Master data handling, visualization, and real-world chemical analysis with Python—no coding or data science background n
Chemistry generates data at every level — from titration curves and kinetic measurements to spectroscopy outputs and thermodynamic models. Yet, many chemists struggle to efficiently analyze and visualize this data in a way that leads to clear insights and impactful communication. This course, Data Analysis & Plotting for Chemistry with Python, is designed to bridge that gap.
What you’ll learn
- Learn to efficiently manage, clean, and preprocess chemical datasets using Python..
- Import, clean, and manipulate raw chemical data using Python libraries (pandas, numpy, etc.)..
- Generate professional-quality plots (2D, 3D, interactive) tailored to chemistry problems..
- Analyze spectroscopic, thermodynamic, and kinetic datasets to draw research-level conclusions..
- Reproduce and interpret data analyses from published chemistry research articles.
- Apply best practices in reproducibility, documentation, and data-driven research..
Course Content
- Introduction –> 1 lecture • 3min.
- some basic python –> 14 lectures • 56min.
- Numpy –> 34 lectures • 1hr 33min.
- Pandas –> 19 lectures • 1hr 33min.
- Plotting of a spectrum through MatPlotLib.PyPlot –> 11 lectures • 50min.
Requirements
Chemistry generates data at every level — from titration curves and kinetic measurements to spectroscopy outputs and thermodynamic models. Yet, many chemists struggle to efficiently analyze and visualize this data in a way that leads to clear insights and impactful communication. This course, Data Analysis & Plotting for Chemistry with Python, is designed to bridge that gap.
Starting with the basics of Python, you will quickly progress to mastering essential libraries such as NumPy and Pandas, for handling and analyzing chemical datasets. You will then learn to create high-quality plots using Matplotlib ensuring your graphs are not just scientifically accurate but also publication-ready. Every concept is explained in the context of chemistry, with real datasets and case studies drawn from spectroscopy, kinetics, thermodynamics, and analytical chemistry.
By the end of the course, you will be able to clean and organize experimental data, perform numerical analyses, and present your results with professional-grade visualizations. Whether you are a student preparing lab reports, a researcher analyzing experiments, or a professional aiming to improve reporting workflows, this course will give you the practical skills to transform raw chemical data into clear, meaningful insights. Moreover, this course will pave your easy way to machine learning where large datasets are handled, analyzed and interpreted.
No prior coding experience is required — just curiosity and a willingness to learn.