Data Science in Chemistry from Udemy

What's inside

Learning objectives

The use of data science in chemistry
Structure representation and smiles
Convert between chemical file formats
Cheminformatics w/ rdkit + py3dmol
Molecules databases

Data scraping from compound databases using python
Visualizing and manipulating molecular structure using python
Chemistry + python
Building datasets with rdkit

The use of data science in chemistry
Structure representation and smiles
Convert between chemical file formats
Cheminformatics w/ rdkit + py3dmol
Molecules databases
Data scraping from compound databases using python
Visualizing and manipulating molecular structure using python
Chemistry + python
Building datasets with rdkit

Syllabus

Understanding and representing chemical structures.

Know more about course structure and contents

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.

A heavy-duty introduction to Cheminformatics with RDKit.

What are Molecular Descriptors ? and How to calculate them ?

How to do substructure search and find the Maximum Common Substructure ?

Accelerate your Data Analysis process when working with molecules and Build a dataset.

How to run reactions and Chemical transformations with RDKit ?

Rendering 3D Structures in notebooks with py3Dmol,

Integrate ipywidgets + RDKit + py3Dmol

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a hands-on approach to cheminformatics, which bridges the gap between data science and chemistry through tutorials, exercises, and real-world applications

Uses Python libraries like RDKit, Openbabel, and PubChemPy, which are essential for managing chemical data and visualizing molecular structures

Covers essential computer-friendly formats for handling compounds in cheminformatics and conversion between them, which facilitates data storage and analysis

Requires setting up a Python environment and using Jupyter Notebooks, which may require learners to install software and libraries

Teaches data scraping from compound databases using Python, which is a useful skill for researchers and professionals

Focuses on using Openbabel and Pybel to construct and convert molecules, which are tools that may require some familiarity with command-line interfaces

Reviews summary

Introduction to data science in chemistry

According to learners, this course serves as an excellent introduction to the intersection of data science and chemistry, offering a solid foundation in key areas. Students appreciated that it covers essential cheminformatics concepts like SMILES, chemical file formats, and the RDKit library. The course includes hands-on Python exercises which were found helpful. However, some reviewers felt that the pace was occasionally rushed and that Python examples could benefit from more detail or better integration, suggesting that some learners might need to consult supplementary material. Overall, it is considered a good starting point for those new to the field.

Includes helpful hands-on coding and exercises.

"Instructor did a great job making complex topics accessible and providing clear explanations and hands-on exercises."

"particularly the hands-on Python parts and RDKit introduction were very helpful."

"I felt the Python examples could be more detailed or better integrated"

Covers essential concepts like SMILES, RDKit.

"covers essential cheminformatics concepts such as SMILES, chemical file formats, PubChem and ChemSpider databases, RDKit, and py3Dmol."

"particularly the introduction to RDKit was very helpful."

"I found the introduction to cheminformatics concepts useful, especially SMILES and file formats."

Provides a strong base for beginners.

"an excellent introduction to the intersection of data science and chemistry"

"a good starting point for anyone interested"

"This course provided me with a strong foundation"

Some parts felt rushed and needed supplements.

"felt the Python examples could be more detailed or better integrated"

"I felt some parts felt rushed"

"I felt I needed supplementary material"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science in Chemistry with these activities:

Review Basic Python Syntax

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Strengthen your understanding of Python syntax to prepare for using libraries like RDKit and Openbabel.

Browse courses on Python Syntax

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Complete a Python tutorial covering basic syntax.
Write small programs to practice using loops, conditionals, and functions.
Review data structures like lists, dictionaries, and tuples.

Read 'Python for Data Analysis' by Wes McKinney

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Learn data manipulation techniques using Pandas to prepare for handling chemical datasets.

View Python for Data Analysis: Data Wrangling with... on Amazon

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Read the chapters on Pandas data structures and data cleaning.
Practice using Pandas to load, clean, and transform sample datasets.

Follow RDKit Tutorials

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Gain hands-on experience with RDKit by working through official tutorials and examples.

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Work through the RDKit Cookbook tutorials.
Explore the RDKit documentation for specific functions and modules.
Try to reproduce examples from research papers using RDKit.

Four other activities

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Write a Blog Post on Molecular Visualization

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Solidify your understanding of molecular visualization by explaining the concepts in a blog post.

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Research different molecular visualization techniques.
Write a clear and concise explanation of each technique.
Include examples of how to use Python libraries like py3Dmol.
Publish your blog post on a platform like Medium or GitHub Pages.

Build a Small Compound Database

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Apply your knowledge by creating a database of chemical compounds with relevant properties.

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Choose a set of compounds to include in your database.
Use PubChemPy to retrieve data for each compound.
Store the data in a Pandas DataFrame or SQLite database.
Implement functions to search and filter the database.

Read 'Cheminformatics: A Basic Introduction' by Gasteiger and Engel

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Gain a deeper understanding of cheminformatics principles and techniques.

View Chemoinformatics: Basic Concepts and Methods on Amazon

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Read the chapters on molecular descriptors and similarity searching.
Take notes on key concepts and definitions.
Relate the concepts to the practical examples covered in the course.

Contribute to RDKit Documentation

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Deepen your understanding of RDKit by contributing to its documentation.

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Identify areas in the RDKit documentation that need improvement.
Write clear and concise explanations of specific functions or modules.
Submit your contributions to the RDKit project.

Career center

Learners who complete Data Science in Chemistry will develop knowledge and skills that may be useful to these careers:

Cheminformatics Scientist

A cheminformatics scientist applies data analysis and computational techniques to solve problems in chemistry. This role involves managing chemical data, visualizing molecular structures, and creating computational models. This course is designed for those new to data science and chemistry, providing hands-on experience with Python and cheminformatics libraries like RDKit. By learning to handle, analyze, and visualize chemical data, you'll build a foundation in computational methods, crucial for this line of work. The course’s syllabus on chemical structure representation and file format conversion directly translates to the daily tasks and challenges faced by a cheminformatics scientist.

See salaries and explore the career path for Cheminformatics Scientist

Computational Chemist

Computational chemists develop and apply theoretical methods and software to study chemical problems. They use computational techniques to predict chemical properties, simulate chemical reactions, and analyze experimental data. This course helps aspiring computational chemists by providing a strong foundation in handling and visualizing chemical data using Python and essential cheminformatics libraries. The course's emphasis on using Python and tools like RDKit, Openbabel, and PubChemPy will be invaluable. Visualizing molecular structures and generating datasets for analysis, as covered in the course, are fundamental for success as a computational chemist.

See salaries and explore the career path for Computational Chemist

Data Scientist

Data scientists analyze large datasets to extract meaningful insights and develop data-driven solutions. While this role spans many industries, this course specifically prepares you for applications within the chemical sciences. The course provides a comprehensive introduction to data science concepts and tools, with a focus on cheminformatics. You'll learn how to use Python and libraries like RDKit to manage chemical data, visualize molecular structures, and apply data science techniques to solve chemical problems. For a data scientist focused on chemistry, the course's modules on data scraping from compound databases and building datasets with RDKit will be particularly valuable.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop and implement machine learning models. The models require data. While this role spans many industries, this course specifically prepares you for applications within the chemical sciences. The course provides a comprehensive introduction to data science concepts and tools, with a focus on cheminformatics. You'll learn how to use Python and libraries like RDKit to manage chemical data, visualize molecular structures, and apply data science techniques to solve chemical problems. The ability to automate chemical data retrieval and database creation from PubChem and ChemSpider will be particularly valuable.

See salaries and explore the career path for Machine Learning Engineer

Environmental Chemist

Environmental chemists analyze chemical compounds in the environment to assess pollution levels and develop solutions for environmental problems. This course helps by providing a foundation in data science and cheminformatics. With the course's tools to manage chemical data and visualize molecular structures, you are able to understand the behavior and impact of chemical pollutants in the environment. The course’s modules on data scraping from compound databases and building datasets with RDKit will be particularly valuable for environmental chemists who need to gather and analyze data on environmental contaminants.

See salaries and explore the career path for Environmental Chemist

Research Chemist

A research chemist conducts experiments and analyzes data to advance scientific knowledge in chemistry. This course helps research chemists by providing a foundation in data science and cheminformatics. With the course, they can use Python and tools like RDKit to manage chemical data, visualize molecular structures, and apply data science techniques to solve complex chemical problems. The data scraping and dataset building components of the course are particularly valuable for research chemists who need to efficiently gather and analyze data.

See salaries and explore the career path for Research Chemist

Data Analyst

Data analysts examine and interpret data to identify trends and patterns that can inform business decisions. While a data analyst role can be broad, this course is perfectly suited for a data analyst specializing in chemical data. The course introduces key concepts in data science and cheminformatics with Python. You'll gain hands-on experience in handling, analyzing, and visualizing chemical data, using libraries such as RDKit. The focus on building datasets with RDKit makes the course particularly relevant for a data analyst who focuses on processing and interpreting chemical information.

See salaries and explore the career path for Data Analyst

Food Chemist

Food chemists analyze the chemical composition of food products to ensure safety and quality. This course may be useful to food chemists because it provides a foundation in data science and cheminformatics. The course's introduction to Python and cheminformatics libraries like RDKit can be applied to analyzing the chemical components of food and identifying potential contaminants. For food chemists, the skills learned in this course can enhance their ability to analyze and manage data related to food safety and quality.

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Pharmaceutical Scientist

Pharmaceutical scientists are involved in the research, development, and manufacturing of pharmaceutical products. They use their knowledge of chemistry, biology, and pharmacology to design and test new drugs. This course may be useful to pharmaceutical scientists because it provides a foundation in data science and cheminformatics that can be applied to drug discovery and development. Skills such as data scraping from compound databases and building datasets with RDKit, are particularly relevant for identifying and analyzing potential drug candidates. The ability to manage and visualize chemical data effectively will be a unique advantage in this field.

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Materials Scientist

Materials scientists research and develop new materials with specific properties. This often involves analyzing chemical structures and properties using computational tools. This course may be useful as it provides a practical introduction to handling, analyzing, and visualizing chemical data, as well as managing molecular structures using Python and libraries like RDKit. The skills acquired in this course can be applied to materials research, particularly in areas that involve computational modeling and data analysis of chemical compounds. The lessons on visualizing molecular structures in 3D and constructing molecules using Openbabel will be especially helpful.

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Bioinformatics Analyst

Bioinformatics analysts analyze biological data using computational tools and techniques. While this course focuses on chemistry, the underlying data science principles and programming skills can be transferable. This course may be useful because it provides a foundation in Python and data manipulation that are also relevant in bioinformatics. The ability to handle, analyze, and visualize data, as well as build datasets using programming, will be essential skills for a bioinformatician. The use of RDKit, though chemistry-focused, introduces concepts applicable to other data analysis tasks.

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Software Developer

Software developers design, develop, and test software applications. This course may be useful to software developers who want to specialize in developing cheminformatics or data analysis tools for the chemistry field. The course provides a foundation in using Python and cheminformatics libraries like RDKit, Openbabel, and PubChemPy, which are essential for building software applications that handle chemical data. The course's emphasis on data handling, visualization, and managing molecular structures will be a valuable asset for any software developer in this domain.

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Database Manager

Database managers are responsible for organizing, storing, and maintaining databases. This course may be useful to database managers who work with chemical data. The course emphasizes using Python to handle, analyze, and visualize chemical data, which are essential skills for managing databases of chemical compounds. For database managers, the course's modules on data scraping from compound databases and constructing molecules using Openbabel are particularly relevant. These skills will help them efficiently populate and maintain chemical databases.

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Science Writer

Science writers communicate scientific information to the public through articles, blog posts, and other media. This course may be helpful because it provides a solid foundation in data science and cheminformatics, enabling you to understand and explain complex chemical concepts more effectively. The ability to visualize molecular structures and handle chemical data using Python, as taught in this course, can enhance your ability to create engaging and informative content. For science writers the insight into how data science is impacting chemistry could be used as a basis for new stories.

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Laboratory Technician

Laboratory technicians assist scientists in conducting experiments and analyzing samples. This course may be useful to laboratory technicians who want to expand their skills in data analysis. The course provides an introduction to data science and cheminformatics, teaching how to use Python to handle and visualize chemical data. This can help laboratory technicians improve their ability to analyze experimental results and manage data more effectively. The course's modules on visualizing and manipulating molecular structures using Python could assist in better understanding experimental outcomes.

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Data Science in Chemistry

What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Introduction to data science in chemistry

Activities

Career center

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