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Instructor
Dr. Stylianos Kampakis, Instructor - Social Media Analytics

Dr. Stylianos Kampakis

Stylianos (Stelios) Kampakis, PhD is an expert data scientist, member of the Royal Statistical Society, honorary research fellow at the UCL Centre for Blockchain Technologies and startup consultant living and working in London. He has more than 10 years of experience in machine learning and analytics, including 4 years of working in sports analytics with Tottenham Hotspur FC, and 3 years working on social media analytics. He has delivered university courses at University College London, the Cyprus International Institute of Management, and the Innopolis University. He also runs his own consultancy and executive education company called Tesseract Academy.

This course will cover various ways into which we can harvest social media data and analyze it, covering real use cases and going over many related topics, from NLP to social network analysis.

  • Explore various ways into which we can access social media data and analyze it.
  • Practice real use cases.
  • The instructor worked with Tottenham Hotspur FC of British Premiere League to build predictive models for football injuries and holds a Ph.D. in Machine Learning. 

Duration: 3h

Course Description

Social media platforms are pervasive in our every day lives. A huge volume of data is produced every day on services like Facebook, Instagram, and Twitter. This data contains a wealth of information which can be used in various industries from digital marketing to counter terrorism. This course will cover various ways into which we can harvest social media data and analyze it, covering real use cases and going over many related topics, from NLP to social network analysis.

What am I going to get from this course?

  • Intro to social media analytics 
  • Practice real use cases
  • NLP
  • Social network analysis
  • Recommender systems
  • Various ways into which we can access social media data and analyze it.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Knowledge of Python and some machine learning is preferred.

Who should take this course? Who should not?

  • Anyone interested in learning how to extract useful data from social media platforms.

Curriculum

Module 1: Introduction to Social Media Analytics

Lecture 1 Curriculum
Lecture 2 What is Social Media Analytics
Lecture 3 History of Social Media Analytics
Lecture 4 Types of Data
Lecture 5 Tools and Products
Lecture 6 Tools and Products 02
Lecture 7 Tools and Products 03
Lecture 8 Tools and Products 04

Module 2: Use Cases

Lecture 9 Analyzing Volume Data
Lecture 10 Sentiment Analysis
Lecture 11 Correlation with Sum and Volume
Lecture 12 Time Series Analysis
Lecture 13 Other Engagement Considerations
Lecture 14 Lab - Sentiment Analysis
Lecture 15 Lab Time Series
Lecture 16 Lab Analysis
Lecture 17 Lab Analysis 02
Lecture 18 Lab - Extracting Data from Youtube 01
Lecture 19 Lab - Extracting Data from Youtube 02
Lecture 20 Lab - Extracting Data from Youtube 03

Module 3: Intro to Natural Language Processing 1:

Lecture 21 What is NLP
Lecture 22 Topic Identification
Lecture 23 Basic NLP Tasks
Lecture 24 More Tokenization Issues
Lecture 25 Tokenizers
Lecture 26 Constituency Parsing
Lecture 27 Pre 1990 NLP Parsing
Lecture 28 Naive Bayes Example
Lecture 29 Selecting Features
Lecture 30 Non Independent Features
Lecture 31 Lab - Intro to Spacy and NLTK
Lecture 32 Lab 01
Lecture 33 Lab 02

Module 4: Natural Language Processing (session 2)

Lecture 34 Sentiment Analysis Overview
Lecture 35 Dictionary Approach
Lecture 36 Semantic Orientation
Lecture 37 Evaluating Sentence Polarity
Lecture 38 Kamps and Marcs
Lecture 39 Why Dirichlet
Lecture 40 Parameter Estimation
Lecture 41 Demo
Lecture 42 Exercises
Lecture 43 Lab Twitter Authorization
Lecture 44 Lab 01
Lecture 45 Lab 02
Lecture 46 Lab 03

Module 5: Social Network Analysis

Lecture 47 What is Social Network Analysis
Lecture 48 Metrics
Lecture 49 Homophily

Lecture 50 Explanation
Lecture 51 Transitivity
Lecture 52 Reciprocity
Lecture 53 Betweenness Centrality
Lecture 54 Betweenness Centrality 02
Lecture 55 Closeness Centrality
Lecture 56 Structural Cohesion
Lecture 57 Distance Metrics
Lecture 58 Lab Graph API
Lecture 59 Lab 01

Module 6: Recommender Systems

Lecture 60 Problem Statement
Lecture 61 Recommendations and Social Media
Lecture 62 CB - Filtering Algorithm Outline
Lecture 63 Another Example: Posts
Lecture 64 Item-Based CF
Lecture 65 Common Issues with CF
Lecture 66 SimRank
Lecture 67 Facebook's EdgeRank Algorithm
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