Analyzing Data with Python

Term: 
Spring
Credits: 
2.0
Course Description: 

This course will provide a comprehensive introduction to programming with Python, starting from the basics. Beyond confidently using Python, the class will focus on solving problems around Data Processing and Analysis. Additionally, we will discuss for what types of problems Python is the right choice and how to further extend your knowledge after the class. The overarching goal is to equip students with enough programming experience to start working in any area of computation and data-intensive research.

The course will run with a mix of theoretical classes and hands-on sessions organized into 6 classes of 200min. Use of a computer will be required during most of the lectures. Students can use their own laptops or the facilities provided by CEU. If you haven’t completed Foundation of Data management in R course or you don’t have programming experience this class will be too advanced for you and we recommend you to take the beginner version in the Winter Semester. The classes use knowledge and skills from the previous ones thus it is important to attend all classes. We will provide extra exercises between classes in the form of homework. The course can accommodate a maximum of 30 students.

Course coordinator: Eszter Somos, somoseszter@gmail.com Teaching Assistant: Zsombor Koman, Koman_Zsombor@student.ceu.edu - please send him all inquiries about schedule, classroom and technical requests 

Learning Outcomes: 

By the end of the course, students will have experience with techniques which are vital to effective data management:

  • The basic syntax and use of Python as a data analysis tool, including writing and executing scripts to automate common tasks, using the IPython interpreter for interactive exploration of data and code, and using the Jupyter notebook to share and collaborate.
  • Loading data from a variety of common formats
  • Manipulating data efficiently with Pandas
  • Basic web scraping
  • Use of web APIs
  • Use of special python packages such as data visualization libraries
  • Performing basic data mining
Assessment: 
  • Students shall not miss more than 2 classes. Failing to do so will yield an administrative fail grade. (If you have a major impediment please contact the Instructor.)
  • To pass, students will need to get at least 50% of the overall grade. The grade will be based 80% on homework and 20% on class participation.  
Prerequisites: