data collection exercise – 25%; Presentation – 10%; Half-done report – 10%; Final report – 45%;) The objective of this assignment is to let students experience how to conduct an empirical study on his/her own: from topic selection, data selection, data extraction, and data analysis, and result interpretation and presentation. Submission Guideline 1. Each student can check the similarity of individual submissions on his/her own on the Moodle for one time only; and submit the version for grading for one time only too. Make sure that you submit your work to correct submission box. No change will be allowed. 2. Submit a softcopy (in .doc format) of your data analysis report to the Moodle by 27 February, 2020 (half-done version) and 19 March, 2020 (final version) respectively. 3. Submit a softcopy of a user manual or a video describing the steps to collect data for your study by 26 March, 2020. 4. Upload your data file(s) used in your study to the Moodle by 19 March, 2020. Assume that we can compile the same results as shown in your report with the data submitted and method suggested. 5. Penalty will be imposed on the act of academic dishonesty such as plagiarism. Introduction You are required to write a data analysis report that will be of interest of either: 1) Kindergarten Parents in Hong Kong, 2) Investors in Hong Kong, 3) People who will kick start online commerce in Hong Kong, or 4) Hong Kong Citizens. Your findings, and interpretations have to be justifiable by literatures, news, quantitative data etc. 1. Identify a topic that is of interest of one group of people as provided. You may email Jean for comment once you have a topic in mind optionally. 2. Identify and collect data that are related to the topic. Based on the data available to you, raise questions that the data can help to answer and vice versa. 3. Conduct data analysis, and interpret the results with justifications (e.g. literature support, if any). 4. Present your findings using an appropriate format (e.g. good use of graph/table/text) in your report. 5. Cite references using APA format, if any. 6. Write a report including at least the sections of: background of your study, your proposed model/hypotheses (if you are taking a research approach), study design, analysis result and interpretation, and conclusion. This part will be graded as the data analysis report – half-done version (10%) and final version (45%). 7. Present the key findings in your study (at most 8 minutes per student) in class on 19 or 26 March. In worst case that we cannot conduct on-campus class, the format will be changed to taking video presentation for submission. Course instructor will update students accordingly if the format need to be changed as early as possible. 8. Prepare a user manual or take a video, reporting the data collection procedure in detail. (e.g. Prepare a user manual or take a video presenting the steps of data collection in your study. If you have written a program to retrieve the data, please submit the source code(s) and/or demonstrate the program in the video.). This part will be graded as the data collection exercise (25%). Marking Rubric Data Analysis Report (10% + 45%) • Originality and Contributions of the topic to the beneficiary • Appropriateness of data selection • Appropriateness of data manipulation done (e.g. data transformation, cleaning) • Breath and depth of your analysis • Validity of the analysis result and interpretation • Appropriate use of analysis tool(s) • Appropriate use of presentation method(s) • Any interesting findings? • Organized writing? • Progress is good? (for half-done version only) Presentation (10%) • Audience knows what you are doing in your study. • Audience knows the key findings in your study. • Presentation skills are good? Data Collection Exercise (25%) • Can demonstrate your capability in collecting relevant data for your study? • Are you using appropriate means to collect data? • Can present the steps clearly? • Difficulty level in data collection • Effort taken
