Python for Scientific Research

Name: Python for Scientific Research
Rating: 4.2 (318 reviews)

Master Python for Scientific Research with Practical Examples

Created bySenior Assist Prof Azad Rasul

Last updated 8/2026

English

English [Auto],

What you'll learn

Master Data Handling: Learn to creatively manipulate, import, and export data using Python.
Perform Statistical Analysis: Gain proficiency in descriptive statistics, correlations, ANOVA, and t-tests for research.
Create Professional Graphs: Develop skills in creating basic, advanced, and animated graphs with Python.
Apply Python in Research: Use Python to process data, perform analyses, and visualize results in scientific research.
Enhance Research Creativity: Unlock unlimited possibilities by applying Python creatively to your research challenges.

Course content

8 sections • 37 lectures • 4h 22m total length

Welcome and Course Overview4:10
Learn to set up Python with Miniconda and Jupyter, master data types, control flow, and libraries, handle files, and apply NumPy, SciPy, visualization, and geospatial AI to real-world case studies.
Installing Miniconda and Python 3 on Windows1:51
There are various ways to install and run Python along with its tools and libraries. In this learning series, we will be using "Miniconda" to install and run Python. Miniconda is a smaller version of Anaconda that comes with Python, conda, and a few pre-installed packages, including pip.
To get started, visit the Miniconda website: https://docs.conda.io/en/latest/miniconda.html and download the appropriate version for your computer. For example, I will be downloading "Miniconda 3 Windows 64-bit" for my computer.
Setting Up Python Environments and Installing Python and Packages3:53
Installing and Running Jupyter Notebook.3:17
Setting Up Your Environment

Running Your First Python Program5:23
Python Basics
What is the correct file extension for Python files?
Understanding Data Types in Python3:02
What is the correct syntax to output the type of a variable or object in Python?
Control Flow in Python1:19
Control Flow Challenges
How do you insert COMMENTS in Python code?
Creating and Using Functions in Python2:19
Function Fundamentals
Working with Modules in Python2:32
Learn to work with modules in Python by creating packages from related modules, saving and importing models, and using built-in and installer package imports.

File Handling in Python3:37
Learn Python file handling with the open function, using modes for reading, appending, writing, and creating files, including opening and closing a sample data file.
File Operations
Managing Directories in Python2:49
Import and Clean Datasets in Jupyter Notebook with Pandas12:48
Import a crop health csv in pandas, inspect for missing values and outliers, fill gaps with means, clip ndVi to -1 and 1, and convert health to binary.
Directory Management

Introduction to Scientific Computing with NumPy10:26
Advanced Scientific Computing with SciPy4:27
Advance scientific computing with SciPy by describing data and performing interpolation and integration. Handle data formats MATLAB nets and IDF, import and generate X and Y data, and evaluate p-values.
Basic Statistics in Python8:10
Explore descriptive statistics in Python by computing mean, median, variance, standard deviation, and percentiles; analyze correlations, covariance, and linear regression using pandas and SciPy.
Performing Zonal Statistics with Python3:50
Scientific Computing Essentials

Introduction to Geospatial AI1:12
Step-by-Step Guide to GPU Setup for Machine Learning & Deep Learning!9:45
Detecting and Counting Plants Using Computer Vision Techniques10:18
Master geospatial data processing to detect and count plants with Python, using Geopandas, Rasterio, and OpenCV to read shapefiles, mask rasters, and extract plant coordinates.
Building a Convolutional Neural Network for Image Classification with PyTorch13:15
Building a Machine Learning Model for Crop Health Analysis8:31
Geospatial AI Tools

Case Study 1 - LAI and LST Analysis (Part 1)6:48
Case study one LAI and LST part one
Case Study 1 - LAI and LST Analysis (Part 2)5:34
Case study one LAI and LST part two
Case study 2 Part1: Analyzing India's Air Quality Data with Machine Learning9:10
Case Study 2 - Analyzing India's Air Quality with Machine Learning (Part 2)14:07
Case Study 2 - Analyzing India's Air Quality with Machine Learning (Part 3)18:45
Explore exploratory data analysis (EDA) of India's air quality data using visualizations, correlations, and regression models to forecast AQI with feature selection and cross-validation.
Case Study 2 - Analyzing India's Air Quality with Machine Learning (Part 4)11:25
Case Study 3 - Climate Data Analysis Using Python (Part 1)2:46
Case Study 3 - Climate Data Analysis Using Python (Part 2)9:32
Learn to use Xarray to open multiple data files, concatenate datasets, and save netcdf, then work with monthly and annual precipitation analyses for Kansas using masking, plotting, and region averages.
Case Study 3 - Climate Data Analysis Using Python (Part 3)9:44
Explore interpolation between CPC club and grade met minimum air temperature datasets at different resolutions, convert longitude ranges, scale kelvin to celsius, and compute moving averages for Texas regions.
Case Study 3 - Climate Data Analysis Using Python (Part 4)20:49
Calculating Remote Sensing Indices in Python7:12
Insights from Case Studies

Requirements

No prior programming experience is required

Description

Are you looking for a powerful and versatile tool to enhance your research capabilities? This course is your gateway to mastering Python for scientific research, where you'll learn through real-world examples across various fields.

As an Assistant Professor of Remote Sensing and a Senior GBD Collaborator with over a decade of experience in Python, R programming, and Big Data, I am excited to guide you on this journey. With a Ph.D. in Geography (Remote Sensing) and over 60 peer-reviewed publications, I bring extensive expertise in data analysis, remote sensing, and climate studies to help you excel.

In this course, you'll gain hands-on experience in:

Data Manipulation: Learn to import, export, and manipulate data efficiently using Python.
Statistical Analysis: Master techniques like descriptive statistics, multi-correlation, ANOVA, and t-tests.
Graph Creation: Create basic, advanced, and animated graphs to visualize your research findings.

This course is designed to not only make you proficient in Python but also empower you to use your creativity in data processing and analysis. Unlike restrictive software like SPSS or Excel, Python offers unlimited possibilities, allowing you to tailor your research tools to your specific needs.

Each lecture is crafted to provide you with actionable insights that you can apply immediately in your research. By the end of the course, you’ll be able to confidently use Jupyter Notebook for scientific research and develop custom Python scripts to tackle complex research challenges.

Take the first step towards elevating your research with Python. Enroll today, and let's unlock the full potential of your research capabilities together.

Sincerely,

Assist. Prof. Azad Rasul

Who this course is for:

Researchers and students who are attempting to efficiently use Python programming for their scientific research.

Python for Scientific Research

What you'll learn

Explore related topics

Course content

Introduction and Setup4 lectures • 13min

Python Programming Fundamentals5 lectures • 15min

File Handling and Directories3 lectures • 19min

Scientific Computing and Statistics4 lectures • 27min

Data Visualization3 lectures • 25min

Geospatial Analysis and AI5 lectures • 43min

Case Studies and Applications11 lectures • 1hr 56min

Conclusion2 lectures • 5min

Requirements

Description

Who this course is for: