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Python Social Media Analytics

  • Course Code: Data Science - Python Social Media Analytics
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for those who wants to Leverage the power of Python to collect, process, and mine deep insights from social media data

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Python Social Media skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Leverage the power of Python to collect, process, and mine deep insights from social media data   
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Python Social Media expert instructor, students will learn about and explore: 

  • Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more 
  • Analyze and extract actionable insights from your social data using various Python tools 
  • A highly practical guide to conducting efficient social media analytics at scale 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand the basics of social media mining 
  • Use PyMongo to clean, store, and access data in MongoDB 
  • Understand user reactions and emotion detection on Facebook 
  • Perform Twitter sentiment analysis and entity recognition using Python 
  • Analyze video and campaign performance on YouTube 
  • Mine popular trends on GitHub and predict the next big technology 
  • Extract conversational topics on public internet forums 
  • Analyze user interests on Pinterest 
  • Perform large-scale social media analytics on the cloud 

Audience & Pre-Requisites 

This course is designed for for beginners who know the Leverage the power of Python to collect, process, and mine deep insights from social media data 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Introduction to the Latest Social Media Landscape and Importance 
  • Introduction to the Latest Social Media Landscape and Importance 
  • 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 
  1. Harnessing Social Data – Connecting, Capturing, and Cleaning 
  • Harnessing Social Data – Connecting, Capturing, and Cleaning 
  • APIs in a nutshell 
  • Introduction to authentication techniques 
  • Parsing API outputs 
  • Basic cleaning techniques 
  • MongoDB to store and access social data 
  • MongoDB using Python 
  1. Uncovering Brand Activity, Popularity, and Emotions on Facebook 
  • Uncovering Brand Activity, Popularity, and Emotions on Facebook 
  • Facebook brand page 
  • Project planning 
  • Analysis 
  • Keywords 
  • Noun phrases 
  • Detecting trends in time series 
  • Uncovering emotions 
  • How can brands benefit from it? 
  1. Analyzing Twitter Using Sentiment Analysis and Entity Recognition 
  • Analyzing Twitter Using Sentiment Analysis and Entity Recognition 
  • Scope and process 
  • Getting the data 
  • Sentiment analysis 
  • Customized sentiment analysis 
  • Named entity recognition 
  • Combining NER and sentiment analysis 
  1. Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured 
  • Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured 
  • Scope and process 
  • Getting the data 
  • Data pull 
  • Data processing 
  • Data analysis 
  1. The Next Great Technology – Trends Mining on GitHub 
  • The Next Great Technology – Trends Mining on GitHub 
  • Scope and process 
  • Getting the data 
  • Data pull 
  • Data processing 
  • Data analysis 
  1. Scraping and Extracting Conversational Topics on Internet Forums 
  • Scraping and Extracting Conversational Topics on Internet Forums 
  • Scope and process 
  • Getting the data 
  • Data pull and pre-processing 
  • Data analysis 
  1. Demystifying Pinterest through Network Analysis of Users Interests 
  • Demystifying Pinterest through Network Analysis of Users Interests 
  • Scope and process 
  • Getting the data 
  • Data pull and pre-processing 
  • Data analysis 
  1. Social Data Analytics at Scale – Spark and Amazon Web Services 
  • Social Data Analytics at Scale – Spark and Amazon Web Services 
  • Different scaling methods and platforms 
  • Topic models at scale 
  • Spark on the Cloud – Amazon Elastic MapReduce 

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