First Year Project (Spring 2023)

Abstract

This course aims to familiarize students with the pipeline for Data Science projects: From a domain-specific context and associated data we need to identify and formulate a domain-specific research question and translate it into a technical problem, which can then be addressed with techniques within Data Science. After performing the relevant data analysis, the results should be communicated in the context of the domain.

Description

The course consists of a series of full-fledged Data Science mini-projects from start to finish, including the initial memo, technical translation of the problem, some methodology decisions, implementation, evaluation, and translation of the results back into non-technical language. Through this course students will gain experience with online collaboration using platforms such as GitHub and Overleaf.

Formal prerequisites

This course combines knowledge from the first-semester courses Introduction to Data Science and Programming, Linear Algebra and Optimisation, and Data science in Research, Business and Society with knowledge that will be acquired during the second semester from the two concurrent courses.

The course is only open for students enrolled in BSc in Data Science.

Intended learning outcomes

After the course, the student should be able to:

• Identify and delimit a problem in Data Science within a given domain-specific context
• Discuss the relevant options for an appropriate scientific methodology to address the problem; this covers considerations on the data-analytical approach and on the implementational approach
• Carry out the full analysis according to the selected methodology
• Communicate their work to both experts and non-experts; this should cover the entire pipeline from problem formulation to analysis methods and their results

Learning activities

The course comprises two  group projects. Each project is associated with a lecture series on the topics of the project, exercise sessions, and project supervision. Hand in and presentation of the first project is a mandatory activity.
The second project is submitted as exam submission followed by oral exam.

In this project you will learn to measure features in images of skin lesions, and predict the diagnosis (for example, melanoma) from these features in an automatic way. You will: 

• Implement methods to measure ”handcrafted” features
• Explore and transform these features
• Use the features with a machine learning classifier to predict the lesion
• diagnosis
• Perform experiments to evaluate different parts of your method

You will receive data and Python code to start with at the start of the project.

Mandatory activities

The obligatory activity comprises of a hand in of the first project and an oral presentation of the first project.

The pedagogical function of the obligatory activities is to provide the students with an opportunity to practice the ILO’s of the course, specifically competences such as oral presentation. In relation to the entire study programme, the mandatory activity will act as a “lab” for testing out and get feedback on their level of competence.

The students will be given formative feedback in order to further develop their competences i.e., oral presentation, written presentation, choice of data visualisation etc. The feedback will serve as supervision-like feedback. The students will be praised on the good parts as well as given constructive feedback on what can improve.

If the students fail to participate in the oral presentation or the presentation in itself is insufficient, they will be given another try.  

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 15%
• Lectures: 10%
• Exercises: 15%
• Assignments: 5%
• Project work, supervision included: 50%
• Exam with preparation: 5%

Ordinary exam

Exam type:
D: Submission of written work with following oral, Internal (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.

reexam

Exam type:
B: Oral exam, Internal (7-point scale)
Exam variation:
B1I: Oral exam with time for preparation. In-house.

Time and date