This course will introduce you about how to use Twitter API to do data analysis in Python. It begins explaining how to connect and get the data from Twitter API, then it demonstrates how to use a NoSQL database to store the collected data. After demonstrate how to prepare the data, it will show how to do text and network analysis using the libraries available for Python. Finally, you will learn how to present the results from the analysis in an effective way using charts.
About This Book
* Collect and store Twitter data using MongoDB
* Apply text mining techniques, create complex networks, and calculate its metrics
* Create useful graphics to present the analysis results
Who This Book Is For
Any Python developer with interests in data science applied to social networks can get valuable skills from this course by learning how to connect to the Twitter API, collect desired data, wrangling it, and transform all those data in insight for decision makers.
What You Will Learn
* Connect and get data from the Twitter REST and Streaming API's
* Use MongoDB to store unstructured JSON files
* Prepare Twitter data for data analysis
* Perform text analysis using Twitter data
* Perform network analysis using Twitter data
* Visually present data using graphics
In Detail
Twitter is a massive social network tuned towards fast communication. More than 330 million active users publish over 500 million 240- character "Tweets" every day. Twitter's speed and ease of publication have made it an important communication medium for people from all walks of life. At the other hand we have Python, a high-level programming language created by Guido van Rossum in 1991. Due to its flexibility and ease to use, the language is today, one of the most used programming languages to do machine learning and data science tasks in general.
This course is for who is interested in understanding the basics of collecting, storing, and analyzing Twitter data using Python.
The first half of this course explains collection and storage of data. It starts by explaining how to collect Twitter data, looking at the free APIs provided by Twitter. We then go on to demonstrate how to prepare and store this data in a tangible way for use in data science tasks.
The second half of this course is about analysis and visualization. Here, the focus is on how to apply basic text mining techniques on the data. Also it is explained how to do common measures and algorithms that are used to analyze networks. We finish the analysis by explaining visual analytics, an approach which helps humans inspect the data through intuitive visualizations.
Sprache
Verlagsort
Maße
Höhe: 235 mm
Breite: 191 mm
ISBN-13
978-1-78995-906-2 (9781789959062)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Elder Santos is an accomplished Python software engineer and data scientist specializing in social network analysis with expertise in machine learning techniques to design, prototype, test, implement, and deploy data science solutions. Holding an MSc in machine learning, his research field is social networks analysis. He is passionate about big data and always looking for solutions to real-world problems. Harshit Tyagi is a Full Stack Developer and Data Engineer at Elucidata, a Cambridge based Biotech company. He develops algorithms for research scientists at one of the world's best medical schools like Yale, UCLA, and MIT. He is a Python evangelist and loves to contribute to tech communities. With the skills acquired over years and being a mentor and reviewer for more than 2 years in the E-learning era, it'd be great to share his enterprise-grade practices in the market for budding data scientists.
