Learn how data science can help online dating services by analyzing how they are leveraging big data analytics. Jungwoo explains that the online dating companies are tracking the behaviors of their candidates on the Internet. This approach may run the risk of fabricated online behavior.
- [Voiceover] These days, it is commonplace for people to find their love online. There are a number of online dating services available today. To name a few, eHarmony, Match.com, and OkCupid are those. There are various methods used by these companies to find the right matches for clients seeking their love. For instance, some online dating services use a match percentage to decide on how similar two individuals are.
The match percentage is typically calculated by the similarities found between answers to a questionnaire. The answers to the questions are weighted depending on their importance. For example, a question on the level of education is more highly weighted than those on music preference. Other online dating services use a more sophisticated method, such as a compatibility predicted model. The leaders of online dating providers are now starting to adopt big data analytics to enhance the quality of their services.
Match.com has collected more that 70 terabytes of data on their customers. According to Match.com, big data analytics allowed them to create 500,000 relationships, which, in turn, resulted in 92,000 marriages and 1 million babies. The primary method used by the big data analytics algorithms when making matches is to keep track of candidates' online behavior.
One caveat in this case is the possibility of people fabricating their online behavior.
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