Python for Data Science Tips, Tricks, & Techniques

Python for Data Science Tips, Tricks, & Techniques
Python for Data Science Tips, Tricks, & Techniques
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 47 min | 139 MB

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. This course highlights twelve tips and tricks you can put into practice to improve your skills in Python. These techniques are readily applied and in common data management tasks and include the following: how to ingest data using CSV, JSON, and TXT files; how to explore data using libraries like Pandas; how to organize and join data using DataFrames; how to create charts and graphic representations of data using ggplot in Python; and more.

Topics include:

  • Working with flat files, including Parquet
  • Reading data using APIs or libraries
  • Inspecting and aggregating data with Pandas
  • Exporting data with Pandas
  • Creating charts using ggplot
  • Styling plots using ggplot
  • Finishing data visualizations
Table of Contents

1 Welcome
2 What you should know
3 Using the exercise files
4 Work with JSON data
5 Work with CSV files
6 Work with Parquet files
7 Read data from GitHub API
8 Read data with Pandas
9 Inspect DataFrames with Pandas
10 Aggregate data with Pandas
11 Export data with Pandas
12 Basic charts in ggplot
13 Small multiples with ggplot
14 Styling plots in ggplot
15 Finish your plots
16 Next steps