Spreadsheets stop fitting research questions at a predictable point, whether the dataset is too large, the test you need is missing from the menu, or a reviewer asks you to rerun everything with one variable changed. This course teaches Python from installation onward using real research data rather than toy examples.

This Course Offers

• Python fundamentals from installation through data types, control flow, functions, and modules, using Miniconda, Python, and Jupyter Notebook
• File handling, directory work, and importing and cleaning datasets with Pandas
• Scientific computing with NumPy and SciPy, descriptive statistics, correlations, ANOVA, t tests, and zonal statistics on spatial data
• Three sections of plotting from first graphs through specialised techniques, plus GPU acceleration setup for machine learning
• Computer vision for plant detection, a convolutional neural network in PyTorch for image classification, and crop health analysis, across three full research case studies

Why We Love This Course

1. No prior programming experience is required, since Python is taught from installation onwards.
2. The examples come from climate, air quality, and remote sensing, so the material reflects real research data.
3. Machine learning and computer vision are included, which most research focused Python courses skip.
4. Dr. Azad Rasul brings over 60 peer reviewed publications and a PhD in Geography, so the case studies reflect genuine research practice.

Reproducible analysis is increasingly expected by journals and supervisors, and code you can rerun is how that gets delivered. Do you want analysis trapped in a spreadsheet, or a script that works again with new data? Enrollment is open, and all software used is free.

• This course is perfect for researchers and postgraduate students who analyze data but have never written code.
• It suits scientists hitting the limits of SPSS, Excel, or other point and click tools.
• PhD candidates needing reproducible analysis, and environmental, climate, or geoscience researchers, will find the case studies directly applicable.

• No prior programming experience is required, since Python is taught from installation onwards.
• All software used is free, including Miniconda, Python, and Jupyter Notebook.
• Setup lectures are recorded on Windows, though the code runs on macOS and Linux as well.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.