Space Tech: CanSat Data Analysis with Machine Learning

Space technology experiments like CanSat missions generate valuable atmospheric data. In this course, you will learn how to analyze real CanSat telemetry data using Python and Machine Learning techniques.

This beginner-friendly course walks you through a practical workflow used in real space experiments. You will start by understanding CanSat missions and the type of sensor data collected during flight experiments.

Next, you will analyze real telemetry data including:

• Temperature
• Pressure
• Humidity
• Altitude

Using Python, you will clean and visualize the data to understand atmospheric behavior during the experiment.

The course then introduces a simple Machine Learning model to predict altitude based on atmospheric parameters. This helps demonstrate how data science techniques can be applied to space technology experiments.

You will also compare your CanSat data with NASA satellite weather datasets to understand how ground experiments relate to real satellite observations.

Finally, the course shows how to automate data reporting using n8n, allowing you to build a simple workflow that generates and sends experiment reports automatically.

By the end of this course, you will have a clear understanding of how space experiment data can be analyzed, modeled, and automated using modern tools.

This course is ideal for students interested in space technology, data science, and real-world engineering experiments.