Unlock the power of location-based insights with data analysis and GIS. This hands-on course gives you the tools, techniques, and technologies—like Python, SQL, and QGIS—to turn raw data into clear, actionable information with a geographic edge. Whether you're just starting a career in data or looking to grow your skills in geoinformation, this course will prepare you to tackle the challenges of today’s data-driven world with confidence.
Data Analysis and GIS
Data ManagementData Management
Data Analysis and GIS
Data analysis and Geographic Information Systems (GIS) are two of the most powerful fields shaping our digital world. As more organizations rely on data to drive decisions, the ability to understand not only what the data says, but also where it applies, has become a major advantage. That’s where the fusion of data analysis and GIS comes in.
Data analysis helps you clean, organize, and interpret numbers to uncover patterns, answer questions, and make informed decisions. GIS adds a critical layer—location. It lets you work with data tied to places, allowing you to visualize and explore information on maps. This spatial perspective makes it easier to see trends and connections that would be invisible in spreadsheets alone.
When you bring these two together, you can do so much more—from mapping traffic patterns and identifying at-risk ecosystems to planning smarter cities and predicting flood zones. GIS transforms complex data into interactive, easy-to-understand visuals that help people across all industries—from urban planning and logistics to disaster response and climate science—make better choices, faster.
As location intelligence becomes more important across sectors, knowing how to work with data and GIS is no longer optional—it’s essential. This course helps you build that exact skill set through a mix of real-world examples, expert instruction, and practical, hands-on experience.
What will you learn
In this course, you’ll explore the foundations of data analysis and GIS, and learn how to use them together to make sense of complex, location-based information. You’ll start by diving into Python, one of the most in-demand programming languages for data science. You’ll learn how to read, clean, and transform datasets, perform calculations, and create meaningful visualizations using powerful libraries like Pandas, NumPy, and Matplotlib.
From there, you’ll build your SQL skills, which are crucial for managing and querying large datasets. SQL gives you the ability to pull exactly the information you need from a database, combine it in useful ways, and make your data ready for further analysis or mapping. You’ll also discover how SQL works hand-in-hand with GIS tools to handle spatial queries and manage geodata more effectively.
Then, you’ll move into QGIS—an open-source platform used around the world for geographic analysis and mapping. You’ll learn to visualize data on maps, perform spatial analysis, and create compelling, interactive presentations that make your insights easy to understand and share.
Every part of the course is built around practical learning. You won’t just read about these tools—you’ll use them. By the end, you’ll be comfortable applying your skills to real-world scenarios, from analyzing traffic flow and predicting flood zones to mapping customer trends and planning sustainable cities.
Why choose this course
If you’re looking for a course that combines modern tools, expert guidance, and real-world relevance, this one’s for you. It’s designed to take you from theory to practice—and get you job-ready in the process.
- Hands-on learning: You’ll work directly with tools like Python, SQL, and QGIS in practical exercises and projects, so you can build skills that stick.
- Up-to-date content: The course is constantly updated to reflect the latest trends and technologies in the field of data and GIS.
- Experienced instructors: You’ll learn from professionals who know the industry and can teach you what really matters in the workplace.
- Career-ready skills: The course is designed with your future in mind—everything you learn is in demand across fields like urban planning, environmental management, logistics, and tech.
Course structure
Day 1
You’ll begin with an overview of data analysis and its connection to key technologies like Python and SQL. You’ll also compare these tools to traditional software like Matlab and SPSS, giving you helpful context for the rest of the course. The day concludes with your first hands-on Python session, where you’ll learn how to read in data, perform basic operations, answer simple statistical questions, and create basic visualizations.
Day 2
The focus shifts to GIS as you dive into QGIS. You’ll explore its capabilities for visualizing data in both raster and vector formats. You’ll also learn to create thematic maps, overlay multiple layers, adjust layer visibility by scale, and add clear, readable labels. The day wraps up with map-based analysis exercises across various disciplines.
Day 3
You’ll return to Python and take a deeper dive into the core concepts of the language. You’ll learn about Python’s object-oriented structure, how to write clean, logical code, and how to work with its powerful built-in libraries. This day builds the foundation for more advanced programming techniques.
Day 4
Building on the previous day, you’ll write more advanced scripts, use data structures like lists, dictionaries, and tuples, and apply control flow to automate your analysis tasks. You’ll also learn how to write Python code that’s efficient, reusable, and easy to understand.
Day 5
This hands-on day focuses entirely on SQL. You’ll install PostgreSQL, load a sample database, and run a wide range of queries. You’ll practice SELECT statements, INNER and OUTER JOINs, subqueries, views, and data manipulation commands like UPDATE and DELETE. By the end of the day, you’ll be comfortable navigating and managing data in a relational database environment.
Day 6
The final day ties everything together. You’ll explore functions, variable scope, lambda expressions, and modular programming. You’ll also dive into object-oriented programming with classes, inheritance, and exception handling. Finally, you’ll connect your Python scripts to a database using the Python Database API—bringing your full data workflow together from analysis to application.
Learning Outcomes
- Understand the core concepts of data analysis and GIS, and how they work together in real-world applications
- Become confident in using Python for collecting, cleaning, analyzing, and visualizing data
- Master SQL for managing and querying large datasets, including spatial data
- Learn how to create clear, compelling maps and spatial visualizations using QGIS
- Apply data analysis techniques to geospatial problems across industries like urban planning, climate science, and public policy
- Build object-oriented Python applications that can process and analyze complex geodata
- Gain the skills to work independently on data projects that require both statistical and geographic insights
- Be prepared for roles in geoinformation, environmental analysis, logistics, and beyond
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
Fequently Asked Question about the Masterclass Data Analysis and GIS
In this course, you will learn how to apply GIS technology for data analysis and visualization on geographic maps, using Python and SQL.
Basic knowledge of GIS is useful, but the course starts with an introduction, making it accessible for beginners as well.
The masterclass offers a practical approach to Python in data science, focusing on applications in GIS and data analysis.
Yes you learn to work with SQL, or Structured Query Language, is a powerful and standardized language used for managing and manipulating relational databases. It allows you to interact with data stored in these databases by performing tasks such as querying, updating, inserting, and deleting data.
Yes, GIS is a key component of the course, where you will learn how to use this tool for geographic data analysis. Both QGIS and ArcGIS are covered.
Some experience with programming is helpful but not required. The course includes an introduction to Python specifically for GIS applications.
Yes, the course begins with an introduction to the fundamentals of data science, with a focus on geographic applications.
The course starts with an introduction to data science and Python, followed by practical applications in GIS using Python and SQL, including the use of QGIS.