Data Science Foundations: Python Scientific Stack
Overview
Learn how to use the Python scientific stack to complete data science tasks. Find out how to work with pandas for data crunching, NumPy for numeric computation, and more.
Data science provides organizations with striking—and highly valuable—insights into human behavior. While data mining can seem a bit daunting, you don't need to be a highly-skilled programmer to process your own data. In this hands-on course, learn how to use the Python scientific stack to complete common data science tasks. Miki Tebeka covers the tools and concepts you need to effectively process data with the Python scientific stack, including Pandas for data crunching, matplotlib for data visualization, NumPy for numeric computation, and more.
Data science provides organizations with striking—and highly valuable—insights into human behavior. While data mining can seem a bit daunting, you don't need to be a highly-skilled programmer to process your own data. In this hands-on course, learn how to use the Python scientific stack to complete common data science tasks. Miki Tebeka covers the tools and concepts you need to effectively process data with the Python scientific stack, including Pandas for data crunching, matplotlib for data visualization, NumPy for numeric computation, and more.
Syllabus
Introduction
- Welcome
- What you should know
- Mac setup
- Windows setup
- Linux setup
- How to use the exercise files
- Ramp up with Scientific Python
- Start the notebook server
- Use code cells
- Extensions to Python language
- Understand markdown cells
- Edit notebooks
- Overview: NumPy
- NumPy arrays
- Slicing
- Learn Boolean indexing
- Understand broadcasting
- Understand array operations
- Understand ufuncs
- Pandas overview
- Load CSV files
- Parse time
- Access rows and columns
- Use pure Python packages
- Calculate speed
- Display a speed box plot
- Introduction to Python packages
- Manage environments
- Create an initial map
- Draw a track on the map
- Use geo data with Shapely
- Generate a report
- Examine data
- Load data from CSV files
- Work with categorical data
- Work with data: Hourly trip rides
- Work with data: Rides per hour
- Work with data: Weather data
- Introduction: scikit-learn
- Learn regression on Boston dataset
- Understand train/test splits
- Preprocess data
- Compose pipelines
- Save and load models
- Overview: matplotlib
- Use styles
- Customize Pandas output
- Use matplotlib
- Tips and tricks
- Understand bokeh
- Other packages overview
- Go faster with Numba and Cython
- Understand deep learning
- Work with image processing
- Understand NLP: NLTK
- Understand NLP: SpaCy
- Bigger data with HDF5 and dask
- Overview
- Understand source control
- Learn code review
- Testing overview
- Testing example
- Next steps
Taught by
Miki Tebeka
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