Python for Data Science Tips, Tricks, & Techniques

48mIntermediate2017-08-09

Authors

Ben Sullins

Data Geek, Tech Consultant

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. 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.

Learning objectives
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

Skills covered

Tips, Tricks, & TechniquesPythonData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware Development

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Using the exercise files

1. Ingesting Data

04 - Work with JSON data
05 - Work with CSV files
06 - Work with Parquet files
07 - Read data from GitHub API

2. Exploring Data

08 - Read data with Pandas
09 - Inspect DataFrames with Pandas
10 - Aggregate data with Pandas
11 - Export data with Pandas

3. Visualizing Data

12 - Basic charts in ggplot
13 - Small multiples with ggplot
14 - Styling plots in ggplot
15 - Finish your plots

Conclusion

16 - Next steps