Python for Data Science Tips, Tricks, & Techniques

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.

Course Timeline:

Welcome Preview

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What you should know Preview

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Using the exercise files Preview

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Work with JSON data Preview

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Work with CSV files

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Work with Parquet files

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Read data from GitHub API

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Inspect DataFrames with Pandas

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Read data with Pandas

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Aggregate data with Pandas

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Export data with Pandas

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Basic charts in ggplot

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Small multiples with ggplot

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Styling plots in ggplot

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Finish your plots

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Python for Data Science Tips, Tricks, & Techniques Next steps Preview

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