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Disclaimer: due to the coronavirus pandemic, this course description might be subject to changes. For the latest updates regarding corona virus, please check this link.
Topics: Data Science.
Disciplines: Data science, Life Sciences, Social Sciences.
Skills: Research, presenting, academic writing and reviewing.
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.
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 using Python;
Have basic knowledge of some of the tools used in data science;
Have developed skills for several tools for analysing data;
Have experience with performing basic analyses for real-world applications.
Programme and timetable:
This course will take place on Mondays from 17:00-19:00, starting in February.
Lecture 1: 7 February
Introduction to data science - Python practical: set-up.
Lecture 2: 14 February
Supervised learning models: classification - Python practical: introduction to Python and import data.
Lecture 3: 21 February
Supervised learning models: regression - Python practical: exploratory data analysis.
Lecture 4: 28 February
Unsupervised learning models - Python practical: exploratory data analysis.
Lecture 5: 7 March
Tools and model evaluation - Python practical: Model fitting.
Lecture 6: 14 March
Data science in society - Python practical: Model fitting.
Lecture 7: 21 March
Guest lectures on specific data science applications.
Lecture 8: 28 March
Guest lectures on specific data science applications.
Lecture 9: 4 April
Guest lectures on specific data science applications.
Lecture 10: 11 April
Guest lectures on specific data science applications.
Lecture 11: 25 April
Final seminar with student presentations and discussions.
Location:
Old Obserbatory
Reading list:
TBA
Course load and teaching method:
This course is worth 5 ECTS, which means the total course load equals 140 hours:
Lectures: 6 of 1 hour, 4 of 2 hours (participation mandatory
Practical sessions: 6 of 1 hour (participation mandatory
Preparation lectures: 1 hour/week
Preparation practical and practical assignment: 5 hours/week
Final group assignment and seminar: 60 hours
Assessment methods:
The assessment methods will look as follows:
20% presentation (5 minutes) during final seminar;
50% paper (3000 words);
20% practical assignment;
10% participation (active).
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.
Registration process:
Submitting an application for this course is possible from Monday 1 November up to and including Thursday 11 November 2021 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:
Esme Caubo: e.caubo@liacs.leidenuniv.nl
Eszter Bokányi: e.bokanyi@liacs.leidenuniv.nl