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Data Analytics ECS648U/ ECS784U/ ECS784P
创建日期：2024-04-06 17:37:48
标签： ECS648U
Data Analytics

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Python coursework specification

Data Analytics ECS648U/ ECS784U/ ECS784P
1. Important Dates

General information:
i. When submitting coursework online you receive an automated e-mail as proof of
submission. Turnitin receipt does not constitute proof of submission. Some students
will sometimes upload their coursework and not hit the submit button. Make sure you
fully complete the submission process.
ii. A penalty will be applied automatically by the system for late submissions.
a. Your lecturer cannot remove the penalty!
b. Penalties can only be challenged via submission of an Extenuating
Circumstances (EC) form which can be found on your Student Support page.
All the information you need to know is on that page; including how to submit
an EC claim along with the deadline dates and full guidelines.
c. If you submit an EC form, your case will be reviewed by a panel and the panel
will make a decision on the penalty and inform the Module Organiser.
iii. If you miss both the submission deadline and the late submission deadline, you will
automatically receive a score of 0. Extensions can only be granted through approval
of an EC claim.
iv. Submissions via e-mail are not accepted.
v. It is recommended by the School that we set the deadline at 10:00 AM. Do not wait
until the very last moment to submit the coursework.
vi. Your submission should be a single PDF file.
vii. For more details on submission regulations, please refer to your relevant handbook.




2

2. Coursework overview

The coursework is based on the Python lectures and labs and can be completed individually or
in a group of up to three students. You are free to choose or collate your own dataset and apply
two data analytic techniques to a real-world problem of your choice. In brief, you will:

i. Decide whether to form a group or not,
ii. Agree on the application area,
iii. Investigate the are and prepare the Introduction along with a short literature review,
iv. Collect data,
v. Clean and pre-process the data (if necessary),
vi. Apply two data analytic methods to the data,
vii. Present and discuss results,
viii. Draw conclusions,
ix. Finalise the report covering all of the above (see Section 4 for marking criteria).

You should address a data-related problem in your professional field or a field you are
interested in. If you are motivated in the subject matter the project will be more fun for you and
you will likely produce a better report. The same applies to the data analytic methods; i.e., you
are free to apply and test the methods of your choice from those covered in the Python labs or
in the Python lectures.


Please note:

i. This module is available to students with and without computer science background.
The labs provide step-by-step tutorials on machine learning with Python. Anyone
should be able to follow these tutorials to analyse some data irrespective of previous
academic background.
ii. Projects can be done individually or in groups of up to three people. If you form a
group, it is always a good idea include people who have different skills so they can
be assigned to different parts of the project (e.g., a coder, an analyst, a person to do
the literature review, etc).
a. Some students will not be able to join a group due to work commitments or
other reasons. This is not a problem since the coursework can be completed
individually.
b. Please do not send an e-mail asking if it is acceptable to form a group of more
than three people; it is not acceptable.



3

3. Deliverables

The coursework deliverable takes the form of a mini conference paper. The report shall have a
maximum length of 10 pages excluding References and Appendices. Font size should not be
lower than 11 and Page margins should not be lower than 2 (these restrictions do not apply to
the References and Appendices).
Reports should be written with a technical audience in mind. It should be concise and clear,
adopting the same style you would use in writing a scientific report. Some of the components
your report may include:

i. Problem statement and hypothesis.
ii. Description of your dataset and how it was obtained.
iii. Description of any data pre-processing steps you took.
iv. What you have learned from exploring the data, including visualisations.
v. How you chose which features to use in your analysis.
vi. Details of your modelling process, including how you selected your data analytic
methods as well as the model through validation.
vii. Your challenges and successes.
viii. Key findings.
ix. Possible extensions or business applications of your project.

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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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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 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