SICSS Istanbul 2023 | Python for CSS: Jupyter, Anaconda, and Data Analysis Basics (Melih Can Yardı)
Melih Can YardıBefore analyzing social data with machine learning, natural language processing, or network analysis, researchers first need a reliable computational environment.
In this practical workshop, Melih Can Yardı (Koç University) introduces the essential Python tools that form the foundation of modern computational social science. Beginning with the installation and configuration of Anaconda, participants learn how to create isolated Python environments, manage packages, and prepare reproducible workflows for research projects.
The lecture then provides a hands-on introduction to Jupyter Notebooks, one of the most widely used interactive programming environments in data science. Participants learn how notebooks combine executable Python code, documentation, mathematical notation, visualizations, and narrative text into reproducible research documents. The session explains notebook structure, markdown cells, code cells, keyboard shortcuts, and best practices for organizing computational research.
The second half of the workshop focuses on practical Python programming. Participants explore common programming errors—including syntax errors, indentation errors, type errors, indexing mistakes, and debugging strategies—before learning how to import and export structured datasets using CSV and JSON formats. Throughout the lecture, emphasis is placed on understanding error messages, reading documentation, and developing effective debugging habits that enable researchers to work independently with Python.
Rather than assuming prior programming experience, the session provides an accessible introduction to the tools and workflows that support more advanced topics throughout the Summer Institute, including machine learning, natural language processing, geospatial analysis, and Large Language Models.
Topics covered
Python for computational social science
Anaconda
Python environments
Package management
Jupyter Notebooks
JupyterLab
Markdown and code cells
Reproducible computational workflows
Python data types
Variables and objects
Lists and dictionaries
Functions and methods
Common Python errors
Debugging techniques
Syntax errors
Indentation errors
Type errors
Name errors
Index errors
Key errors
Attribute errors
Reading CSV files with Pandas
Writing CSV files
JSON data structures
Importing and exporting data
Pandas
NumPy
Python documentation and best practices
Using live demonstrations and interactive coding exercises, participants build the practical skills needed to manage Python projects, organize research workflows, troubleshoot common programming problems, and prepare data for computational social science analyses.
This lecture was delivered during the Summer Institute in Computational Social Science (SICSS Istanbul 2023) for graduate students and researchers seeking a practical introduction to Python programming, Jupyter Notebooks, reproducible research, and the computational tools that support modern social science research.