
Course Overview, Structure and Contents
Introduction to Data Science in Chemistry
Molecular Models Types
- Line Model
- Stick Model
- Ball and Stick Model
- CPK Model
- Cartoon Model
- Polyhedral Model
Chemical structure formulas commonly used in everyday practice are difficult for computers to interpret directly. To effectively manage structural information on a computer, it's crucial to first convert these formulas into a format that computers can easily process.
We will explore the various file formats used to represent chemical structures digitally and how they facilitate the storage, sharing, and analysis of chemical data across different platforms.
In this lecture, you'll learn how to convert chemical structures between different file formats.
Setting up Python environment and Getting started with Jupyter Notebooks
Know the difference between Jupyter Notebook Versions.
how to retrieve Compound Information from the PubChem Database ?
Automating Chemical Data Retrieval and Database Creation from PubChem Database
Chemical Data Retrieval from ChemSpider Database
Getting started with Openbabel and Pybel and how to convert chemical structures between different file formats with Python.
Visualizing Chemical Structures in 3D in Jupyter Notebooks
Basic understanding of how to add interactivity to Notebooks
Enhance your workflow by integrating ipywidgets
Your task is to visualize the structure from any chemical file format.
Dive into the exciting world of data science in chemistry with this comprehensive beginner-friendly course! Start Out with Data Science in Chemistry and Cheminformatics offers a complete introduction to the key concepts and tools transforming the way we understand and manipulate chemical data.
Designed for newcomers, this course provides a hands-on approach to cheminformatics, bridging the gap between data science and chemistry through step-by-step tutorials, practical exercises, and real-world applications.
You’ll learn how to use Python powerful libraries like RDKit to manage chemical data, visualize molecular structures, and explore essential cheminformatics techniques.
By the end of this course, you’ll have a strong foundation in computational methods, data handling, and visualization, and be equipped to apply data science tools to solve complex chemical problems.
Whether you’re a student, researcher, or professional seeking to expand your skill set, this course is the perfect entry point to the dynamic fields of data science and cheminformatics.
What You’ll Learn:
Fundamentals of data science and its applications in chemistry
Using Python and essential cheminformatics libraries (RDKit, Openbabel, PubChemPy, and more)
How to handle, analyze, and visualize chemical data
Generating, storing, and managing molecular structures
Applications of data science to solve real-world chemistry challenges
Who Should Enroll: This course is ideal for chemistry students, researchers, and professionals with basic python syntax understanding. If you’re interested in learning how data science can open new possibilities in chemical research and development, this course is designed with you in mind. Join us and start your journey in data science and cheminformatics today!