Everyone is using social media to share their life experiences, initiate ideas and provide opinions in a free and open way. Businesses are hence interested in understanding what people think and say about their products and services. They are augmenting their business applications to extract, understand and analyze social media data about them. If you are working or hoping to work in the analytics world, you need to enrich your skill set with social media analytics to improve your market value.
This Social Media Analytics with R course helps you achieve exactly that ! It introduces you to the tools and technologies required to extract social media data. Twitter, Facebook and Google interfaces are covered. It then walks through multiple use cases for analyzing this data and generating business insights. The examples range from simple histograms to advanced machine learning techniques. After completing this course, you will be able to execute end-to-end social media analytics projects and integrate them with existing business applications.
This course requires previous R experience.
What challenges exist that are specific to social media analytics
An Overview of REST API technologies
An introduction to authentication and authorization with OAuth
An overview of twitter data, authentication and authorization
Examples of using TwitterR libraries for connecting to twitter and extracting data
How to setup Google+ APIs for data extraction
Examples of using Google R library to connect to Google+ and extract data
An overview of Facebook data, authentication and authorization
Examples of using Facebook R library to connect to Facebook and extract data
Introduction to the types of use cases covered in this course
Perform basic frequency analysis
Perform sentiment analysis of tweets and posts
Analyze links - friends, followers and and find patterns
Understand social media actions and find patterns
Extract deep meanings - find items that frequently occur together and understand what they mean
Get real time streaming data from social media and then analyze them in real time and extract meaning
Types of machine learning - Supervised and unsupervised.
Converting text into numeric representation using TF-IDF
Use clustering to group similar messages with R
Classify messages into pre-defined classes using Naive Bayes Classification algorithm
Linking social media data with enterprise data sources
V2 Maestros is dedicated to teaching big data / data science at affordable costs to the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building data science talent across the world.