Deze informatie is alleen in het Engels beschikbaar.
Topics: Data Science
Disciplines: Computer Science, Data Science, Social Sciences, Life Sciences
Skills: Analysing, project-based working, digital skills, collaborating, oral and written communication, presenting, reflecting
Admission requirements:
This course is an (extracurricular) Honours Class: an elective course within the Honours College programme. Third year students who don’t participate in the Honours College have the opportunity to apply for a Bachelor Honours Class. Students will be selected based on i.a. their motivation and average grade.
There are no official prerequisites to this course, but a general knowledge about managing and installing programs and files on a personal computer is assumed. The course is easier if students have any prior coding experience, but it is not required. Those who would like to prepare in advance (which again, is not required) can have a look at the following resources: • https://app.datacamp.com/learn/courses/intro-to-python-for-data-science • https://app.datacamp.com/learn/courses/introduction-to-data-science-in-python • https://app.datacamp.com/learn/courses/data-manipulation-with-pandas • https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-matplotlib
Description:
Data Science deals with handling, processing, analysing, interpreting, and extracting knowledge from data, ultimately to derive optimal decisions. Often, the term is associated with the concept of big data, i.e., data that is characterised by large volume, high velocity of generation, and data variety, meaning many different types of information.
Today, data science is of paramount importance in just about any domain, ranging from the life sciences, including e.g. health and biosciences, to banking, sports, insurances, retail, and heavy industries.
The possibilities for generating new insights and decisions based on data are considerable. This Honours Class first introduces students to some of the fundamental concepts and tools of Data Science, an introduction into Python programming for data analysis and then continues with overviews of specific applications.
Course objectives:
Upon successful completion of this course, students will:
have a general overview of the possibilities in the field of data science;
have knowledge of different types of data;
be able to understand and perform basic data analysis tasks in Python;
have basic knowledge of some of the tools used in data science;
have developed skills for several tools for analyzing data;
have experience with performing basic analyses for real-world applications.
Programme and timetable:
This course will take place on Tuesdays from 17:15-19:00. Note that thefinal session will be from 17:15-20:00.
Session 1: February 6
Lecture: introduction to data science and programming basics
Session 2: February 13
Lecture and practical: data exploration and visualization
Session 3: February 20
Lecture and practical: supervised learning (classification and regression)
Session 4: February 27
Lecture and practical: unsupervised learning (clustering and dimension reduction)
Session 5: March 5
Lecture and practical: model evaluation and explainability
Session 6: March 12
Lecture and practical: data science in society
Session 7: March 19
Guest lecture, tba
Session 8: March 26
Guest lecture, tba
Session 9: April 2
Guest lecture, tba
Session 10: April 9
Guest lecture, tba
Session 11: May 28 (17.15 - 20:00)
Final seminar with student presentations and discussions.
Location:
Huygens building, room 207
Course load and teaching method:
This course is worth 5 ECTS, which means the total course load equals 140 hours:
Lectures: 5 lectures of 1 hour, 5 lectures of 2 hours (15 hours)
Practicals: 5 practicals of 1 hour (5 hours)
Basic Python programming course (10 hours)
Weekly programming assignments: 5 times 5 hours (25 hours)
Individual assignment: 15 hours
Group project including final seminar: 60 hours
Assessment methods:
Individual assignment (30%)
Group project
o Paper of 3000 words (30%)
o Presentation (20%)
o Peer review of another group’s work (10%)Active participation (10%)
Brightspace and uSis:
Brightspace will be used in this course. Upon admission students will be enrolled in Brightspace by the teaching administration.
Please note: students are not required to register through uSis for the Bachelor Honours Classes. Your registration will be done centrally.
Application process:
Submitting an application for this course is possible from Monday 30 October up to and including Sunday 19 November 2023 23:59 through the link on the Honours Academy student website.
Note: students don’t have to register for the Bachelor Honours Classes in uSis. The registration is done centrally before the start of the class.
Contact:
For questions about course content: Marieke Vinkenoog m.vinkenoog@liacs.leidenuniv.nl
For administrative questions: Honours Academy baclasses@ha.leidenuniv.nl