Python for Data Science (5 op)

Aika ja paikka

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.

Oppimateriaalit

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

Harjoittelu- ja työelämäyhteistyö

No Intership or company collaboration on this summer course

Tenttien ajankohdat ja uusintamahdollisuudet

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

Kansainvälisyys

No internatinal collaboration in this summer

Toteutuksen valinnaiset suoritustavat

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

Sisällön jaksotus

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.

Opetusmenetelmät

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.

Lisätietoja opiskelijoille

Open University students enrol via enrolment form that can be found on website (Applicants -> Open university -> Open studies) as well as the enrolment timetable. Student pays the course fee upon registration. HAMK students and students from other universities of applied sciences register for studies in Pakki/Peppi. Accepted cross-institutional students will receive instructions for activating their HAMK credentials via email. The learning platform is accessed using HAMK credentials. Note! the message may be redirected to the spam folder. HAMK’s Spark feedback system opens 7 days before the course/module ends and it stays open for 14 days after the end date. You may give course/module feedback via Pakki. A notification of the courses/modules (to whom you have registered) for which feedback can be given appears on the desktop. You will always receive a notification on the student desktop when a course/module is ready for feedback. You will receive a reminder in HAMK's e-mail on the end date of the implementation, if you have not already given feedback before. For more information on course/module feedback, see the student feedbackpage. Open University of Applied Sciences and CampusOnline students can provide course/module feedback via a browser at https://pakki.hamk.fi/spark. 30 study places reserved for HAMK students 40 study places reserved for CampusOnline students 30 study places reserved for Open Studies students

Arviointiasteikko

1-5