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Assessments

The summative assessment in this course consists of two in-person practical tests:

  1. In-person mid-term practical test (30%): 2-hour test that takes place during your Week 7 seminar.
  2. In-person final practical test (70%): 3-hour test that takes place during Week 1 of Winter Term on Friday 15 January 2027. You will be randomly assigned a time slot.

Read the Timetable carefully

The week 7 and 8 seminars are 2 hours, so the start or end times may differ. The final test is listed as a lecture in week 1 of Winter Term. Carefully review the Timetable and make note of all dates and times.

In addition, the course has weekly exercises assigned during seminars, which serve as formative assessments that help prepare you for the two summative tests. Solutions to these exercises will be provided.

Practical tests

Both summative assessments are closed-book, in-person practical tests taken on LSE-issued Apple MacBooks with a test configuration that restricts access to internal software, external websites and AI tools.

  • Both tests are task-based, modelled closely on the seminar activities. You will work through a Quarto notebook and use the command line. Expect a mixture of coding exercises, code interpretation, and short written-answer questions.
  • You earn full marks for completing the task asked of you, and partial credit for partial progress. Unless a question says otherwise, you are not marked on how elegant or efficient your code is. This is not a computer programming course.
  • Practice problems will be circulated before each test. Specific logistics (times, rooms, what to bring) will be announced closer to each test.

The best way to prepare is to practise writing code and to make sure you understand the concepts behind it:

  1. Review the lecture content and try to write any code yourself.
  2. Redo the seminar exercises as many times as you can.
  3. Devise a sample task for each topic and write code to accomplish it.
  4. Study the conceptual material from the lectures: be comfortable explaining key ideas (e.g. how APIs work, what tidy data means, how a web scraper navigates a page) in your own words.
  5. Make sure you can explain why your code works, not just that it works.

Attendance at practical tests

Please be sure that you will be available to take both practical tests on their scheduled dates. If you miss either test, we cannot guarantee that you will be able to make it up during this academic year. If not, you will need to resit in January 2028.

Reasonable adjustments

If you have approved reasonable adjustments, please contact the course convenor within the first two weeks of the term to ensure proper arrangements can be made for the tests.

Marking

Each registered non-auditing student will receive a numerical mark in this course. This mark will be based on the student's performance on the summative assessments only. For a taught master's programme at LSE:

  • marks ranging from 70 to 100 are classified as distinction;
  • marks ranging from 60 to 70 are classified as merit;
  • marks ranging from 50 to 60 are classified as pass; and
  • marks below 50 are classified as fail.

Please keep in mind that the marking scale at LSE may differ from your prior institution(s).

If you are not yet familiar with the assessment system at LSE, please review LSE's Understanding Results webpage.

If you are used to an American-style grading system, you might find this LSE website helpful for better understanding the marking system.

Academic integrity

As scholars and educators, your instructors take academic integrity very seriously. Any academic misconduct will be dealt with in accordance with the LSE Regulations on Assessment Offences.

If you are unfamiliar with the norms and expectations around academic integrity at LSE (or UK universities more generally), then you should review the resources available on LSE's website and especially the School's Academic Integrity Awareness Week webpage.

We expect that you will use generative AI to aid your learning. However, any use of generative AI that would reasonably be considered academic misconduct is prohibited. Academic misconduct with generative AI includes (but is not limited to) using these tools to substantially complete your work or to fabricate data or other information. Any assessment that appears to be written in large part by generative AI will be referred for further investigation, which could include an oral examination to verify authorship.