Social Media Data Analytics

About this course: Learner Outcomes: After taking this course, you will be able to: - Utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr. - Process the collected data - primarily structured - using methods involving correlation, regression, and classification to derive insights about the sources and people who generated that data. - Analyze unstructured data - primarily textual comments - for sentiments expressed in them. - Use different tools for collecting, analyzing, and exploring social media data for research and development purposes. Sample Learner Story: Data analyst wanting to leverage social media data. Isabella is a Data Analyst working as a consultant for a multinational corporation. She has experience working with Web analysis tools as well as marketing data. She wants to now expand into social media arena, trying to leverage the vast amounts of data available through various social media channels. Specifically, she wants to see how their clients, partners, and competitors view their products/services and talk about them. She hopes to build a new workflow of data analytics that incorporates traditional data processing using Web and marketing tools, as well as newer methods of using social media data. Sample Job Roles requiring these skills: - Social Media Analyst - Web Analyst - Data Analyst - Marketing and Public Relations Final Project Deliverable/ Artifact: The course will have a series of small assignments or mini-projects that involve data collection, analysis, and presentation involving various social media sources using the techniques learned in the class.

Who is this class for: This course is for students who have at least some beginning training in technologies, including programming and databases. Specifically, it is expected that the student has done some programming before such as C, Java, Python, PHP, Perl, Pascal, Cobol, and JavaScript. The minimal training from any of such programming language expected is the understanding of variable declaration (x=10), condition checking (if x==10), and simple loops (while x<10). The student may or may not have some knowledge of statistics for data analysis - an optional statistics module will be included for reference.

Created by:   Rutgers the State University of New Jersey

  • Chirag Shah
    Taught by:    Chirag Shah, Associate Professor
    Information and Computer Science
Commitment4 weeks of study, 3-6 hours/week
How To PassPass all graded assignments to complete the course.
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Average User Rating 3.9See what learners said
Introduction to Data Analytics
In this first unit of the course, several concepts related to social media data and data analytics are introduced. We start by first discussing two kinds of data - structured and unstructured. Then look at how structured data, the primary focus of this course, is analyzed and what one could gain by doing such analysis. Finally, we briefly cover some of the visualizations for exploring and presenting data. Make sure to go through the material for this unit in the sequence it's provided. First, watch the four short videos, then take the practice test, followed by the two quizzes. Finally, read the documents about installation and configuration of Python and R. This is very important - before proceeding to the next units, make sure you have installed necessary tools, and also learned how to install new packages/libraries for them.
4 videos4 readings
  1. Video: Video-1: Introduction
  2. Video: Video-2: Structured vs. Unstructured Data
  3. Video: Video-3: Analyzing Structured Data
  4. Video: Video-4: Visualization of Data
  5. Reading: Anaconda Installation
  6. Reading: Python installation, configuration, and usage
  7. Reading: R installation
  8. Reading: R/RStudio Setup Guide (on Windows)
  9. Discussion Prompt: Installation and Configuration of Development Environment
Graded: Quiz-1
Graded: Quiz-2
Collecting and Extracting Social Media Data
In this unit we will see how to collect data from Twitter and YouTube. The unit will start with an introduction to Python programming. Then we will use a Python script, with a little editing, to extract data from Twitter. A similar exercise will then be done with YouTube. In both the cases, we will also see how to create developer accounts and what information to obtain to use the data collection APIs. Once again, make sure to go item-by-item in the order provided. Before beginning this unit, ensure that you have all the right tools (Python, R, Anaconda) ready and configured. The lessons depend on them and also your ability to install required packages.
4 videos6 readings
  1. Video: Video-1: Introduction
  2. Reading: Errata: please read this first
  3. Video: Video-2: Introduction to Python Programming
  4. Reading: Python Packages Installation
  5. Reading: (Optional) Introduction to Python for Econometrics, Statistics and Data Analysis
  6. Video: Video-3: Using Python to Extract Data from Twitter
  7. Reading: Script:
  8. Reading: Twitter libraries
  9. Video: Video-4: Using Python to Extract Data from YouTube
  10. Reading: Script:
Graded: Python Programming Exercise
Graded: Twitter data download using Python
Graded: YouTube data download using Python
Data Analysis, Visualization, and Exploration
In this unit, we will focus on analyzing and visualizing the data from various social media services. We will first use the data collected before from YouTube to do various statistics analyses such as correlation and regression. We will then introduce R - a platform for doing statistical analysis. Using R, then we will analyze a much larger dataset obtained from Yelp. Make sure you have covered the material in the previous units before proceeding with this. That means, having all the tools (Anaconda, Python, and R) as well as various packages installed. We will also need new packages this time, so make sure you know how to install them to your Python or R. If needed, please review some basic concepts in statistics - specifically, correlation and regression - before or during working on this unit.
4 videos7 readings
  1. Video: Video-1: Introduction
  2. Video: Video-2: Analyzing Social Media Data Using Python
  3. Reading: Script:
  4. Discussion Prompt: Statistical Analysis with Twitter Data
  5. Video: Video-3: Introduction to R
  6. Reading: Data: iqsize.csv
  7. Reading: R Installation Guide
  8. Reading: Installing R Packages
  9. Reading: Statistical Analysis with R
  10. Video: Video-4: Social Media Data Analysis with R
  11. Reading: Scripts for converting json to csv
  12. Reading: Data Visualization with ggplot2 (R) - Cheat Sheet
  13. Discussion Prompt: Data Visualization using R
Graded: Statistical Analysis with Twitter Data
Graded: Data Visualization using R
Case Studies
In the final unit of this course, we will work on two case studies - both using Twitter and focusing on unstructured data (in this case, text). The first case study will involve doing sentiment analysis with Python. The second case study will take us through basic text mining application using R. We wrap up the unit with a conclusion of what we did in this course and where to go next for further learning and exploration.
4 videos4 readings
  1. Video: Video-1: Introduction
  2. Video: Video-2: Sentiment Analysis with Twitter Data
  3. Reading: Script:
  4. Reading: NLTK
  5. Discussion Prompt: Sentiment Analysis with Twitter Data
  6. Video: Video-3: Text Mining of Twitter Data
  7. Reading: Script: text_mining_twitter.r
  8. Reading: An Introduction to Network Analysis with R and statnet
  9. Video: Video-4: Conclusion
Graded: Sentiment Analysis with Twitter
Graded: Text Mining with Twitter
How It Works
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