- [Instructor] Before you get started in this course, it would be helpful if you were familiar with Jupyter Notebooks. Also, we'll be doing everything in Python, so it'd be helpful if you had a basic understanding of how Python code works and how it's organized.
Resume Transcript Auto-Scroll
Author
Ben Sullins
Released
8/9/2017
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
Skill Level Intermediate
47m 46s
Duration
52,581
Views
Show More
Show Less
-
Introduction
-
Welcome55s
-
-
1. Ingesting Data
-
Work with JSON data4m 57s
-
Work with CSV files3m 58s
-
Work with Parquet files5m 11s
-
Read data from GitHub API4m 51s
-
-
2. Exploring Data
-
Read data with Pandas5m 42s
-
Aggregate data with Pandas2m 58s
-
Export data with Pandas2m 16s
-
-
3. Visualizing Data
-
Basic charts in ggplot4m 8s
-
Styling plots in ggplot2m 55s
-
Finish your plots2m 22s
-
-
Conclusion
-
Next steps48s
-
Show MoreShow Less
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: What you should know