This specialization is designed for post-graduate students interested in mastering social computing techniques to solve real-world problems. Through four in-depth courses, learners will explore key topics such as social network analysis, chatbot development, crowdsourcing, and AI performance optimization. You will learn to analyze social networks using R programming, create functional chatbots with AWS, and enhance AI models by leveraging crowdsourced data and machine learning techniques. By the end of the specialization, you will have practical experience applying advanced tools and methodologies across domains like social media analytics, conversational interfaces, and human-AI collaboration. This hands-on, industry-relevant learning path equips you with the skills needed to excel in social computing, artificial intelligence, and data-driven innovation.
Applied Learning Project
In this specialization, learners will apply their skills in social computing, social network analysis, AI, and machine learning through hands-on projects. These projects include tasks such as collecting and analyzing social media data, constructing machine learning classifiers, and developing chatbots. For example, students may extract data from social media platforms, perform sentiment analysis, or build classifiers to predict specific outcomes, such as wine quality or social trends. Learners will engage in real-world problems by applying techniques like decision trees, logistic regression, and random forests. Through these projects, they will gain practical experience in AI model evaluation, human-computer interaction, and the development of socially aware AI applications. These projects reflect authentic challenges in combining human and machine intelligence for better decision-making.
