Python for Data Science (5 ECTS)

Learning outcomes

You will gain a brief understanding of the field of data science and it’s main concepts. After this course, you will know how to collect, process and visualize different types of data using Python and it’s libraries, such as NumPy, Pandas and Matplotlib. The main focus of this course is in practical assignments that are designed to develop your skills especially within the data processing steps of an analytics project. We assume that the students are familiar with have basics of Python. Structures that are more characteristic for Python are discussed.

Content

Main contents of the course: - Programming structures more characteristic for Python - Principles of visual analytics - Reading and writing data - Data structures and their basic properties - Fundamental exploratory analysis of data - Selecting, indexing, grouping and transforming data - Working with missing data and duplicates - Visualizing data with Matplotlib

Teaching methods

This study is conducted as online studies. Studying is done independently based on the materials provided in Moodle and based on other provided materials. The structure of the study has been prepared in Moodle so that studies there progress from top to bottom. To complete the study the course project work need to be returned within the given schedule and based on set requirements for the course project work. Project work and it's guidelines is announced in Moodle at the beginning of the course. The project work is putting together several topics covered during this course. Topics covered in the study include * Programming structures more characteristic for Python * Visualizing data with Matplotlib * Principles of visual analytics * Reading and writing data * Data structures and their basic properties * Selecting, indexing, grouping and transforming data * Working with missing data and duplicates * Fundamental exploratory analysis of data In addition to lecture materials and self-study materials, instructional videos are provided such that they contribute to reviewing and deepening the issues covered in the lecture materials. Instructional videos can also provide guidance on how to go through the topics and also provide the information needed to do project work.

Location and time

Independently by using own computer and the material found in the Moodle between 9.5.-31.7.2023. The studying is independent and the students decides by self how much time to spent daily to the studies. Enrolment for open studies is 13 - 24 of March.

Learning materials and recommended literature

Mostly the study material is provided in Moodle together with further links and literature references. Most used material outside Moodle, is the highly recommended book Python for Data Analysis, 2nd Edition by Wes McKinney Released October 2017 Publisher(s): O'Reilly Media, Inc. ISBN: 9781491957660

Alternative completion methods of implementation

For Recognition of Prior Learning (RPL) please contact the module teachers before the module starts/as soon as possible

Internship and working life connections

No Intership or company collaboration on this summer course

Exam dates and retake possibilities

There is not a final exam in this implementation. The grade is based on practice work.

International connections

No internatinal collaboration in this summer

Content scheduling

It is preferrable to study new things in the beginning in daily basis making the tasks and making own modified practices. At the latter part of the course a student has knowledge to plan and implement the project work, which is normally quite intensive time.

Evaluation scale

1-5