Linkedin - Python For Data Science Tips Tricks And Techniques

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Linkedin - Python For Data Science Tips Tricks And Techniques (Size: 227.7 MB)
  01_01.ipynb 11.7 KB
  01_02.ipynb 9.2 KB
  01_03.ipynb 2.4 MB
  01_04.ipynb 7.3 KB
  02_01.ipynb 3.9 KB
  02_02.ipynb 18.2 KB
  02_03.ipynb 1.7 KB
  02_04.ipynb 7 KB
  03_01.ipynb 8.3 MB
  03_02.ipynb 2.5 MB
  03_03.ipynb 14.8 MB
  03_04.ipynb 398.1 KB
  1. Basic charts in Plotly.mp4 15.2 MB
  1. Discover new tips and tricks for Python.mp4 4.3 MB
  1. Next steps and additional resources.mp4 1.5 MB
  1. Reading data with pandas.mp4 20.9 MB
  1. Working with JSON data.mp4 21.8 MB
  2. Inspecting DataFrames with pandas.mp4 9.3 MB
  2. Small multiples with Plotly.mp4 14.3 MB
  2. What you should know.mp4 381.8 KB
  2. Working with CSV files.mp4 7.6 MB
  3. Aggregating data with pandas.mp4 12.8 MB
  3. Using color in Plotly.mp4 12.3 MB
  3. Using the exercise files.mp4 3.1 MB
  3. Working with Parquet files.mp4 23.2 MB
  4. Exporting data with pandas.mp4 11.9 MB
  4. Finishing your data visualizations in Plotly.mp4 12.6 MB
  4. Reading data from GitHub API.mp4 13.7 MB
  4. Set up on Windows.mp4 6.4 MB
  5. Set up on Mac.mp4 4.9 MB
  MonthlyProductSales.csv 515.1 KB
  MonthlyProductSales.parquet 74.2 KB
  MonthlySales.csv 1 KB
  anscombes-quartet-hier.csv 512 B
  anscombes-quartet.csv 614.4 B
  diamonds.csv 2.6 MB
  monthlySalesbyCategoryMultiple.json 1.6 KB
  mpg.csv 15.7 KB
  ▲ 38 total files

Description


Quote:

Course details

Modern work in data science requires skilled professionals versed in analysis workflows and using powerful tools. Python can play an integral role in nearly every aspect of working with data—from ingest, to querying, to extracting and visualizing. In this course, instructor Ben Sullins highlights tips and tricks you can use right away to improve your skills in Python. Learn how to work with JSON data, CSV files, and Parquet files. Explore ways to read, inspect, and aggregate data using pandas. Plus, find out how to visualize data using basic charts, small multiples, and color in Plotly, as well as how to put the finishing touches on your data visualizations.

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