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Build a Streaming Twitter Filter with Python and Redis
Rating: 4.6 out of 5(240 ratings)
13,385 students

Build a Streaming Twitter Filter with Python and Redis

Learn how to use Twitter's Streaming API, Redis and Flask together.
Created byTy Shaikh
Last updated 10/2018
English

What you'll learn

  • Learn how to use Tweepy to interact with Twitter's API
  • Learn how to use Redis to setup a local key-value store
  • Learn about sentiment analysis
  • Learn how to pre-process and format data in Python
  • Learn how to display data from Redis via Flask and Jinga2

Explore related topics

Course content

1 section • 8 lectures • 1h 5m total length

  • Introduction5:55

    Talks about the project and data pipeline

  • Twitter Stream11:58

    Show how to ingest the Twitter stream

  • Design & Front-end Development8:01

    Design a pen & paper mockup, then convert to front-end code

  • Sentiment Analysis10:22

    Explain sentiment analysis and how to do it using Python

  • Redis Server14:27

    Explain Redis and show how to setup a local server

  • Data Formatting4:27

    Prepare a custom Python class to preprocess and format data fields

  • Flask Server7:22

    Build a simple Flask server to combine all the code so far

  • Next Steps2:44

    Explores further improvements to the pipeline

Requirements

  • Understanding of Python and web development

Description

This video series will walk through building a streaming Twitter filter using Python and Redis. 


Here is a synopsis of each video:

  1. Talks about the project and data pipeline

  2. Show how to ingest the Twitter stream

  3. Explain sentiment analysis and how to do it using Python

  4. Explain Redis and show how to setup a local server

  5. Design a pen & paper mockup, then convert to front-end code

  6. Prepare a custom Python class to preprocess and format data fields

  7. Build a simple Flask server to combine all the code so far

  8. Explores further improvements to the pipeline



Who this course is for:

  • Intermediate programmers who are interested in data engineering

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