This comprehensive Python training program is designed for economists, statisticians, and researchers who want to harness the power of programming for data-driven analysis. The course begins with the fundamentals of Python programming, ensuring that no prior coding knowledge is required. Participants gradually progress from writing simple programs to handling complex datasets, preparing them fir applying statistical and econometric techniques, and exploring modern machine learning methods.
The training is divided into three structured Modules:
- Module 1: Core Python
Build a strong foundation in Python programming, learn how to write function, and play with datasets. - Module 2: Data Management
Learn data management techniques, data cleaning, filtering, grouping, combining from multiple sources, handling time-series and longitudinal data. - Module 3: Visualization
Learn data visualization methods, create a range of charts, statistical charts and 3D graphs.
By the end of the course, participants will have the practical skills to clean, manage, analyze, and visualize data in Python, enabling them to conduct high-quality research and policy analysis with confidence.
Curriculum
- 3 Sections
- 0 Lessons
- 12 Weeks
- Module 1: Core Python - 4 WeeksStudents learn Python programming, starting from variables and data types to loops, functions, and file handling. The goal is to make students comfortable in writing programs and handling data tasks confidently.0
- Module 2: Data Management- 4 WeeksThis section focuses on preparing datasets for analysis. Students learn how to import data, clean data, filter and group information, and manage time series. By the end, they can structure raw data into a usable form for economic and statistical work.0
- Module 3: Visualization - 4 WeeksStudents learn to communicate insights through visual representation of data. They create basic and advanced charts (2D & 3D), such as line, bar, scatter, statistical, and heatmaps, to analyze patterns, trends, and relationships.0
Requirements
- No prior programming experience needed – course starts from scratch.
- Basic understanding of statistics is helpful but not mandatory.
- Python environment installed (Anaconda recommended; setup will be guided in class). Willingness to practice daily for reinforcement of concepts.
- Laptop/PC with internet access (Windows, Mac, or Linux).
Features
- Zero to Python: Start with no prior coding knowledge.
- Hands-on Learning: Explain, demonstrate, and practice in every class.
- Data Ready Skills: Import, clean, and organize real-world datasets.
- Economics & Statistics Focus: Examples tailored for policy and research.
- Practical Statistics: Compute descriptive measures and summaries.
- Visualization Mastery: Create clear charts to reveal trends and patterns.
- Time Series Friendly: Work with economic data over time.
- Step-by-Step Progression: From coding basics to full dataset analysis.
- Error Handling Confidence: Learn how to debug and fix mistakes.
Target audiences
- Economists, statisticians, researchers, and students with no prior coding background who want to use Python for data analysis, visualization, and applied research.
- Data enthusiastic