Analyzing Social Media Data in Python
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Mastering Real-Time Data Streams
In an era of constant digital chatter, the ability to extract actionable insights from massive datasets is a vital skill for data scientists. This intermediate-level course focuses on the practical application of Python to decode the complexities of Twitter's massive message stream.
What You Will Learn
- Data Collection: Techniques for accessing and managing high-volume message streams.
- Multi-Dimensional Analysis: How to analyze tweet text, mapping social networks, and identifying geographical origins.
- Practical Application: Using real-world datasets—ranging from tech company trends to political addresses—to drive business and political decision-making.
This course is ideal for those looking to move beyond basic scripting and into the realm of sophisticated social media intelligence, specifically focusing on topic prevalence and network diversity.
About this course
Twitter produces hundreds of million messages per day, with people around the world discussing sports, politics, business, and entertainment. You can access thousands of messages flowing in this stream in a matter of minutes. In this course, you will learn how to collect Twitter data and analyze tweet text, Twitter networks, and the geographical origin of the tweet. We'll be doing this with datasets on tech companies, data science hashtags, and the 2018 State of the Union address. Using these methods, you will be able to inform business and political decision-making by discovering the prevalence of important topics, the diversity of discussion networks, and a topic's geographical reach.
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