ST5188 Statistical Research Project
(AY 2022/23, Semester 2)
– 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.
During the Q&A session, questions may be asked by any of the members in attendance.
9 | 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 4: Peer Evaluation
Each student will have to participate in two types of peer evaluation activities:
1) Peer group evaluations: At the beginning of week 7 and after the final project report
submission, each individual student will be asked to provide feedback on the
performance / contribution of his/her team members as well as a self-evaluation.
Peer group evaluation feedback may be taken into consideration should there be
significant differences in contribution by the group members.
2) Peer project evaluation: During week 13, each group will be asked to (virtually) attend
two other group project presentations; all group members are required to attend. The
attending groups of students will be asked to complete a feedback / evaluation form
(e.g., provide a brief summary, three key achievements, and three constructive
feedback items) and are strongly encouraged to ask questions – but should do so only
during the allocated Q&A slot after the corresponding presentation has completed.
Peer evaluation / feedback forms will be made available via the ST5188 Canvas page.
10 | 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 5: Final Project Report
The final project report must not exceed 10 pages (using the final report template, which will
be made available via the ST5188 Canvas page). It should be a concise reflection of your
group’s project work; content may cover the following aspects:
- Project title
- Abstract
- Detailed problem description (including context, problem statement, objectives, and
assumptions)
- Related work
- Description of data collection and pre-processing processes
- Description of the concept, method, model, algorithm, and/or technique that is/are
at the core of your project
- Discussion of key challenges
- Evaluation of your experiments / results / findings (including lessons learnt)
- Recommendations for next steps or follow-on efforts
- Outline of each team member’s contributions
- List of references (using either APA version 6 or 7, Chicago or IEEE formatted references).
Your final report should be self-contained (i.e., do not assume that the reader is familiar with
any of your prior submission documents). We will evaluate and assess the final report as an
individual / stand-alone document.
While there is a 10-page limitation, you may add supplementary details (e.g., a full description
of all the variables covered in your dataset, additional or enlarged versions of tables or figures
that are hard to read in the main body) in the appendix. In addition, your reference list may
extend beyond page 10 as long as the first reference appears on page 10 itself.
In addition, you will be asked to submit your code along with a documentation detailing how
to replicate your work.
Final project reports are due by Apr 16th, 11:59pm. Submission details will be made available
via the ST5188 Canvas page. You are only required to submit one final project report per
group.
Note: Final report submissions will be examined for plagiarism! You are strongly encouraged
to self-inspect your final report documents prior to submission. As a general guidance, your
final report’s similarity score must be below 20% overall with no individual paper contributing
more than 2% to the overall score.
End of Course Guide – ST5188 Statistical Research Project