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CMT309 Computational Data Science
创建日期：2024-05-25 14:35:00
标签： CMT309
CMT309 Computational Data Science

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CMT309

Computational Data Science
Assessment Number : 2
Extenuating Circumstances submission deadline will be 1 week after the submission date
above.
Extenuating Circumstances marks and feedback return will be 1 week after the feedback re-
turn date above.
This assignment is the CMT309 Data Science Portfolio, which accommodates 70% of the total marks
available for this module. If coursework is submitted late (and where there are no extenuating circum-
stances):
1.) If the assessment is submitted no later than 24 hours after the deadline, the mark for the
assessment will be capped at the minimum pass mark;
2.) If the assessment is submitted more than 24 hours after the deadline, a mark of 0 will be given
for the assessment.
Extensions to the coursework submission date can only be requested using the Extenuating Circum-
stances procedure. Only students with approved extenuating circumstances may use the extenuating
circumstances submission deadline. Any coursework submitted after the initial submission deadline
without approved extenuating circumstances will be treated as late.
By submitting this assignment you are accepting the terms of the following declaration:
I hereby declare that my submission (or my contribution to it in the case of group submissions)
is all my own work, that it has not previously been submitted for assessment and that I have
not knowingly allowed it to be copied by another student. I understand that deceiving or
attempting to deceive examiners by passing off the work of another writer, as one’s own is
plagiarism. I also understand that plagiarising another’s work or knowingly allowing another
student to plagiarise from my work is against the University regulations and that doing so will
result in loss of marks and possible disciplinary proceedings1
Assessment
(1) You have to upload the files mentioned in Submission Instructions section below.
(2) Failing to follow submitted file names, and file types (e.g. naming your file p1.py instead of
P1.py) will have a penalty of 10 points from your total mark.
(3) The coursework includes different datasets, which are automatically downloaded. Since these
files are already with the markers, students do not need to submit these files back.
(4) Changing the txt file names, and developing your codes with those changed file names would
cause errors during the marking since the markers will use a Python marking code developed
with the original file names.
(5) You can use any Python expression or package that was used in the lectures and practical
sessions. Additional packages are not allowed unless instructed in the question. Failing to
follow this rule might cause to lose all marks for that specific part of the question(s).
(6) You are free to use any Python environment, or version to develop your codes. However, you
should fill and test your notebook in Google Colab since testing and marking process will be
done via Google Colab.
(7) If any submitted code for any sub-question fails to run in Google Colab, that part of the code
will be marked as 0 without testing the code in Jupyter, or any other environment.
(8) It is not allowed to use input() function to ask user to enter values.
(9) If a function is asked to be developed, the name and input arguments of that function should be
as the same as instructed in the paper.
Learning Outcomes Assessed
• Carry out data analysis and statistical testing using code
• Critically analyse and discuss methods of data collection, management and storage
• Extract textual and numeric data from a range of sources, including online
• Reflect upon the legal, ethical and social issues relating to data science and its applications
Criteria for assessment
Credit will be awarded against the following criteria. Different criteria are applied to pandas code
(using pandas outside of a function), function code, and figures obtained with matplotlib or seaborn
. pandas code is exclusively judged by its functionality. Functions are judged by their functionality and
additionally their quality will be assessed. Figures are judged by their quality and completeness. The
below tables explain the criteria.
Mark Functionality (80%) Quality (20%)
Distinction
(70-100%)
Fully working application that
demonstrates an excellent
understanding of the assignment
problem using relevant python approach
Excellent
documentation with
usage of docstring
and comments
Functions
Merit
(60-69%)
All required functionality is met, and the
application are working probably with
some minors’ errors
Good documentation
with minor missing of
comments
Pass
(50-59%)
Some of the functionality developed with
and incorrect output major errors
Fair documentation
Fail
(0-50%)
Faulty application with wrong
implementation and wrong output
No comments or
documentation at all
Mark Functionality (100%)
Distinction
(70-100%)
Fully working application that demonstrates an excellent
understanding of the assignment problem using relevant python
approach
Pandas
Code
Merit
(60-69%)
All required functionality is met, and the application are working
probably with some minors’ errors
Pass
(50-59%)
Some of the functionality developed with and incorrect output
major errors
Fail
(0-50%)
Faulty application with wrong implementation and wrong output
Page 3
Mark Quality and completeness (100%)
Distinction
(70-100%)
Excellent figures with complete and informative data and
formatting, labels, titles, and legends if appropriate
Figures
Merit
(60-69%)
Good figures with good formatting, labels, titles, and legends
Pass
(50-59%)
Acceptable figures with missing information, bad formatting,
labels, titles, or legends
Fail
(0-50%)
Faulty or missing figures
Mark Quality (100%)
Distinction
(70-100%)
In addition to the requirements for Merit, there is a scholarly
approach, including references to external resources or types of
biases not covered in class.
Ethics
Merit
(60-69%)
Significant discussion is provided, with deep mapping between
several or all sources of bias and the argumentation.
Pass
(50-59%)
Some discussion is provided, and shallow mapping between few
sources of bias and discussion is provided.
Fail
(0-50%)
Incomplete discussion, sources of bias no discussed or
discussed with major mistakes.
Feedback and suggestion for future learning
Feedback on your coursework will address the above criteria. Feedback and marks will be returned
within 4 weeks of your submission date via Learning Central. In case you require further details, you
are welcome to schedule a one-to-one meeting.
Submission Instructions
Start by downloading P1.ipynb, and P2.ipynb from Learning Central, then answer the following
questions. You can use any Python expression or package that was used in the lectures and practical
sessions. Additional packages are not allowed unless instructed in the question. You answer the
questions by filling in the appropriate sections in the Jupyter Notebook.
Your coursework should be submitted via Learning Central by the above deadline. You have to upload
the following files:
Description Type Name
Your solution to part 1 Compulsory
One jupyter notebook
(.ipynb) file
P1.ipynb
Your solution to part 2 Compulsory
One jupyter notebook
(.ipynb) file
P2.ipynb
Make sure to include your student number as a comment in all of the Python files! Any deviation
from the submission instructions (including the number and types of files submitted) may result in a
reduction of marks for the assessment or question part.
You can submit multiple times on Learning Central. ONLY files contained in the last attempt
will be marked, so make sure that you upload all files in the last attempt.
Staff reserve the right to invite students to a meeting to discuss the Coursework submissions.
Page 4
Part 1 - Text Data and Ethics (45 marks)
This part covers the course content of weeks 1 to 4. It is advised students to complete this
part by the end of Week 5, to better plan your time and leave enough preparation time for the
second part of the assignment!
In this question you will write Python code for processing, analysing and understanding the social
network Reddit. Reddit is a platform that allows users to upload posts and com-
ment on them, and is divided in subreddits, often covering specific themes or areas of interest (for
example, world news, ukpolitics or nintendo). You are provided with a subset of Reddit with posts
from Covid-related subreddits (e.g., CoronavirusUK or NoNewNormal), as well as randomly selected
subreddits (e.g., donaldtrump or razer ).

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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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MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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