Social Media Analytics with Python

Learn to extract and analyze data from Twitter, Facebook, Google and other social media sites
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  • Lectures 28
  • Length 3.5 hours
  • Skill Level Intermediate Level
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 4/2016 English

Course Description

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 Python 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 python experience.

What are the requirements?

  • Python

What am I going to get from this course?

  • Appreciate how businesses use Social Media data
  • Learn how to extract social media data
  • Transform social media data to be ready for analytics
  • Execute a number of use cases for social media analytics

Who is the target audience?

  • Analytics Professionals
  • IT students
  • Data Analysts

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Introduction
About V2 Maestros

Contains slide handouts, example code and datasets

Section 2: Social Media Analytics Overview

Data present in Social media - content, links and comments

Social Media Applications

What challenges exist that are specific to social media analytics


An overview of REST API technologies


An introduction to authentication and authorization with OAuth

Social Media Analytics Quiz
3 questions
Section 3: Twitter Analytics

An overview of twitter data, authentication and authorization.

Getting Twitter data with Python
Section 4: Google+ Analytics
Google+ data API Overview

Connect to Google+ and extract data using python modules

Section 5: Facebook Analytics

An overview of Facebook data, authentication and authorization


Connect to Facebook and extract data using Python modules

Section 6: Analytics Use Cases
Introduction to Use Cases

Frequency Analysis use case


Sentiment Analysis use case


Link Analysis use case


Action Analysis use case


Mining frequently occurring patterns in social media


Mining and analyzing data in real time - sentiment analysis

Section 7: Advanced Topics

Overview of machine learning - supervised and unsupervised learning


Converting text into numeric representation with TF-IDF


K-means clustering


Naive Bayes Classification

Linking data from other sources
Section 8: Conclusion
Closing Remarks
BONUS Lecture : Other courses you should check out

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Instructor Biography

V2 Maestros, LLC, Big Data Science / Analytics Experts | 10K+ students

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.

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