Monday 15 July - Friday 26 July, 2019
2 weeks = 2 ECTS
Python is one of the most popular and rapidly developing programming languages. A clear syntax which facilitates learning and a plethora of built-in and third-party libraries made Python especially popular among academics and researchers of all kinds. Python has already been the first-choice language in Machine Learning and Data Science for a while, but as far as Social Sciences are becoming more digitally-oriented it is getting in demand by sociologists, economists, linguists, and other social researchers. This course is created for students who want to learn how to solve real-world data-related problems with Python programming environment but have no experience in programming. The course syllabus covers most of Python functionality from basics syntax to the modern libraries for machine learning and data analysis.
Part 1: Python basics
The first part of the workshop reviews the history of Python and discusses its strengths and weaknesses and features that make Python extremely useful for social scientists. It also covers the basic programming concepts such as variables, data types operators, functions and so on. Moreover, it gives an introduction to the applied tools essential for every programmer such as interactive shell, IDEs, and packages.
Duration: 10 hours
1. Introduction to Python programming language.
2. Syntax and semantics.
3. Going further: more advanced use of Python.
Part 2: Python for data analysis
The second part of the workshops deeps into Python as a tool for data analysis. The primary focus here is on the specialized programming packages allowing to do data analysis in a slick and easy way.
Duration: 10 hours.
1. Linear algebra with NumPy.
2. Pandas. Pandas everywhere! How to transform Python to R.
4. StatsModels: Statistics in Python
1 interactive workshop per day; 10 days of classes in total.
The course gives an introduction to the basics of Python programming language, Data wrangling, analysis and visualization with Python, as well as the basics of machine learning with Python.
Practical assignments and quizzes.
Proceed to application
Oleg Stanislavovich Nagornyy