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This repository contains all the Python materials (mainly Jupyter notebook) we use for our training courses. They are meant to introduce the Python programming language (Python for Beginners) and Python related tools for Data Science, Machine Learning, Earth Science, Geospatial Analysis, etc. We do our best to make the materials self-contained. However, a few topics require large data files that only available during live presentations.
Here is a list of topics/tools we have materials on.
- Introduction to Jupyter Notebook
- Introduction to Version Control with Git
- Creating and Maintaining Git Repositories with Github
- Basic Data Types
- Data Structures
- Conditional Statements
- For Loops and While Loops
- Functions and Modules
- Basic Manipulation of Text Files
- Manipulating YAML Files
datetimeModuleNumPy- Visualization with
Matplotlib Pandas- Web Scraping (
Requests,BeautifulSoup)
- Basic Concepts of Artificial Intelligence (AI) and Machine Learning (ML)
- Overview of Artificial Neural Networks
- Exploratory Data Analysis with Python
- AI/ML Modeling with
Scikit-Learn- Regression problem
- Image classification problem
- AI/ML Modeling with
TensorFlow- Logistic regression classifier problem
- Regression problem
- Image classification problem
- AI/ML Modeling with
PyTorch- Logistic regression classifier problem
- Regression problem
- Image classification problem
- Modeling with DINOv2
- Python Coding Standards
- Object Oriented Programming
- Exception Handling
- Packaging and Deployment
- List Comprehension
- Optimizing Python Applications
- Speeding your Application with
Numba - Scaling your Application with
Dask
CuPyCuDFcuMLNumba
- Python Decorators
- Passing Parameters to Applications (
argparse,click,configparser)
CartopyHoloViewsGeoViews
- Manipulating Scientific Data Format Files
netCDF4h5pypyhdf
Xarray
ShapelyGeoPandasMovingPandas
SciPy- Serialization and Deserialization with
pickleandjson

