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Home » Technology » Big Data & Data Science » Python Social Media Analytics

Course Skill Level:

Foundational

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    PYTSMAL21E09

Who should attend & recommended skills:

Beginners with basic Python experience

Who should attend & recommended skills

  • This course is geared for beginners who want to leverage the power of Python to collect, process, and mine deep insights from social media data.
  • Skill-level: Foundation-level Python Social Media skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautiful soup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this course, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes.

Skills acquired & topics covered

  • Acquiring data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more
  • Analyzing and extracting actionable insights from your social data using various Python tools
  • This highly practical guide to conducting efficient social media analytics at scale
  • The basics of social media mining
  • Using PyMongo to clean, store, and access data in MongoDB
  • User reactions and emotion detection on Facebook
  • Performing Twitter sentiment analysis and entity recognition using Python
  • Analyzing video and campaign performance on YouTube
  • Mining popular trends on GitHub and predict the next big technology
  • Extracting conversational topics on public internet forums
  • Analyzing user interests on Pinterest
  • Performing large-scale social media analytics on the cloud

Course breakdown / modules

  • Introducing social graph
  • Delving into social data
  • Understanding the process
  • Working environment
  • Getting the data
  • Analyzing the data
  • Visualizing the data
  • Getting started with the toolset

  • APIs in a nutshell
  • Introduction to authentication techniques
  • Parsing API outputs
  • Basic cleaning techniques
  • MongoDB to store and access social data
  • MongoDB using Python

  • Facebook brand page
  • Project planning
  • Analysis
  • Keywords
  • Noun phrases
  • Detecting trends in time series
  • Uncovering emotions
  • How can brands benefit from it?

  • Scope and process
  • Getting the data
  • Sentiment analysis
  • Customized sentiment analysis
  • Named entity recognition
  • Combining NER and sentiment analysis

  • Scope and process
  • Getting the data
  • Data pull
  • Data processing
  • Data analysis

  • The Next Great Technology – Trends Mining on GitHub
  • Scope and process
  • Getting the data
  • Data pull
  • Data processing
  • Data analysis

  • Scope and process
  • Getting the data
  • Data pull and pre-processing
  • Data analysis

  • Scope and process
  • Getting the data
  • Data pull and pre-processing
  • Data analysis

  • Different scaling methods and platforms
  • Topic models at scale
  • Spark on the Cloud †Amazon Elastic MapReduce

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