YoVDO

Data Science Foundations: Python Scientific Stack

Offered By: LinkedIn Learning

Tags

Data Science Courses Data Analysis Courses Data Visualization Courses Machine Learning Courses Matplotlib Courses pandas Courses NumPy Courses scikit-learn Courses Folium Courses

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
1. Visual Studio Code
  • Working with VS Code
  • Using code cells
  • Extensions to Python language
  • Understanding markdown cells
2. NumPy Basics
  • NumPy overview
  • NumPy arrays
  • Slicing
  • Boolean indexing
  • Understanding broadcasting
  • Understanding array operations
  • Understanding ufuncs
  • Challenge: Working with an image
  • Solution: Working with an image
3. pandas
  • 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
4. Installing Packages
  • Introduction to Python packages
  • Using environments
  • Managing dependencies
  • Challenge: Creating requirements
  • Solution: Creating requirements
5. folium and Geo
  • 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
6. NYC Taxi Data
  • 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
7. scikit-learn
  • scikit-learn introduction
  • Regression
  • Understanding train/test split
  • Preprocessing data
  • Composing pipelines
  • Saving and loading models
  • Challenge: Hand-written digits
  • Solution: Hand-written digits
8. Plotting
  • 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
9. Other Packages
  • Other packages overview
  • Going faster with Numba
  • Understanding deep learning
  • Working with image processing
  • Understand NLP
  • Working with bigger data
10. Development Process
  • Development process overview
  • Understanding source control
  • Code review
  • Testing overview
  • Testing example
Conclusion
  • Next steps

Taught by

Miki Tebeka

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