Analyze Tweets with Jupyter Notebooks  

Analyze and create data visualizations with Jupyter Notebooks

Last updated | By Mark Sturdevant, Rich Hagarty, David Taieb

Description

Built for the application developer who may not have data science experience or a fully dedicated data science team, this journey is the fast track to leveraging pre-enriched Twitter Insights data from Bluemix® within Jupyter Notebooks.

Overview

As part of our ongoing effort to democratize data science, this journey aims to teach application developers who have an interest in (but not necessarily a specialized focus in) data science applications. We show you how to quickly build powerful data visualizations by using IBM and open source technologies, thus eliminating the need to staff up data science teams or the time dedicated to data science classes. Accelerate your time to value based on data insights knowledge that generally takes a lot longer to build.

From this scenario, you’ll learn how to create a dashDB warehouse that contains Twitter data, such as advanced enrichments like sentiment, gender, and location. After you create an Insights for Twitter service through Bluemix, you’ll load tweets into dashDB and analyze them in Jupyter Notebook by using SparkContext and pandas (Python data analysis library). With Jupyter, you’ll be able to easily share results with others. We’ll also demonstrate how you can create visualizations with Matplotlib and Google GeoChart.

Flow

  1. The developer adds the Bluemix services needed for this application, dashDB for Analytics, and Insights for Twitter.
  2. The developer creates a notebook within Bluemix by using the DSX Spark Service.
  3. SparkContext enables the developer to run tasks on the Spark cluster.
  4. dashDB analyzes the loaded, specified tweets from Twitter.

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