What you'll learn:
- Set up Python, Miniconda and Jupyter Notebook, and write your first program with no prior experience
- Import, clean, manipulate and export research data with Pandas
- Run descriptive statistics, correlations, ANOVA and t-tests on your own datasets
- Use NumPy and SciPy for scientific computing, including zonal statistics on spatial data
- Create basic, advanced and specialised graphs to present your findings
- Apply AI to research data: computer vision plant counting, a PyTorch CNN, and a crop health model
- Work three case studies end to end: LAI and LST, India air quality with ML, and climate data analysis
Your data is bigger than your spreadsheet, and your analysis needs to be repeatable.
Most researchers reach a point where the tool stops fitting the question - the dataset is too large, the test you need is not in the menu, or a reviewer asks you to re-run everything with one variable changed. Python solves all three, and this course teaches it from the beginning, with research data rather than toy examples.
You will install Miniconda, Python and Jupyter Notebook and write your first program, then cover data types, control flow, functions and modules. From there into the work itself: file handling, directories, and importing and cleaning datasets with Pandas.
The analysis
Scientific computing with NumPy and SciPy
Descriptive statistics, correlations, ANOVA and t-tests
Zonal statistics on spatial data
Three sections of plotting, from first graphs to specialised techniques
Then the part most Python courses for researchers leave out
Setting up GPU acceleration for machine learning
Detecting and counting plants with computer vision
Building a convolutional neural network in PyTorch for image classification
Training a machine learning model for crop health analysis
Three full case studies
Leaf Area Index and Land Surface Temperature analysis
India's air quality data analysed with machine learning, across four lectures
Climate data analysis, across four lectures
Before you enrol
No programming experience is needed. The setup lectures are recorded on Windows, though the code runs on macOS and Linux as well, and every tool used is free. Fourteen quizzes run through the course so you can check your understanding as you go.
Taught by Dr. Azad Rasul, Assistant Professor of Remote Sensing, with a PhD in Geography, over 60 peer-reviewed publications, and more than 150,000 students enrolled across his Udemy courses.
Enrol now and start doing your analysis in code that you - and your reviewers - can run again.
