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.
Key words:
Skills: Research, presenting, academic writing and reviewing
Topics: Data Science
Disciplines: Data science, Life Sciences, Social Sciences
Description:
Data Science deals with handling, processing, analyzing, 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 characterized 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 of Data Science and then continues with overviews of specific application domains.
Course objectives:
Upon successful completion of this course, students:
have a general overview of the possibilities in the field of data science;
have knowledge of different types of data;
are able to understand and perform basic data analysis tasks;
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:
Monday 03-02-2020 17.00-19.00
Lecture 1: Introduction to data science
Python practical: Set-up Python
Lecturer: Iris Yocarini
Monday 10-02-2020 17.00-19.00
Lecture 2: Supervised learning models: classification
Python practical: Intro to Python and import data
Lecturer: Iris Yocarini
Monday 17-02-2020 17.00-19.00
Lecture 3: Supervised learning models: regression
Python practical: Exploratory data analysis
Lecturer: Iris Yocarini
Monday 24-02-2020 17.00-19.00
Lecture 4: Unsupervised learning models
Python practical: Exploratory data analysis
Lecturer: Iris Yocarini
Monday 02-03-2020 17.00-19.00
Lecture 5: Tools and model evaluation
Python practical: Model fitting
Lecturer: Iris Yocarini
Monday 09-03-2020 17.00-19.00
Lecture 6: Data science in society
Python practical: Model fitting
Lecturer: Iris Yocarini
Monday 16-03-2020 17.00-19.00
Lecture 7: Text mining and Natural Language Processing
Lecturer: Anne Dirkson & Alex Brandsen
Monday 23-02-2020 17.00-19.00
Lecture 8: TBA
Monday 30-03-2020 17.00-19.00
Lecture 9: Urban Computing
Lecturer: Mitra Baratchi
Monday 06-04-2020 17.00-19.00
Lecture 10: TBA
Monday 25-05-2020 17.00-19.00
Lecture 11: Final seminar with student presentations, discussions, and dinner
Location:
Old Observatory, Leiden. Room c005.
Reading list:
To be announced.
Course load and teaching method:
This course is worth 5 EC, which means the total course load equals 140 hours.
Lectures: 9 lectures of 2 hours; (attendance is mandatory)
Seminars: 1 seminar of 4 hours; (attendance is mandatory)
One Practical session of 2 hours; (attendance is mandatory)
Literature reading & practical work: on average 8 hours p/week;
Final essay, practical assignment, reviews and presentation: 60 hours.
Assessment method:
The assessment methods will look as follows:
20% presentation (5 minutes) during final seminar;
50% paper (3000 words);
20% practical assignment;
10% participation (active).
Blackboard:
Blackboard will be used in this course. Students can register for the Blackboard site two weeks prior to the start of the course
Registration process:
Enrolling in this course is possible from Monday the 4th of November up to and including Thursday the 14th of November until 23:59 o'clock through the Honours Academy. The registration link will be posted on the student website of the Honours Academy.
Please note: students are not required to register through uSis for the Bachelor Honours Classes. Your registration will be done centrally after successful completion of the Bachelor Honours Class.
Contact:
Drs. Esme Caubo: e.caubo@liacs.leidenuniv.nl.