COMP0035 specification

COMP0035 Coursework 01 2024 Coursework specification

1. Table of contentsIntroductionCoursework specificationGetting startedGeneral requirements and constraintsSection 1: Data exploration and preparation

Section 2: Database design and creation

Section 3: Tools

2. IntroductionThe aim of the combined coursework in this module is for you to select and apply some of the relevantsoftware development and data science techniques that are used in a typical project lifecycle.Coursework 1 focuses on data preparation and database design.Coursework 2 continues from coursework 1, focusing on requirements, application design and testing.hisdocument specifies coursework 1 which is worth 40% of the assessment marks available for the

odule. This is an individual coursework.

ou will submit a written report; and a repository of code files that combined meet the requirementsdetailed in this specification.Aim to make progress each week of first five weeks of the module, in line with module’s teaching activities.Coursework specification3.1. Getting started

1. Select a dataset using the ‘group’ selection task in Moodle Week 1 (https://moodle.ucl.ac.uk/mod/view.php?id=6089982). Each ‘group’ option is associated with a data set. ‘Groupselection’ is a Moodle term for the type of task, the coursework is individual.Accept a GitHub classroom assignment. This creates the repository. Instructions are also given inTutorial 1.

1. Login to GitHub.com.
2. Click on the GitHub classroom link (https://classroom.github.com/a/zqVIaThf)
3. Accept the assignment.
4. If prompted, accept to join the comp0035-ucl organisationPage 1 of 103. Download the dataset for your group choice and add it to your repository. Use the links in Moodle(Resources > Datasets). For files > 25MB use GitHub largefile storage (https://docs.github.com/en/repositories/working-with-files/managing-large-files/about-git-large-file-storage).

3.2. General requirements and constraints

• Compile all written work into a single report in either PDF or Markdown format. Name thedocument coursework1.

• The report supports the code and techniques used in the coursework. It is not an essay, be succinct.There are no word limits.

• Demonstrate regular use of source code control using GitHub. Create the repository using theGitHub classroom assignment. Keep the repository private. Keep the repository in the uclcomp0035 organisation.You must use the data set allocated to you on Moodle.

• This is an individual coursework. Do not collude with other students using the same data set.Use of code AI tools is permitted when writing code. UCL recommends using Microsoft Copilot(https://liveuclac.sharepoint.com/sites/Office365/SitePages/Bing-Enterprise-Chat.aspx) using yourUCL credentials. This must be stated in the ‘References’ section.

• Use relevant techniques from the course, or from data science and/or software engineeringprocesses. Provide references for techniques not included in the course material.

• Diagrams can be hand-drawn and scanned. Using software to draw them does not increase marks.

3.3. Section 1: Data exploration and preparation The purpose of this section is:

• to use python pandas to describe the data set structure and content; and as a result demonstratehat you understand the data set.to use python pandas prepare the data for later use in developing applications. The data youprepare will be used in COMP0034 coursework to create charts in a dashboard app.

• to demonstrate that you can write code that is reusable and understood by other developers.
• to demonstrate that you can apply relevant software 代寫COMP0035 specification engineering and data science techniques.Code quality is also assessed.Use only Python and pandas. matplotlib may be used where pandas DataFrame.plot() (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html) is not sufficient.Create charts where they support your exploration and preparation; but do not focus on the visuaaesthetic as this is not assessed.You may need to prepare the data in order to complete the exploration and hence your code may notneatly split between 1.1 and 1.2. This is OK, the code structure does not need to exactly match the reportstructure.3.3.1. Section 1.1 Data exploration

1. Code: Write python code to explore and describe the data structure and content. Including, butnot limited to, size, attributes and their data types, statistics,distribution of the data, etc.Consider potential data quality issues.

2. Report: Describe the results of your exploration of the data. Do not include the code in the report.

3.3.2. Section 1.2 Data preparation

1. Report: Briefly describe a target audience and state at least 3 questions that they might beinterested to explore using the data. This defines the purpose for which you will prepare the data.Page 2 of 102. Code: Write python code to prepare the data such that it can be used to try to answer thquestions for the audience described in step 1. Aim to have sufficient data, and avoid unnecessaryata. The prepared data should be in a format that can be read into one or more pandasdataframes from a file (.csv or .xlsx). If relevant, address any data quality issuesdentified insection 1.1.

3. : Explain how you ensured the data is relevant for the purpose.
4. Include the original and prepared versions of your data set files in your repository.

3.4. Section 2: Database design and creation

The purpose of this section is:

• to demonstrate that you understand the structure of a relational database and the principles ofnormalisation by designing an appropriate database and drawing this as an entity relationshipdiagram (ERD).

• to demonstrate that you can write Python code to create an SQLite database based on the ERD.The database you create can be used in COMP0034 coursework in a data driven web application.

3.4.1. Section 2.1: Database design Design a relational database that can store the data (based on either the prepared or the raw data set,your choice). Consider normalisation.Document the design as an Entity Relationship Diagram (ERD) that includes the following details as aminimum:

• table(s)
• attributes in each table
• data type of each attribute
• primary key attribute for each table
• foreign key attribute(s) if relevant
• relationship lines between tablesInclude the ERD in your report. An explanation is not required, though you may discuss yournormalisation if relevant.

3.4.2. Section 2.2: Database code Write python code that:

• creates a database structure based on the ERD for an SQLite database file.
• takes the data from the dataset file and saves it to the SQLite database file. Note: do not create adatabase that requires a server such as MySQL or PostgresSQL.The quality of the code is assessed.

e relevant Python packages, i.e. pandas and sqlite3.

3.5. Section 3: Tools The purpose of this section is to demonstrate appropriate and effective use of relevant software engineeringtools.Page 3 of 103.5.1. Section 3.1 Environment management Provide relevant files and instructions that allow the marker to set up and run your code in a Pythonvirtual environment. They will use pip and setuptools with the commands:pip install -r requirements.txtpip install -e .As a minimum, edit the files that were provided in the starter code of the repository:requirements.txt: list the packages used in your code

• pyproject.toml: provide basic project details and code package location
• README.md: provide instructions to install and run your code for the data preparation and the

atabase creation

3.5.2. Section 3.2: Source code control Add the URL for your repository to the report.Make regular use of source code control.3.5.3. Section 3.3: Linting Use a Python linter to demonstrate how your code meets Python style standards such as PEP8, PEP257.For example:

• state which Python linter you used.
• provide evidence of the results of running the linter.
• if issues are reported by the linter, address these and then run the linter again and show the results.
• if any issue cannot be addressed, explain why not.

3.6. Section 4: References nclude code references in comments in the code files close to where it is used.Include all other references, if used, in the report.

3.6.1. Section 4.1 Reference use of AI State either that you used AI, or state that you did not.If you used AI, include the details stated in the UCL guidance (https://library-guides.ucl.ac.uk/referencing-plagiarism/acknowledging-AI#s-lg-box-wrapper-19164308).

3.6.2. Section 4.1 Dataset attribution Comply with any license condition required for your data set (given in the data set link in Moodle >Resources > Data sets).Each license is different and tells you what has to be cited; e.g. see open government licence v3 (https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). Typically, but not always‘attribution’ is required: i.e. include a statement listing who owns the data and its location.Page 4 of 104. Submission Refer to Moodle > Assessment for the deadline date and time.Submit your work on Moodle in the assignment submission. The submission states the upload format:.zip for the code (and report if in markdown) plus .pdf for the report (if not in markdown).GitHub is not an acceptable alternative for submission, though its facility to download the code files aszip may be useful to you.Make sure all files arein the submission. URLs linking to external files cannot be marked as theycould bechanged after the submission time. The only exception is where the original data files are too large tupload to Moodle - in this exceptional situation list url(s) to the data files in your report or theREADME.md instead.Do not include your .venv folder in the zip file, this creates unnecessarily large zip files.

Table: Submission checklist Section Report Code files

1. DataexplorationandpreparationDescription andexplanation.ython code to explore/describe the data.Python code to prepare the data.Original data setPrepared dataset

2. Databasedesign andcreationEntityRelationshipDiagram (ERD).Python code to create the database.SQLite database file.

3. ToolsSource codecontrol: URL toGitHub repositoryLinting evidence.Environment management: requirements.txt,pyproject.toml, README.mdReferencesStatement of AIse.Data setattribution.Other references ifused.nclude code references within the code files.Marking5.1. Module learning outcomes The module’s published learning outcomes that are assessed in this coursework are indicated in the table.Page 5 of 10Learning outcomeCoursework 1 ?

1. Describe how software development methodologies can be used to manage thesoftware development process and select and apply an appropriate methodology fora given project.

2. Select and apply techniques for capturing and modelling requirements.
3. Select and apply techniques for modelling an application; and model aapplication using these.Yes -database

4. Select the aspects of a software application can be modelled with the UnifiedModelling Language (UML); and use UML to model different views of anapplication.ERD (notUML)

5. Model the design for a databaseYes

6. Describe testing and recommend an appropriate approach to testing for a givenproject.

7. Recognise the challenges of working in a team and organise themselves and theirgroup to deliver a complex project.Recognise the ethical implications of using data in the context of this course andbe aware of their responsibilities to comply with relevant UCL and UK legislation.Yes

9. Work in a group to apply the skills and knowledge gained in the course to: a)produce a coherent and cohesive specification for an application; and b) select,install, configure and use a set of open-source tools and use these to support thesoftware development cycle for the application.Yes, part (b)The published learning outcomes are being revised. In particular:Data preparation and visualisation are core to the module content yet missing from the publishedlearning outcomes.Following feedback from previous students, the coursework is now individual. Learning outcome 7 isnot addressed; and learning outcome 9 needs to be re-worded.

5.2. Mark allocation You are expected to spend 18 hours on coursework 1 (45 * 40%).

The weighting of each section is shown with an indication of the expected hours of effort required.

5.3. Grading criteria

The coursework is assessed according to the standards set in the standard UCL Computer Science gradingcriteria ( see copy on Moodle in the Assessment section). The criteria most relevant to this assessment are1, 2, 4 and 5.The following tables give the standard UCL CScriteria, and indicators specific to this coursework. Thcoursework is open-ended and allows for different solutions; it is not possible to describe every aspect thatcould be considered.Page 6 of 10The descriptors typically focus on quality and standard of the response, rather than quantity. If you aregoing to do something more (quantity), then you are advised to focus on demonstrating something thatas not already been evidenced in your work.

5.3.1. Generic CS Descriptors Grade band

Generic CS Descriptor Distinction90+Exceptional response with a convincing, sophisticated argument with precisconclusions.

Exceptional grasp of complexities and significance of issues.Exceptional thought and awareness of relevant issues. Sophisticated sense ofconceptual framework in context.Exceptional solution and advanced algorithm/technical design.

Distinction70-89A distinctive response that develops a clear argument and sensible conclusions, withevidence of nuanceThorough grasp of issues; some sophisticated insights.Concepts deftly defined and used with some sense of theoretical context.Excellent algorithmic solution, novel and creative approach.Merit

60-69A sound response with a reasonable argument and straightforward conclusions, logicalconclusions.Sound understanding of issues, with insights intobroaderimplications.Good solution, skilled use of concepts, mostly correct and only minor faults.High pass50-59

A reasonable response with a limited sense of argument and partial conclusionsReasonable grasp of the issues and their broader implications.

Reasonable reproduction of ideas from taught materials. Rudimentary definition anduse of concepts.Reasonable solution, using basic required concepts, several flaws in implementation.Low pass40-49An indirect response to the task set, towards a relevant argument and conclusions.Rudimentary, intermittent grasp of issues with confusions.Analysis relying on the partial reproduction of ideas from taught materials. Someconcepts absent or wrongly used.Rudimentary algorithmic/technical solution, but mostly incomplete.

Page 7 of 105.3.2. Data preparation and understanding

Grade

band

Coursework-specific indicators Distinction90+Evidence beyond the earlier indicators - these solutions are distinctive and as suchthere is no set indication of what might be included.Distinction70-89vidence of a thorough understanding of the data. Analysis and preparation is clearlexplained and decisions justified in the context of the intended purposeCode quality is high. Effective code structure. Effective code documentation. Error

handling thorough and consistently applied.Merit60-69Evidence of a good understanding of the data. Actions taken that would allow thedata set to be used for the intended purpose.Code quality mostly adheres to Python standards. Evidence of structure. Appropriatedocumentation. Evidence of error handling.High pass50-59Evidence that the student has understood and/or prepared the data using code,though may be more limited. Decisions taken are not clearly explained. The purposegiven is appropriate for the data.Reasonable code, using basic required concepts, several flaws in implementation.Low pass

40-49The described purpose may not be clear and/or relate well to the given data.Insufficient evidence that the data has been described and explored.Rudimentary preparation code, but mostly incomplete.

5.3.3. Database design and creation

Grade band

Coursework-specific indicators

Distinction90+Evidence beyond the earlier indicatorsDistinction70-89ERD shows an understanding of potential issues that have been considered in the

structure and the extent of the normalisation.Code quality is high. Effective code structure. Effective code documentation. Errorhandling thorough and consistently applied. Data and relationships are correct in thedatabase.Merit60-69ERD is appropriate for the data given its intended use in applications. Design is clearand uses correct notation. Evidence of appropriate normalisation for the intended use.Code quality mostly adheres to Python standards. Evidence of structure. Appropriatedocumentation. Evidence of error handling. There may be minor issues in the data/relationships in the database.High pass50-59ERD provided and adheres to notation but may lack minor detail and/or limitedevidence of the application of normalisation concepts.Appropriate code that generates adatabase file with data. There may be minor issueswith the code, code quality or data.

Low pass40-49ERD provided but may not adhere to an appropriate notation and/or misses requireddetail.Code shows some understanding but may not generate a usabledatabase file.

5.3.4. Software engineering tools There are no generic CS criteria relating to this aspect.Page 8 of 10Grade band

Coursework-specific indicators e to exceptional use of the expected tools.Merit 60-69Appropriate use of the expected tools.High pass50-59Mostly appropriate use of the expected tools.Low pass40-49Limited, or inappropriate, use of the expected tools.

‘Regular’ use of source code control is stated in section 3.3. ‘Regular’ cannot be precisely define sincestudents work over different periods. You are expected to makeprogress on your coursework weeklyCommits over a period of weeks could be considered ‘regular’; commits only during a short period such as1-2 days could not be considered ‘regular’.

6. Appendices

6.1. Code quality

This is considered as:Code that is easy for others to read and understand.Code that is re-usable. The focus in this IEP minor is on writing code that could be used inapplications, not simply on whether the code works.When you are writing code consider:code structure, e.g. use of functions, classes, modules, packages.adherence to python conventions (PEP8 style guide (https://peps.python.org/pep-0008/), PEP275docstring conventions (https://peps.python.org/pep-0257/)).

documentation (docstrings, comments).error handling.

6.2. Code that does not fully function

If your code does not fully work, and you cannot ‘debug’ and fix it before submission, then in the relevantsection of the coursework document state as much of the following as you can:What is the code that doesn’t work (e.g. a function name)What you think the problem may be. This shows you understand the issue even if you cannot solveAny solutions you have tried. This shows that you understand the issue and were able to take stepsto try and resolve it.Clear code documentation (docstrings, comments) is often useful in these situations as the marker can

more easily see what you intended your code to do, even if it does not fully achieve that.

6.3. Guidance on Moodle

Referencing: AI, Code, other (https://moodle.ucl.ac.uk/mod/page/view.php?id=6363796)Assessment information Q&A: support, late submission, SoRA and EC, submission date change