Data Management for Scientists & Engineers
Track Data Analysis Track, Managerial Track
Data is a valuable scientific resource. In this course you will learn best practices about data stewardship as well as the digital skills needed to store, retrieve, and manage data.
While learning the technical Python skills required to effectively store and retrieve data from text files, relational databases, and structured data files, students will also gain exposure to the need for a data management plan, metadata, and long-term storage.
After this course, students will have both the skills and mindset needed to transform raw data collected for a single experiment into a long-term resource that can be mined for multiple purposes.
This course explores solutions for managing complex scientific data. Following Python Foundations, instruction supports the next step in the Manager Track and Data Analysis Track of study.
This course requires basic proficiency with Python and the scientific Python stack. Some practical experience with Jupyter Notebooks, NumPy (ndarrays), Pandas (DataFrames), and scientific visualization in Python using Matplotlib are essential to working with the code and concepts presented in this course.
If you have taken Enthought’s Python Foundations for scientists and Engineers, you have the requisite background knowledge for this course.
To be announced.
Enthought instructors have advanced degrees in scientific fields such as physics, engineering, computer science, and mathematics, and all have extensive experience through research and consulting in applying Python to solve complex problems across a range of industries, allowing them to bring their real world experience to the classroom every day.
csv, json, pandas, pickle, re, sqlite, uuid, yaml
Download the syllabus for this course here.
Interested in this course?
For more information, contact the Enthought Academy team.
Our Scientific Python Experts
Enthought Academy instructors are scientists and engineers themselves and have deep knowledge and understanding of the strategies and technologies covered in each track, and extensive practical experience applying Python to solve complex challenges across a range of science-based industries.
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