Course Information
The course gives an introduction to obtaining, transforming, exploring and visualising data using modern tools. This is at the core of datascience. We will specifically be using the following tools: Python/R, Jupyter notebook, Git and Github. The course will give and introduction to these tools and how they are used in Data Science using practical excersises. The lectures will include both theoretical and practical aspects using a combination of traditional lectures and live demos. The course materies is based on ideas & material developed by Martin Sköld, Erik Thorsén, Michael Höhle and Felix Günther.
The main literature of the course will be
- R for Data Science by Grolemund and Wickham which is specific to R.
- Python for Data Analysis by Wes McKinney which is for Python.
Both have an open access version, which are accessed through the links above. You can buy the books if you want but most of the information can be found on the web!
The lecturer of the course is Taariq Nazar.
Moodle page
This website is supplemented by a Moodle page, which requires an Stockholm University(SU) login. However, the content of the course will be fully contained on this page. The Moodle page is used for forum discussion and registration.
Examination
Examination of the course consists of three parts
- Weekly individual homework assignments with sharp deadlines and peer review, see Homework
- An individual project, see Project
- A theoretical take home exam, see Exam
Contact
The contact info to the TAs can be found on the Moodle page.