Learn how social media analytics can help improve disease control by analyzing a University of Pennsylvania study on a predictive relationship between Twitter post content and heart disease.
- [Voiceover] One of the areas of social media analytics applications is disease control. University of Pennsylvania conducted a study on a predictive relationship between Twitter post content and heart disease. Emotional factors are linked to heart disease. The University of Pennsylvania study identified indicators of emotional distress expressed in words and correlated them to the occurrences of heart disease.
Their study used linguistic analysis techniques as well as various big data analytics techniques to reveal key words of emotion such as hate to be strongly correlated to the incidence of heart disease. On the other hand, positive words like wonderful showed the opposite correlation. The Twitter data they collected consisted of tweets posted by 88 percent of the people from countries in 2009 and 2010.
This ample data set provided much stronger evidence of correlation than what they could provide through conventional surveys of subjects.
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and analytics, explores which markets are using big data the most, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the four leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why analytics is important
- How data science is used in social media, climate research, and more
- Data science skills
- Data science certifications
- The future of big data