ADV 480: Social Media Analytics

The art and science of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision making.

Please find syllabus here.

Module 0: Introduction & Preparation

• Week 1:
  • Wed 1.18 (in-person lecture): Slides - Orientation & Logistics
  • Finish the pre-class survery
• Week 2:
  • Mon 1.23 (in-person lecture):
    • Lecture slides: Overview of introduction to SMA
    • Intro to R programming:
      • Slides
      • Html
      • R Markdown (.Rmd)
    • Intro to Python programming
      • Colab
  • Wed 1.25 (lab 0): Hands-on intro to R & Python programming
    • This week’s lab activity consists of 20 questions focusing on list(Python)/vector(R) manipulation and operations on data frames. Students can choose one language to finish the assignment.
    • If using R, download the Lab0.Rmd file. The HTML preview is here.
    • If using Python, make a copy of this Colab notebook and work in your own notebook.
    • Python solution to Lab Activity 0’s questions
    • R solution to Lab Activity 0’s questions
    • R solution in .Rmd format

Module 1: Data Collection

• Week 3:
  • Mon 1.30 (in-person lecture):
    • Lecture slides: Data collection using API
    • In class Colab tutorial
  • Wed 2.1 (lab 1): Using NYT API to collect data
    • Colab
    • Solution
• Week 4:
  • Mon 2.6 (in-person lecture): Data collecting through web scraping
    • Lecture slides: Web scraping
    • In class Colab tutorial
  • Web 2.8 (lab 2)
    • Colab
• Week 5:
  • Mon 2.13 (in-person lecture): Advanced data collection
    • Lecture slides: advanced data collection
  • Web 2.15 (lab 3)
• Week 6:
  • Mon 2.20 (in-person lecture): Data wrangling
    • Lecture slides: Data wrangling
    • In-class Python coding
  • Web 2.22 (no class: Assignment consultation session in lab)

Module 2: Data Analysis

• Week 7:
  • Mon 2.27 (in-person lecture): Text mining (1)
  • Web 3.1 (lab 4): Colab
• Week 8:
  • Mon 3.6 (in-person lecture): Text mining (2)
  • Web 3.8 (lab 5): Colab
• Week 9: Spring break
• Week 10:
  • Mon 3.20 (in-person lecture): Network analysis (1)
  • Web 3.22 (lab 6): Colab
• Week 11:
  • Mon 3.27 (in-person lecture): Network analysis (2)
  • Web 3.29 (lab 7): Colab

Module 3: Data Storytelling

• Week 12: No class - Proposal feedback on Zoom
• Week 13:
  • Mon 4.10 (in-person lecture): Data visualization
  • Web 4.12 (lab 8)
• Week 14:
  • Mon 4.17 (in-person lecture): Brand & Consumer Insight
  • Web 4.19 (in-person lecture): Connecting the dots
• Week 15: No class - final project working days
• Week 16: Final project presentation