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BMAN60422 DATA ANALYTICS FOR BUSINESS DECISION MAKING
创建日期：2024-05-29 15:26:34
标签： BMAN60422
DATA ANALYTICS FOR BUSINESS DECISION MAKING

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BMAN60422

DATA ANALYTICS FOR BUSINESS DECISION MAKING
Use your Student ID as the file name of your submission
Answer ALL questions
You have 48 hours to complete this examination, but it is expected that you spend
no more than three times the duration of the original examination completing it.
You must not exceed the specified word limit for short-answer questions. This is a
maximum word limit (+10% does not apply). Markers will be instructed that they do
not have to mark anything beyond the words limit.
You will not be penalised for answers that are shorter than the limit; answers will be
given credit for being comprehensive, rather than for being a certain length.
Electronic calculators may be used in accordance with the University regulations

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SECTION A. Multiple-choice questions - WITHHELD
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SECTION B. Short-answer questions (your answer to each question in this section should
be no more than 150 words in length)
4. Introduce five different ways of reducing the number of variables in predictive
modelling, and discuss briefly their main strengths.
(10 marks)
5. The following table gives a sample data set of the churn management for a telecom
company, where the target variable Churn_flag indicates if the customer has
churned (Churn_flag=1) or not (Churn_flag=0), and the last two columns provide the
classification results from a decision tree model. Comment on the performance of the
decision tree model through constructing a confusion matrix, and plot the
corresponding ROC curve.
Actual data Decision tree model
Customer ID Churn_flag
Predicted:
Churn_flag = 1
Predicted:
Churn_flag
1 1 0.95 1
2 1 0.90 1
3 1 0.75 1
4 0 0.65 1
5 0 0.55 1
6 1 0.50 0
7 1 0.45 0
8 0 0.30 0
9 0 0.25 0
10 0 0.10 0
(10 marks)
6. For the data shown below, briefly discuss whether the regression model or the
neural network would be considered as a better modelling choice.
(10 marks)
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7. Use two appropriate sketches to explain how the Silhouette Width can be used to
support the following tasks:
- Determination of the number of clusters.
- Assessment of the quality of a given partition, with consideration of the
assignment of individual data points.
(10 marks)
8. At a high level, explain the concept of topic modelling, as implemented in the SAS
software. What does topic modelling tell us about a document collection and about
each document individually? What does a topic represent? How can we validate the
results of topic modelling?
(10 marks)
9. Describe the four V’s of big data, and use examples to explain the challenges each
of these poses for traditional data analytics.
(10 marks)
SECTION C. Short-essay question (your answer should be no more than 300 words in
length)
10. Reflect on the key steps you have taken to complete the group coursework project
for this course unit, in line with the cross industry standard process for data mining
(CRISP-DM) framework, and discuss briefly the limitations of the data analytics
techniques you have applied.
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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 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 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