[100% Off] Programming For Scientific Research With Python And R
Learn both languages from scratch – data wrangling, statistics, visualisation, AI and three research case studies
What you’ll learn
- Write working code in both Python and R – variables
- data types
- control flow
- functions and packages,Import
- clean
- manipulate and export scientific datasets in both languages,Run the statistics your research needs: descriptive stats
- correlation
- t-tests
- ANOVA and multiple linear regression,Produce publication-quality figures with Matplotlib and ggplot2
- including animated graphs and wind rose plots,Apply deep learning to scientific data in R
- and process geospatial data in Python,Work through three full case studies: LAI and LST
- India air quality with ML
- and climate data analysis,Choose the right language for a given research task instead of defaulting to the one you know
Requirements
- No programming experience required – both languages are taught from installation onwards,The installation lectures use Windows; the code itself runs on macOS and Linux too,All software used is free: Miniconda
- Python
- Jupyter Notebook and R,An interest in research data – the examples come from climate
- air quality and remote sensing
Description
Python or R? Learn both, and stop guessing.
Researchers waste a lot of time on this question. One colleague swears by R, another by Python, and the honest answer is that each is better at different parts of the job. This course teaches you both from installation onwards, and shows you where each one earns its place.
You will start by setting up Miniconda, Jupyter and R, then cover the fundamentals in parallel – data types, control flow, functions, modules and packages in both languages. From there into the work researchers actually do: file handling and directories in Python, importing, exporting and manipulating data in R.
The statistics your research needs
NumPy and SciPy in Python, and in R the tests you will be asked for in review: descriptive statistics, correlations, t-tests, ANOVA and multiple linear regression. Then visualisation – three lectures on Matplotlib, plus basic and advanced plotting in R, animated graphs, report generation, and wind rose plots (with a homework exercise using real data from the Erbil station).
Three full case studies
Leaf Area Index and Land Surface Temperature from satellite data
Analysing India’s air quality data with machine learning, across four lectures
Climate data analysis, across four lectures
There is also an introduction to artificial intelligence, deep learning in R, geospatial data processing in Python, and calculating remote sensing indices.
What you get
Over five hours across 56 lectures, taught in both languages
Seven quizzes and a practice test
Real research data, not toy examples
Code you can adapt straight into your own work
Before you enrol
No programming experience is needed – both languages start from installation. The installation lectures are recorded on Windows, though the code runs on macOS and Linux as well. Everything used is free.
Taught by Dr. Azad Rasul, a geospatial data scientist and Assistant Professor, with over 150,000 students enrolled across his Udemy courses.
Enrol now and start analysing your research data in the language that suits it.
