Deze informatie is alleen in het Engels beschikbaar.
Topics: Data Science
Disciplines: Computer Science, Data Science, Social Sciences, Life Sciences
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 you have prior coding experience, but it is not required. At the beginning of the course, you will complete two online Python courses (see course load), which you will get free access to through a DataCamp Classroom for students of the course. In case you would like to start these courses early to prepare for the course, contact the course instructor.
https://app.datacamp.com/learn/courses/intro-to-python-for-data-science
https://app.datacamp.com/learn/courses/data-manipulation-with-pandas
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 the course, students will be able to:
apply basic Python programming concepts to develop simple scripts for analysing datasets;
summarise a dataset using descriptive statistics and appropriate visualisations;
identify suitable modelling options for a dataset and their pros and cons;
carry out a full data analysis using Python, including: data preparation, hyperparameter tuning, training and evaluating a model;
evaluate the performance of a model using appropriate metrics and relate the results to the context of a problem;
construct an original data science project, resulting in a report;
summarise results from their data science project in an oral presentation;
apply their understanding of the advantages and limitations of data science and AI to specific real-world situations.
Programme and timetable:
This course will take place on Tuesdays from 17:15-19:00. (Note that the final session will be from 17:15-20:00.)
Session 1: February 4, 2025
Introduction to Data Science
Session 2: February 11
Data Exploration
Session 3: February 18
Supervised Learning
Session 4: February 25
Unsupervised Learning
Session 5: March 4
Tools and Evaluation
Session 6: March 11
Data Science in Society
Session 7: March 18
Guest lecture #1
Session 8: March 25
Guest lecture #2
Session 9: April 1
Guest lecture #3
Session 10: April 8
Guest lecture #4
Session 11: May 27 (17.15 - 20:00)
Final seminar
Deadline final assignment: 27 May 2025
Location:
TBA
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)
Assignment: 15 hours
Group project including final seminar: 60 hours
Assessment methods:
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%)
It is not required to successfully complete all partial exams in order to pass this course. Students are allowed to compensate a ‘fail’ (grades up to and including 5.0).
The assessment methods will be further explained in the first session of the class.
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 28 October up to and including Sunday 17 November 2024 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:
Dr. Marieke Vinkenoog, m.vinkenoog@liacs.leidenuniv.nl