Programming in Python for data analysts 2400-ZEWW750
• Python basics, environment installation and preparation, IPython Notebook usage
• Basics of programming: data structures, flow control, functions, objects, methods
• Debugging
• Linear algebra: Numpy
• Working with dataframes: Pandas
• Time and date
• Data bases vs Python
• Visualisation: seabron, matplotlib
• Animations in Python
• Python online: using API, JSON, XML, geocoding
• Web scraping: requests, webdriver
• Simple web applications: bottle
Type of course
Course coordinators
Learning outcomes
Knowledge:
After finishing the course student knows the fundamentals of Python programming. Student knows how to use Python and its packages to prepare and analyse data to solve basic economic problems.
Skills
Student is able to prepare Python programming environment and install required packages.
Student is able to read/write, transform and aggregate data which can be used in economic analysis. Student is able to prepare complex visualization to illustrate socio-economic phenomena.
Social Competence
Student understands that use of Python on the expert level requires continuous practice and improvement of his/her own skills.
Student understands that programming in Python gives a number of universal competencies, which can be applied in many areas of economics as well as other fields of knowledge.
SU05, SU06, SK01, SK03, SU04, SU03, SU02, SU01, SW03, SW02, SW01, SW04, SW05, SK02, SK04
Assessment criteria
Final project (60%)
Written test (40%)
Bibliography
Course refers to dynamically changing software. Therefore, it will be based on the materials prepared by the teacher, which will be regularly updated and available for students. There is no obligatory literature.
Non-obligatory literature:
Jake VanderPlas,Python Data Science Handbook Essential Tools for Working with Data, O'Reilly Media, 2016
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: