Johns Hopkins University
Social Computing Specialization
Johns Hopkins University

Social Computing Specialization

Master Applied Skills in Social Computing. Learn advanced techniques to analyze social networks, build chatbots, and enhance AI with crowdsourcing

Ian McCulloh

Instructor: Ian McCulloh

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 months
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 months
at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Analyze social media dynamics using advanced techniques in social computing.

  • Explore graph theory and centrality measures in social network analysis.

  • Utilize crowdsourcing to enhance AI through reliable data annotation.

  • Build and optimize chatbots using AWS for diverse applications.

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Taught in English
Recently updated!

September 2024

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Specialization - 4 course series

What you'll learn

  • Understand the basics of social computing and its relationship with social media and analytics.

  • Analyze how social media influences communication, behavior, and societal interactions.

  • Recognize how cognitive biases affect online behavior and the spread of information.

  • Explore how gamification enhances user motivation and improves social computing applications.

Skills you'll gain

Category: Gamification Techniques
Category: Social Media Analytics
Category: Data Collection and Ethics
Category: Cognitive Bias Recognition
Category: Network Conformity Analysis

Social Network Analysis

Course 213 hours

What you'll learn

  • Learn to calculate and interpret key centrality measures to identify influential nodes in social networks.

  • Gain skills in applying statistical models to analyze relationships and dynamics within social networks.

  • Understand how foundational social theories inform network analysis and shape interpretations of social interactions.

Skills you'll gain

Category: Social Theory Application
Category: Network Construction
Category: Data Analysis in R
Category: Statistical Modeling
Category: Centrality Analysis

Training AI with Humans

Course 322 hours

What you'll learn

  • Learn to construct and evaluate various machine learning classifiers and performance metrics.

  • Master the calculation and implications of Inter-Annotator Agreement (IAA) for data consistency.

  • Understand how to design and implement effective crowdsourcing tasks using Amazon Mechanical Turk.

  • Analyze crowdsourced data to enhance machine learning models and understand ethical considerations in AI.

Skills you'll gain

Category: Ethical Considerations in AI and Crowdsourcing
Category: Inter-Annotator Agreement (IAA) Analysis
Category: Data Collection and Analysis
Category: Machine Learning Fundamentals
Category: Crowdsourcing Techniques

Chatbots

Course 413 hours

What you'll learn

  • Explore the history and mechanics of chatbots, enhancing your understanding of their design and function.

  • Construct and evaluate machine learning classifiers using BERT for effective text classification tasks.

  • Gain hands-on experience in creating and configuring functional chatbots using AWS Chatbot services.

Skills you'll gain

Category: AWS Chatbot Implementation
Category: Chatbot Design Principles
Category: Collaborative Problem-Solving
Category: Machine Learning Classifiers
Category: Performance Metrics Calculation

Instructor

Ian McCulloh
Johns Hopkins University
6 Courses12 learners

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