Data Science in Chemistry

Thorsten Gressling

Data Science in Chemistry

Examples

Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter Series: De Gruyter Textbook De Gruyter | 2021

The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing.

A uniquely concise introduction for chemists looking to tap the potential of data science.
Contains numerous working examples in Jupyter

Credits to the coders

The book would not have been possible without the passion and the contributions of hundreds of scientists who had listed their programs on GitHub! I thank Greg Landrum, Oliver Beckstein, Daniel Pelliccia, Björn Dahlgreen, and Jean-Baptiste Lamy, five individuals who are part of the GitHub community.

Contact

In case of errors or comments, please open an issue on github.com/Gressling/ examples. I am available on LinkedIn or ResearchGate for further contact and discussion.

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
data		data
07-2 Using CUDA in Python.ipynb		07-2 Using CUDA in Python.ipynb
08-1 Make Jupyter a REST Server.ipynb		08-1 Make Jupyter a REST Server.ipynb
08-2 Jupyter Notebook for retrieving from REST APIs.ipynb		08-2 Jupyter Notebook for retrieving from REST APIs.ipynb
08-3 MQTT subscriber for Jupyter (paho).ipynb		08-3 MQTT subscriber for Jupyter (paho).ipynb
09-2 Plot serial data read from Arduino.ipynb		09-2 Plot serial data read from Arduino.ipynb
09-3 Raspberry GPIO access to log laboratory data.ipynb		09-3 Raspberry GPIO access to log laboratory data.ipynb
11-1 Using modin instead of pandas.ipynb		11-1 Using modin instead of pandas.ipynb
11-2 cuDF + rapid.ai dataframe.ipynb		11-2 cuDF + rapid.ai dataframe.ipynb
11-3 Lambda, filter and map example.ipynb		11-3 Lambda, filter and map example.ipynb
13-1 Runtime preparation Google Colab (in the first cell).ipynb		13-1 Runtime preparation Google Colab (in the first cell).ipynb
14-1 Papermill example.ipynb		14-1 Papermill example.ipynb
15-1 Notebook interaction examples.ipynb		15-1 Notebook interaction examples.ipynb
15-2.1 intake example.ipynb		15-2.1 intake example.ipynb
15-3.1 debugging with ipdb.ipynb		15-3.1 debugging with ipdb.ipynb
15-4.1 Example hvPlot.ipynb		15-4.1 Example hvPlot.ipynb
15-5.1 PlantUML.ipynb		15-5.1 PlantUML.ipynb
15-6.1 tqdm example.ipynb		15-6.1 tqdm example.ipynb
17-2 Work with a workflow via KNIME.ipynb		17-2 Work with a workflow via KNIME.ipynb
17-3 Excel in Python with openpyx.ipynb		17-3 Excel in Python with openpyx.ipynb
17-4 Use Python in Excel with xlwings.ipynb		17-4 Use Python in Excel with xlwings.ipynb
18-1 Access HDFS file system.ipynb		18-1 Access HDFS file system.ipynb
18-2 Capture production logs with FLUME		18-2 Capture production logs with FLUME
18-3 Massive data broker KAFKA .ipynb		18-3 Massive data broker KAFKA .ipynb
19-1 PySpark Example		19-1 PySpark Example
19-2 RDD Example on Hadoop		19-2 RDD Example on Hadoop
19-3 Spark configuration example		19-3 Spark configuration example
21-1 Parsing data from file Chemical Markup Language (CML) .ipynb		21-1 Parsing data from file Chemical Markup Language (CML) .ipynb
22-1 Simple ETL example		22-1 Simple ETL example
22-2.1 ETL with bonobo		22-2.1 ETL with bonobo
23-2.1 Build pandas pipelines using pdpipe		23-2.1 Build pandas pipelines using pdpipe
23-3 Salt strip in ChEMBL structure pipeline		23-3 Salt strip in ChEMBL structure pipeline
24-2.1 Query ChemSpider online		24-2.1 Query ChemSpider online
24-2.1 Query ChemSpider online.ipynb		24-2.1 Query ChemSpider online.ipynb
24-3.1 Query Wikidata and Wikipedia		24-3.1 Query Wikidata and Wikipedia
24-4.1 Query ChEBI ontology via Python		24-4.1 Query ChEBI ontology via Python
24-5.1 Query ChEBI data via REST		24-5.1 Query ChEBI data via REST
25-1.1 SQL with a relational database		25-1.1 SQL with a relational database
25-2.1 Neo4J graph example.ipynb		25-2.1 Neo4J graph example.ipynb
25_1_1_SQL_with_a_relational_database.ipynb		25_1_1_SQL_with_a_relational_database.ipynb
25_1_SQL_with_a_relational_database.ipynb		25_1_SQL_with_a_relational_database.ipynb
26-1 Confusion matrix example		26-1 Confusion matrix example
29-1 MNIST comparison in major NN frameworks		29-1 MNIST comparison in major NN frameworks
31-3 UBER Ludwig model training		31-3 UBER Ludwig model training
32-1.1 Symbolic mathematics with Sympy		32-1.1 Symbolic mathematics with Sympy
34-1 Bayesian Linear Regression with pymc3.ipynb		34-1 Bayesian Linear Regression with pymc3.ipynb
36-1.1 k-nearest neighbors algorithm (k-NN).ipynb		36-1.1 k-nearest neighbors algorithm (k-NN).ipynb
36-2.1 Gaussian and complement Naïve Bayes (scikit).ipynb		36-2.1 Gaussian and complement Naïve Bayes (scikit).ipynb
36-3.1 Support Vector Machine SVM (scikit).ipynb		36-3.1 Support Vector Machine SVM (scikit).ipynb
95-1.1 Running Shor's algorithm (IBM qiskit, on QPU).ipynb		95-1.1 Running Shor's algorithm (IBM qiskit, on QPU).ipynb
LICENSE		LICENSE
README.md		README.md
gressling test.ipynb		gressling test.ipynb

License

Gressling/examples

Folders and files

Latest commit

History

Repository files navigation

Data Science in Chemistry

Examples

Credits to the coders

Contact

About

Topics

Resources

License

Stars

Watchers

Forks

Languages