Data Science Foundations: Python Scientific Stack
Offered By: LinkedIn Learning
Course Description
Overview
Learn how to use the Python scientific stack to solve problems and complete common data science tasks.
Syllabus
Introduction
- The Python scientific stack
- What you should know
- Setting up
- Working with VS Code
- Using code cells
- Extensions to Python language
- Understanding markdown cells
- NumPy overview
- NumPy arrays
- Slicing
- Boolean indexing
- Understanding broadcasting
- Understanding array operations
- Understanding ufuncs
- Challenge: Working with an image
- Solution: Working with an image
- pandas overview
- Loading CSV files
- Parsing time
- Accessing rows and columns
- Calculating speed
- Displaying speed box plot
- Challenge: Taxi data mean speed
- Solution: Taxi data mean speed
- Introduction to Python packages
- Using environments
- Managing dependencies
- Challenge: Creating requirements
- Solution: Creating requirements
- Creating an initial map
- Drawing a track on a map
- Using geo data with Shapely
- Challenge: Drawing the running track
- Solution: Drawing the running track
- Examining data
- Loading data from CSV files
- Working with categorical data
- Working with data: Hourly trip rides
- Working with data: Rides per hour
- Working with data: Weather data
- Challenge: Graphing taxi data
- Solution: Graphing taxi data
- scikit-learn introduction
- Regression
- Understanding train/test split
- Preprocessing data
- Composing pipelines
- Saving and loading models
- Challenge: Hand-written digits
- Solution: Hand-written digits
- Matplotlib overview
- Using styles
- Customizing pandas output
- pandas plotting
- Using Matplotlib with pandas
- Interactive plots
- Other plotting packages
- Challenge: Stocking data bar charts
- Solution: Stocking data bar charts
- Other packages overview
- Going faster with Numba
- Understanding deep learning
- Working with image processing
- Understand NLP
- Working with bigger data
- Development process overview
- Understanding source control
- Code review
- Testing overview
- Testing example
- Next steps
Taught by
Miki Tebeka
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