Basic Python for Official Statistics

Course Leader

Christian Kauth

Target Group

Statistical production units and methodologists of National Statistical Institutes (NSIs).

No prior programming experience required.

Entry Qualifications

• Sound command of English for discussions and presentations

• Basic computer literacy

• Enthusiasm for learning programming

• No prior Python or programming experience required

Objective(s)

Master Python fundamentals through hands-on practice with real statistical data, building a solid foundation for modern applications in official statistics. By course end, participants will:

• Understand Python's role, advantages, and limitations in statistical production workflows

• Write and execute Python programs using core syntax, data types, operators, and control structures

• Use Python's built-in data structures (lists, dictionaries, sets, tuples) for data manipulation

• Create and use functions to structure reusable code

• Handle files and data input/output: reading and writing CSVs, JSON files

• Access and analyze statistical data from APIs (Eurostat) and databases (SQLite)

• Manage packages and virtual environments for reproducible projects

• Work with Jupyter Notebooks as a tool for exploratory analysis and reporting

• Perform data analysis using pandas for tabular data

• Create statistical visualizations with matplotlib and seaborn

• Debug code effectively and leverage AI coding assistants

• Apply best practices for code organization, documentation, and collaboration

Contents

This immersive course introduces NSI staff to Python as a versatile tool for statistical work. Through balanced theory and hands-on practice with real official statistics data, participants gain fundamental programming skills, explore modern data analysis libraries, and learn to manage real projects—building a solid foundation for statistical production work.

The course combines traditional programming fundamentals with modern development practices, ensuring participants are prepared for contemporary statistical production challenges.

Day 1: Python Fundamentals

Welcome to the Python ecosystem and programming basics. Understand Python's journey from scientific computing to AI dominance, set up cloud-based environments (Google Colab), and write your first programs using variables, data types, operators, conditionals, and loops.

Day 2: Data Structures & Functions

Master Python's powerful collections (lists, dictionaries, sets, tuples), create reusable functions with proper documentation, learn advanced function techniques (lambda, map, filter), and develop debugging skills using AI coding assistants (GitHub Copilot, GPT, Claude).

Day 3: Data Analysis & Visualization

Set up reproducible environments with pip and venv, work with standard libraries (pathlib, datetime, json, csv), perform data analysis with pandas and Jupyter notebooks—loading data from CSV files and Eurostat API—and create statistical visualizations with matplotlib and seaborn using real official statistics datasets.

Day 4: Database Integration & Project Launch

Learn SQLite for data persistence and SQL queries, followed by comprehensive Q&A session. Innovative hackathon where YOU pitch project ideas, teams form around the most exciting ideas, and collaborative coding begins.

Day 5: Project Development & Showcase

Intensive project work with personalized instructor coaching—build your working demo, debug challenges, and refine your solution. Present your team's project to peers (10-minute demos), celebrate collective achievements through peer voting, and leave with a portfolio piece showcasing your new Python skills.

Expected outcome

Participants gain both technical proficiency and strategic understanding to leverage Python for modern statistical production:

Technical Skills:

• Write, debug, and execute Python scripts and Jupyter notebooks

• Manipulate data using pandas and Python's built-in data structures

• Access statistical data from APIs (Eurostat) and databases (SQLite)

• Work with CSV and JSON files for data input/output

• Create statistical visualizations with matplotlib and seaborn

• Manage Python environments and dependencies for reproducible workflows

• Structure code using functions and modules with proper documentation

• Use debugging tools effectively and leverage AI coding assistants (GitHub Copilot, GPT, Claude) for problem-solving

Strategic Capabilities:

• Assess when Python is the right tool for statistical production tasks

• Design reproducible analytical workflows following best practices

• Understand the Python ecosystem and know where to find resources

• Collaborate effectively using modern Python development tools

• Bridge traditional statistical methods with contemporary data science approaches

Professional Development:

• Confidence to tackle new programming challenges independently

• Access to a supportive community and extensive learning resources

• Foundation for advanced Python topics and specialization

• Ability to communicate technical concepts to non-technical colleagues

Training Methods

• Interactive lectures with live coding demonstrations
  (Days 1-3: ~40%)

• Hands-on coding exercises and labs embedded in theory sessions
  (Days 1-3: ~20%)

• Hackathon-style collaborative project work
  (Days 4-5: ~30%)

• Team presentations and peer learning
  (Day 5: ~10%)

Required Reading

None

Suggested Reading

None

Required Preparation

Software to install (detailed instructions provided during course)

• Modern web browser (Chrome, Edge or Firefox)

• VS Code (https://code.visualstudio.com/)

Free accounts to create

• Google Colab (course material) (https://colab.research.google.com/)

Project preparation:

• Think about a dataset or project idea relevant to your work that you'd like to explore during the hackathon (Days 4-5)

• Consider bringing your own data (CSV, Excel, or API access) or plan to work with public datasets (Eurostat, national statistics)

• The hackathon uses a reverse-pitch format where YOU propose ideas, so come prepared with a challenge you're excited to tackle!

Trainer(s)/
Lecturer(s)

Christian Kauth (Independent expert)