Data Analysis with Python – MOOC
Python is a simple to use programming language, very commonly used within the field of Data Science; in many cases it is easiest to write the first prototype with Python until switching to another language to optimize running time. Also, many program libraries have evolved around Python, making it possible to develop efficient programs directly. However, creating efficient code with Python requires skills beyond basics.
The project aims to develop a new massive online open course – Data Analysis with Python. Central primitives and libraries of Python language are introduced directly in the context of data analysis. The focus is not on programming skills per se, and the course assumes some familiarity with basis principles of programming on some language. The primary goal is to give an overview of data representations (including lists, arrays, matrices) and their use in central data analysis tasks (including summary statistics, linear regression, clustering, classification). The hands-on exercises using powerful libraries aim to give motivation to continue studying the science behind the tools in advanced courses.
The majority of the course content has been developed by Jarkko Toivonen (PhD student) during Autumn 2018. During January- February 2019 the course is offered for a small number of students in the piloting phase. Assignments are conducted in Test My Code environment, providing automatic evaluation against correct answers. After passing 80% of the assignments, the students can take an exam and submit a final project. Course evaluation mechanisms are under development, but the plan is to scale the examination by using multiple choice exams and peer-review for the project work. Right after the pilot phase, the course is planned to be offered for all students at the University of Helsinki during March-April 2019 (period IV), and then annually. While many aspects will be automated, there will also be some weekly guidance (pajaopetus) to support the learning.
Interested in joining the development team?
We are looking for project topics from variety of fields of science: Course evaluation includes a final project (of size 1 ECTS) where the data analysis skills are applied in a real context. This project works also as a teaser of the respective field, and makes it possible to tailor the course to students from variety of study programmes. Currently, we have one project ready on biological sequence analysis. If you interested in contributing a project work from your field, please contact Veli Mäkinen.