Linkedin - Python For Data Science Tips Tricks And Techniques
- Category Other
- Type Tutorials
- Language English
- Total size 227.7 MB
- Uploaded By xHOBBiTx
- Downloads 375
- Last checked 14 hours ago
- Date uploaded 1 year ago
- Seeders 21
- Leechers 2
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.
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.
Files:
Linkedin - Python For Data Science Tips Tricks And Techniques 1. Introduction- 1. Discover new tips and tricks for Python.mp4 (4.3 MB)
- 2. What you should know.mp4 (381.8 KB)
- 3. Using the exercise files.mp4 (3.1 MB)
- 4. Set up on Windows.mp4 (6.4 MB)
- 5. Set up on Mac.mp4 (4.9 MB)
- 1. Working with JSON data.mp4 (21.8 MB)
- 2. Working with CSV files.mp4 (7.6 MB)
- 3. Working with Parquet files.mp4 (23.2 MB)
- 4. Reading data from GitHub API.mp4 (13.7 MB)
- 1. Reading data with pandas.mp4 (20.9 MB)
- 2. Inspecting DataFrames with pandas.mp4 (9.3 MB)
- 3. Aggregating data with pandas.mp4 (12.8 MB)
- 4. Exporting data with pandas.mp4 (11.9 MB)
- 1. Basic charts in Plotly.mp4 (15.2 MB)
- 2. Small multiples with Plotly.mp4 (14.3 MB)
- 3. Using color in Plotly.mp4 (12.3 MB)
- 4. Finishing your data visualizations in Plotly.mp4 (12.6 MB)
- 1. Next steps and additional resources.mp4 (1.5 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 (7.0 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) data
- MonthlyProductSales.csv (515.1 KB)
- MonthlyProductSales.parquet (74.2 KB)
- MonthlySales.csv (1.0 KB)
- anscombes-quartet-hier.csv (0.5 KB)
- anscombes-quartet.csv (0.6 KB)
- diamonds.csv (2.6 MB)
- monthlySalesbyCategoryMultiple.json (1.6 KB)
- mpg.csv (15.7 KB)
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