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讲解 IOM209 Business Intelligence 2nd SEMESTER 2023/24辅导 C/C++程序

IOM209

2nd SEMESTER 2023/24 IndividuaI Report Brief

BSc Information Management and Information Systems

Business InteIIigence

Coursework : Individual Report

Your research report will be a comprehensive analysis of a single topic on business intelligence and data analytics. You will need to carefully examine why the topic chosen is important, the research that has been done already (to put your work in context), the data source / data set you select, the data exploration and analysis process, the results and conclusion. You may select a data set from but not limit to the following data sources:

1. https://www.kaggle.com/

2. https://aistudio.baidu.com/competition

3. http://archive.ics.uci.edu/ml/

Each project case (data set) usually provides a short description with attribute information.

Each project will likely have a different focus and structure. This report will count for 70% of your final mark. There are 100 total marks available.

Requirements:

 The report must be less than 3000 words. This does not include appendices (if needed), but the appendices should not be longer than 10 pages. This will require you to demonstrate the succinct (compact) writing skills that are required in writing executive summaries in the industry.

 The report should have page numbers inserted.

 The report must be submitted by 11:59 pm, Sunday, 26 May 2024 ( China Standard Time, UTC/ GMT+8) on L earning Mall Online.

 This should be structured as an academic style. research paper using Harvard referencing ( https://libguides.lib.xjtlu.edu.cn/xjtlureferencing ). There are several examples of such style. already provided throughout the course. You may want to consult with the Language Centre for help with this as well .

 Plagiarism will not be tolerated and will be dealt with in the standard way. Your report should include the URLs of the data set and any other on-line sources that you have used.

Report structure:

The report may have different structure. A sample structure is as below:

Title Page

Table of Contents

1. Executive Summary

2. Introduction / case background

3. Relevant research / literature

4. Data understanding / exploration / preparation

5. Modelling / methods / data analysis

6. Results / interpretation / discussion

7. Conclusion, limitation and future work

8. References

9. Appendices (if any, such as the core code, evidence of achievements)

Some general issues:

 In a case study it is crucial that you integrate relevant theory from the course and evidence from the case. Failure to attempt to integrate theory will lead to severe mark reduction or failure.

 Referencing of all non-original material is essential. You will lose marks for poor referencing.

 Check your completed work for internal consistency.

 Try not to be overly descriptive. Remember you are trying to identify, analyze, and solve the problems of the case using the relevant theories from the course - not just repeating what the references, or case information, has said.

Marking:

The assessment will comprise of the following criteria:

1. Report presentation (20%)

The extent to which the assignment represents an effective report. This will be judged on:

Appearance: Is a word count included at the end of the report? Is it within the specified amount?

Structure: Does the report follow the conventions of the format? Does is have a clear introduction, explaining how it answers the questions? Do the sections of the report develop ideas in a logical sequence?

Spelling and grammar: Are all words spelled correctly and is the meaning of sentences clear?

Referencing: Have appropriate references have been included in the report. Has a recognized referencing system has been used for notation? Harvard-style. referencing is required here.

2. Use of relevant theory (20%)

Has the right theoretical content / modelling method been chosen as the basis for solving the problem? Is the theory that is selected significant to the questions?

3. Analysis Process (40%)

This measures the extent to which students conduct a systematic data analysis process, by combining relevant modelling method / theory with the information provided in the case. The process includes data understanding, preparation, exploration, modelling, analysis, etc. Other evaluation criteria will be the quality and accuracy of the research and analysis; preparedness, usefulness, creditability.

4. Result interpretation / conclusion (20%)

This measures the presentation of the results in an elegant and very informative way, including the use of visualization methods to show the results clearly, the evaluation of the model, and managerial recommendation.

Grading Criteria:

First Class (70 – 100%)

Makes many insightful connections between real world situations and theoretical considerations.

Makes a thorough study on the selected topic.

Explores and analyses the questions in an in-depth way.

Presents in an elegant and very informative way.

Demonstrates the thought clearly.

Writes fluently, with evidence of a highly developed capacity to structure work systematically and argue logically.

Upper Second Class (60 – 69%)

Makes an appropriate study on the selected topics.

Explores and analyses the topic following a systematic procedure. Comprehensive knowledge of concepts and theories.

Employs appropriate application of theory and experience to the questions.

Demonstrates ability to inter-relate concepts and ideas.

Shows some originality in approach and awareness of scope and limitations.

Answers questions in a systematic and coherent manner.

Lower Second Class (55-59%)

Shows evidence of knowledge of concepts and theories.

Explores and analyses the questions, but lacking depth and/or details.

Addresses the main issues appropriately.

Presents the answers clearly without error.

Presents the work in a structured form. but arguments are weak in places.

Third Class (50-54%)

Shows Evidence of uncritical knowledge of main concepts and theories.

Attempts to relate theory to practice with limitations and relies on personal opinion or assertions.

Shows limited evidence of data analytical or theoretical skill.

Presentations and structure are weak in several places.

Fail (0 – 49%)

Shows some knowledge of main concepts and theory but major omissions and/or misunderstandings.

Styles and structure are weak and overly descriptive.

Has considerable limitations in ability to present analysis.









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