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MN3515 Business Data Analytics Assignment
创建日期：2024-05-04 16:34:16
标签： MN3515
Business Data Analytics Assignment

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MN3515 Business Data Analytics Assignment 


You will use R to mine actual data for a problem of interest. These could be data from a problem from your current
job if you have one, something of interest to the School of Management or College, data acquired from the web,
etc. (there are suggestions as to places where you can find relevant data on the electronic reading list for this
course). You will design the data mining task, mine the data, and describe your results. You also will research
existing solutions to the problem, if any have been proposed or documented. Your own data and results need not
be on a par with actual industry results; the goal is for you to get as realistic a hands-on experience as possible,
given the constraints of what you have learned.
In writing up/presenting your research, think of yourself as an analyst employed by or retained by a company (large
or small) or by a funding source (e.g., a venture capital (VC) firm or incubator), who wants to understand the state
of the art for using data mining for the task in question. Review what has been done to date on your problem.
Consider as an example predictive analytics for on-line advertising: A VC firm considering funding on-line ad
networks or ad-tech start-ups would need to understand the state of the art in using data mining for targeting on-
line advertising, when considering an idea for applying data mining. Don’t worry too much about coming up with
a novel idea. It is more important to develop the idea well (within the scope of what we’ve discussed in class).
You should use the CRISP-DM data mining process to structure your research and report. Keep in mind that it may
be ineffective simply to proceed linearly through the steps, and this may need to be reflected in your analysis. You
should interact with me from the preparation of your initial ideas through to the preparation of your report, as a
consultant would interact with a firm or funding source in preparing a research report. Use your imagination, prior
experience, or ask for help to fill in any gaps between the material available and what you would be able to find
out if you actually could interact with the client firm.
This assignment will have a phased submission of work, as follows:
Submission 1: On Friday 4 March 2022, you will submit a proposal for your project via Moodle. This should give as
much detail as possible on your ideas, so that I can give you brief feedback as to whether your proposal is viable.
Include in your proposal your ideas about: What is the exact business problem? What precisely is the data mining
problem? Is it supervised or unsupervised? What data will you be using and where will you obtain it? What is a
data instance? What might be the target variable? What features/variables would be useful? How exactly would
it add business value? And so on. Please include a link to the data set you will be using – this is absolutely vital.
Submission 2: On Friday 29 April 2022 you will submit your final report which should be about 1500 words, plus
any appendices you would like to include. Use external sources where appropriate, and provide clear citations and
bibliography. You should also submit your data file and a working R script which I can run against it.
You will get the most out of the project if you interact with me during the development of your ideas. Please feel
free to talk to me about your ideas as often as you’d like either in workshops in the second half of term or in my
online feedback and guidance hours. Or email me with specific questions/problems you are having – please include
your complete R script file and data file or link to it so that I can answer your question easily.
While we often learn coding by copying and editing code written by others, there is a limit to how much copying
you can do for this assignment. You may copy code from any of the workshop notes. You may also copy code
snippets (a line or two at a time) from elsewhere so long as you have to edit them in some way to refer to your
dataset. You should not copy code from a source which is working with the same dataset that you are using – I
will regard this as plagiarism, and you will then have to take the consequences.
Your report should include the information detailed below, in approximately the order given. Be as
precise/specific as you can.
Business Understanding (take this seriously)
• Identify, define, and motivate the business problem that you are addressing.
• How (precisely) will a data mining solution address the business problem?
(NB: I’d like to see a good definition/motivation of the business problem and a precise statement of how
a data mining solution will address the problem. It’s not so important that the hands-on results match
perfectly. It’s more important that you have the experience of working through a realistic problem
definition.)
Data Understanding
• Identify and describe the data (and data sources) that will support data mining to address the business
problem. Include those aspects of the data that we talk about in class and/or in the quizzes. This should
include some exploration of the data, such as:
o Summary statistics
o Visualisation using graphs/charts
Data Preparation
• Specify how these data are integrated and prepared to produce the format required for data mining.
(NB: data preparation can be time consuming. Get started early. Talk to me if you need advice.)
Modelling
• Specify the type of model(s) built and/or patterns mined.
• Discuss choices for data mining algorithm: what are alternatives, and what are the pros and cons? How
did you evaluate each of the models you used? Which is the best-performing model?
• Discuss why and how this model should “solve” the business problem (i.e., improve along some
dimension of interest to the firm).
Evaluation
• Discuss how the result of the data mining is/should be evaluated. How should a business case be
developed to project expected improvement? ROI? If this is impossible/very difficult, explain why and
identify any viable alternatives.
Deployment
• Discuss how the result of the data mining will be deployed.
• Discuss any issues the firm should be aware of regarding deployment.
• Are there important ethical considerations?
• Identify the risks associated with your proposed plan and how you would mitigate them.

MARKING CRITERIA
The submitted and assessed part of this coursework is a report together with R code and data files, rather than an
academic essay. Thus, the marking criteria are different from those usually required for an academic essay. Your
assignment will be assessed on the criteria shown in the rubric on the next page:
• The percentage given in the leftmost cell of each row shows you the percentage of the final mark available
for that criterion
• The % shown in the topmost cell of each column shows you the range of final marks you would achieve if
you were awarded marks in this column for all criteria
• Your feedback will include a mark for each criterion enabling you to see exactly where you gained/lost
marks
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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