Data Management for Natural Scientists
Description
Data Management for Natural Scientists offers a practical guide for scientific processing of data. It covers the way from "getting hands on" experimental results to ensuring their use for addressing various scientific questions. Code snippets are provided in order to introduce the proposed workstream and to demonstrate the adjustability to specific challenges.
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Person
Matthias Hofmann
holds a Ph.D. in Physical Chemistry from the University of Regensburg. Currently, he is working in the chemical industry where his focus is on streamlining data processing routines and ensuring the accessibility of experimental data.
Content
- Intro
- Acknowledgment
- Contents
- 1 Presenting the challenge
- 2 Python quick start
- 3 The steps of data processing
- 4 From experimental files to data
- 5 From data to information
- 6 Where to put data and information
- 7 How to visualize data and information
- 8 Responding to lessons learned
- 9 Where to go from here
- 10 Conclusion
- A Packaging a custom module
- B Comments to tables and columns via SQLAlchemy
- C A word on version control systems
- D Overview of utilized Python and package versions
- E Extracting data via pd.read_csv
- F Installation of an ODBC driver
- List of Figures
- Bibliography
- Index
