The following course structure describes eScience educational materials by topic. If you know about additional materials or topics that should be included, please submit an issue to this repo.
Keep Learning! Keep Growing!
- Software Carpentry Lessons
- Python Data Science Handbook
- Software Engineering for Data Scientists
- Still Magic for Research Projects
- CSE 583: Software Development for Data Scientists (syllabus with links to videos and slides)
- Create and manipulate elementary types and lists
- Read and write CSV and Excel files
- Create and manipulate DataFrames
- Basics of flow control (if, for)
- Create functions
- Organize code into files
There are two versions of Python in common use: Python 2 and Python 3. There are some small differences between them. You likely will use Python 3 more, but don’t hesitate to take advantage of a Python 2 tutorial.
- Hitchhiker's Guide to Python - Beginner’s tutorial (Python 3)
- Code academy (Python 2)
- Python Seminar 2015
- Testing. See CSE 583 syllabus
- Debugging and exceptions. See CSE 583 syllabus
- Design. See CSE 583 syllabus
- Creating reusable packages. See CSE 583 syllabus
- Create and manipulate elementary types and lists
- Read and write CSV and Excel files
- Create and manipulate DataFrames
- Basics of flow control (if, for)
- Create functions
- Organize code into files
- Git initialization
- Creating a local repository
- Creating a remote repository
- Cloning a local repository
- Add, remove files
- Committing and pushing a change
- Obtaining a previous version of a file
- Viewing the changes made since the last commit
- Git workflow
- Resolving conflicts
- Managing branches
- Using issues
- See CSE 583 syllabus
- w3schools JavaScript Introduct
- CodeAcademy Introduction to JavaScript
- Javascript.info Introduction to JavaScript
- See CSE 583 syllabus
- HPC for python with Dask
- Resolving conflicts
- Managing branches
- Using issues
- Introduction to SQL and Geospatial Data Processing (DSSG)
- Geohackweek Tutorials
-
Coursera Machine Learning. A bit more advanced.
- Introduction to Data Structures and Algorithms
- Data Structures and Algorithms in slides
- Basics of Data Structures in Python
- Statistical basics: probability distributions, sampling, variability, estimation vs. prediction vs. hypothesis testing
- Residual analysis
- Model evaluation: metrics, cross validation
- Parameter estimation