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ST5188 Statistical Research Project
创建日期：2024-05-23 16:57:03
所属分类：统计学statistics
标签： ST5188
Statistical Research Project

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ST5188 Statistical Research Project

– Course Guide –
Version 1.0
ST5188, which will be conducted in e-hybrid mode, is a project course. As such, there will
be no regular lectures or tutorials. Throughout the semester, students will work (in groups)
on their projects independently. However, there will be dedicated touch points with
facilitators including three lectures, consultation sessions (for each group) with the
lecturer, and TA support.
Given that ST5188 is a project course, it is evaluated as such. Different students may
contribute in different ways; according to their skills, abilities and project plan agreed upon
by the whole group. However, all students are expected to contribute similar efforts to the
project.
All the students in a group share equal responsibility for creating team spirit and making
the group work as a whole. Should a problem arise, each student must be willing to work
towards resolving the problem. Do not hesitate to ask your assigned TA for mediation;
should problems persist, the TA will escalate the matter to the lecturer.
ST5188 assessment components are as follows:
Contribution Due Date
Late Submission
(25% penalty applies)
Project Proposal 15% Feb 8th, 11:59pm up to 48 hours
Project Progress Report 5% Mar 15th, 11:59pm up to 48 hours
Project Presentation 15% Week 13 No
Peer Project Evaluation 5% Apr 16th, 11:59pm No
Final Project Report 60% Apr 16th, 11:59pm up to 48 hours
ST5188 will be graded on a completed satisfactorily / completed unsatisfactorily basis!
There will be three lectures (90mins each; conducted in LT26 and streamed via Zoom):
• Jan 9th (Mon), 5pm: ST5188 Introductory Briefing Session
• Jan 14th (Sat), 2pm: How to Write an Effective Capstone Project Proposal
• Jan 28th (Sat), 2pm: Data Science Project Best Practises
For course details and updates, please refer to the ST5188 Canvas page.
2 | P a g e M a r k u s K i r c h b e r g
Dept of Statistics and Data Science :: Faculty of Science :: National University of Singapore
ST5188 Summary
Students will have an opportunity to conduct basic research activities on a topic of interest.
The topics are either self-proposed, proposed by a faculty member, or real-world problems
from companies, research institutes, etc. Students will work on projects in groups of three
students (default). However, for real-world problems sponsored by companies, research
institutes, etc. smaller group sizes are permitted, but subject to approval by the lecturer.
During the course, each group of students will progress through three stages:
1) Formalise a project proposal (typically, weeks 2 to 4);
2) Conduct basic research activities (typically, weeks 5 to 13); and
3) Present their approach and findings (typically, week 13).
Throughout the semester, project groups will have the opportunity to get direct feedback on
their project progression via consultation sessions.
Learning Outcomes
A student will acquire many of the following skills:
• Conduct systematically an extensive literature search and review of a topic of interest.
• Review critically the papers read.
• Propose feasible research projects arising from a literature review.
• Perform coding in R or Python.
• Write a report summarizing all the findings.
• Present the findings in an oral presentation.
• Discuss the findings with classmates and the lecturer / TAs.
Pre-Requisite
• ST5201/ST5201X Statistical Foundations of Data Science; and
• ST5202/ST5202X Applied Regression Analysis
Modular Credit
4
Workload
0-0-0-8-2
Teaching Modes
E-hybrid mode.
This is a project course; there will be no regular lectures / tutorials. Throughout the semester,
students will work on their projects independently with the lecturer doubling as consultant.
Grading Basis
Completed Satisfactorily / Completed Unsatisfactorily (CS / CU).
• CS means scoring 50% or above; a CU grade will be awarded for scoring below 45%;
all scores in the range of 45—50% will be adjudged on a case-by-case basis.
3 | P a g e M a r k u s K i r c h b e r g
Dept of Statistics and Data Science :: Faculty of Science :: National University of Singapore
What to Expect: Example of Typical Activities (week-by-week)
Students are given the freedom to choose their own project groups, project topics, and
approach to work towards tackling the project. However, with such freedom comes greater
responsibility, accountability, and communication requirements (with your peers and
teaching staff). Below, you find a sample timeline of core activities and course deliverables.
Week Core Activities (Example) Deliverables (Example)
1 • Read up on ST5188
• Attend ST5188 introductory briefing session
• Form and register project group
• Attend lecture on “How to write an effective
capstone project proposal”
2 • Explore project topics
• Literature review (wrt. project topics explored)
3 • Formulate project scope, objectives / deliverables,
approach, success measures, and project plan
• Schedule first consultation with lecturer
• Attend lecture on “Data science project best
practises”
4 • Attend first consultation session with lecturer
• Finalise project proposal
• Commence project work
5 • Work on project • Project Proposal
6 • Work on project
• Schedule second consultation with lecturer
Recess Week
7 • Work on project
• Attend second consultation session with lecturer
• Complete first peer group evaluation form
• Peer Group Evaluation 1
8 • Work on project
• Write project progress report
9 • Work on project • Project Progress Report
10 • Work on project
• Schedule third consultation with lecturer
11 • Work on project
• Attend third consultation session with lecturer
12 • Work on project
• Plan for project presentation
13 • Present your project to lecturer, TAs, and peers
• Complete work on project
• Attend two peer project presentations and write
peer project evaluations
• Finalise final project report
• Complete second peer group evaluation form
• Project Presentation
• Peer Project Evaluations
• Project Report Submission
• Peer Group Evaluation 2
14 Reading Week
15 Examination Week 1
16 Examination Week 2
4 | P a g e M a r k u s K i r c h b e r g
Dept of Statistics and Data Science :: Faculty of Science :: National University of Singapore
Getting in Touch / Asking for Help / Receiving Feedback
Throughout group project activities, your lecturer will double up as your group’s consultant
and assist with brainstorming and decision-making activities; plan ahead to ensure that you
use this opportunity wisely. Consultation sessions (20 minutes each) commence in week 3
and are available until the end of week 12 (each group may book up to 3 such consultation
sessions). In addition, each group will be assigned a teaching assistant (TA) who should be
your first point of contact wrt. any queries related to the project or the course in general. If
and as necessary, TAs will escalate enquiries to the lecturer.
Besides consultation sessions and TA support, you will also receive detailed written feedback
for your first two assignments, your project proposal as well as your progress report, and
verbal feedback during the Q&A session of your group’s presentation during week 13.
Recommendations for consultation sessions
You are recommended to arrange for up to three consultation sessions with your lecturer;
the first such session MUST be completed before submitting your project proposal.
• You can either book an online consultation session (conducted via Microsoft Teams)
or an in-person consultation session (held in Blk S16, 6 Science Drive 2, Singapore
117546); please choose the mode that is most appropriate for all your group
members. A booking form will be made available via the ST5188 Canvas page.
• For online consultation sessions, group leads should ensure that the meeting link is
circulated to all group members well in advance of the agreed upon consultation slot.
• Consultation sessions are meant for you to get feedback and/or inputs from the
lecturer. As such, you should have an agenda on what topic(s) to discuss / question(s)
to raise. Ideally, you will run consultation sessions; your lecturer will then add-on and
keep an eye on the time.
• Since every group will work on a different project, you will need to set the context. For
this, we strongly suggest that you send a summary (limited to half a page or two
paragraphs) of your project idea, project progress, key discussion points or else (at
least 2 hours) PRIOR to the consultation session. That way, the lecturer can have a
pre-read, and you have the full 20 minutes of your session available for discussion.
• Be ready a few minutes BEFORE the start of your session. If some or all your group
members join / arrive late, you will lose valuable discussion time.

5 | P a g e M a r k u s K i r c h b e r g
Dept of Statistics and Data Science :: Faculty of Science :: National University of Singapore
Key Activity 1: Project Formulation & Project Proposal
ST5188 group projects will require you to apply current or emerging statistical concepts,
methods or techniques to an interesting application or real-world data sets. Each project
should include some form of analysis and some form of experimentation on real-world or
synthetic data sets.
Throughout group project activities, your lecturer will double up as your group’s consultant
and assist with brainstorming and decision-making activities. Each group MUST schedule and
attend their first consultation session with the lecturer prior to submitting the project
proposal.
The first key activity is comprised of two parts:
1) In-depth review / study of a statistical concept, method, or technique that falls under
the scope of the MSc (Statistics) by Coursework Programme:
• Statistical foundations of data science, applied regression, design of
experiments for product design and process improvements, nonparametric
regression, analytics for quality control and productivity improvements,
analysis of time-series data, multivariate data analysis, sampling from finite
populations, survival analysis, categorical data analysis, probability and
stochastic processes, statistical analysis of networks, spatial statistics, and data
mining.
2) Proposal of a group project related to your concept study from part 1).
Guidelines for part 1: In-depth review / study of a statistical concept, method, or technique
You are expected to learn BEYOND what was covered in prior coursework. You may think of
it as a literature review comprising of a technical concept summary, concept critique (pros
vs. cons, limitations wrt. assumptions made, practical feasibility or scalability of approach,
etc.), and initial brainstorming of at least two potential project opportunities (e.g., open
questions, better model, better algorithm, test on different data set, reformulation / removal
of assumptions, apply to different domain, etc.). Based on this, you will then work on the
second part, your group’s project proposal.
6 | P a g e M a r k u s K i r c h b e r g
Dept of Statistics and Data Science :: Faculty of Science :: National University of Singapore
Guidelines for part 2: Proposal of a group project
Your project proposal (up to 6 pages including a 2–3-page literature review / concept study)
should cover the following subjects:
• Project title (this can be a working title)
• Project introduction / motivation
• Problem statement or hypothesis
• Literature review / concepts study (2-3 pages)
• Project objective(s)
• Requirements (in terms of data sets [how to obtain / generate them], compute
resources, tools, etc.)
• Success measure(s) (in terms of evaluation, experimentation, testing, etc.)
• Project plan including key activities
o Note: Take into consideration the size of your group and time allotted for this
group project.
The second lecture (to be conducted on Jan 14th at 2pm) will focus on how to write an
effective project proposal. Please carefully review the lecture to ensure that you meet
expectations!
Project proposals are due by Feb 8th, 11:59pm. You are highly recommended to submit your
project proposals early (i.e., during week 4). We aim to return detailed project proposal
feedback five to eight working days after the respective proposal has been received. That is,
the earlier your submission is received, the earlier you will receive corresponding feedback!
Project proposal templates as well as submission details will be made available via the ST5188
Canvas page. You are only required to submit one project proposal per group.
7 | P a g e M a r k u s K i r c h b e r g
Dept of Statistics and Data Science :: Faculty of Science :: National University of Singapore
Key Activity 2: Project Progress Report
As a rough guideline, your group should have completed at least 50% of the work by the time
you submit your progress report. That being said, there may be circumstances under which
this is not feasible (e.g., in case your group decided to significantly revise the project’s focus
or objectives). However, such circumstances should be made known to your assigned TA /
lecturer as soon as they arise and not only during progress report submission. Thus, please
make sure that any significant project proposal amendments have been discussed during a
consultation session prior to project progress report submission.
In the project progress report, you should provide a summary of your progress thus far (i.e.,
demonstrate that you have completed 50% of the work) as well as a discussion of any
unforeseen challenges or difficulties encountered (you can limit yourself to discussing three
such challenges / difficulties). This report gives each group also a chance to re-evaluate initial
problem statements, objectives, and contributions (espc. for those groups who have been
recommended to revise such sections in the project proposal feedback) and make
amendments to the project scope / plan.
Your project progress report (up to 3 pages) should cover the following subjects:
- Project title
- Discussion of project progress (e.g., what has and what has not yet been finished;
difficulties / challenges encountered; and changes made to the initial problem
statement, objectives, assumptions, success measures, and/or contributions)
o We strongly encourage you to include an explorative data analysis (to
demonstrate that your chosen data-set(s) is/are suitable for your project
scope) unless you have already done so in your Project Proposal submission.
This also forms a good baseline for the discussion of challenges and difficulties
encountered (as they are often data pre-processing related in the early stages
of project work).
- Updated project plan for remaining / unfinished work items
Project progress reports are due by Mar 15th, 11:59pm. You are highly recommended to
submit your project progress reports early (i.e., during week 8). We aim to return project
progress report feedback five to eight working days after the respective report has been
received. That is, the earlier your submission is received, the earlier you will receive
corresponding feedback!
As a general guideline, you are encouraged to have completed your second consultation
session with the lecturer prior to submitting your progress report.
Project progress report templates as well as submission details will be made available via the
ST5188 Canvas page. You are only required to submit one project progress report per group.
8 | P a g e M a r k u s K i r c h b e r g
Dept of Statistics and Data Science :: Faculty of Science :: National University of Singapore
Key Activity 3: Project Presentation
Each team will be given a 30-minutes presentation slot during week 13 – corresponding
details such as venue and sign-up sheets will be announced via the ST5188 Canvas page. All
team members are required to attend. Presentations are time-constrained (please plan for
about 15 minutes of presentation time followed by 10 minutes of Q&A; we will reserve 5
minutes for set up and other contingencies). During your presentation time (about 15
minutes), we will not interrupt for questions; however, we will stop you after 20 minutes
should you exceed the presentation guidance by more than 5 minutes and move on to Q&A.
For the group presentation, there is no prescribed format; you may use slides, but you don’t
have to, you may ‘walk through’ your project deliverables, or else. You are not expected to
cover every aspect of your project during the presentation (there is not enough time for that);
instead, your presentation should give the audience an overview of what your project is
about, highlight notable challenges and/or achievements, and, ideally, motivate the
members of the audience to have a desire to read your final project report.
Each presentation will be attended by all members from two other project groups, one of the
TAs (but not necessarily the TA who was assigned to your group as primary point of contact),
as well as the lecturer. As such, please do not assume that the members of the audience are
familiar with your project; you will have to provide corresponding context at the beginning of
your presentation.
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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 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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ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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FN1024 MATH0094 MANG 6554 ECOS3003 ELEC6252 C2511C PHY328 EC1060 ECON32202 COMP4002-E1 MATH3063 EFIMM0124 QBUS2310 ECON4411 FUNDAMENTALS ACCT20001 18YX RPM COMP 2711 QBUS6600 BUSS5221 ST3189 FIT1047 MAT344 EGB375 MATH60013 MATH96011 COMP3557 MAST20029 ECM159 EAE1011 ENSCEN 105 SECTION A STA 141C SOSE 2022 PAM006 COMP0048 M40007 ISOM2500 CMPE 142 A06472 MCC150 ACST 8083 GEOG6087 ECON920 FINM7008 ENVENG 4110 COMPSCI 361 ENGRCEE 21 PHYS 1112 ACF2100 ECMM108 COMPS267F MAT00050M MAST30025 CSIT970 MSCI 224 INT202 PUBL0054 MSDM5004 BMAN 21020 BMAN21020 MGMT1002 MATH11150 LAWS7023 ACTL30002 CIVL3340 MATH1002 ECON 8069 COMPSCI 351 IBUS7302 STAT4528 CHEM1101 STAT3925 BUSN 7008 Comp 3120 ECOS2040 SWEN90010 ENGN6528 ECM158 ECON20022 COMP6452 Calculation CITS1401 S1889112 BUS 351 ACTL30008 FIT2091 ECON6002 IBUS6020 PHI 001 ECON30290 MATH7501 ECON10004 MFIN7017 ISYS90043 LAWS7012 ECON30025 COMP30023 COMM1190 ECOM20001 GSBS6009 STAT 337 EMET8002 ECOM90001 ECOM30001 FINC 2012 BUSINESS ENGCV512 ECO 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