Learn how data science can drive and enhance simulations by defining different types of simulations and evaluating the value of data-driven simulations especially in terms of their enhanced accuracy and predictive power. Jungwoo will explain how the field of climatology is benefiting from the marriage between simulations and big data.
- [Voiceover] Simulations imitates the operative of a real world system. The true power of simulation comes from its predictive nature. A computer simulation can completely rely on a mathematical model, can be interpreted into an algorithm, and then finally implemented into a piece of code. A physics engine used for gaming, is an excellent example of this. This purely model-driven simulation is not always accurate and can misrepresent what can happen in the real world.
Which is why data science can play an important role in simulations. By feeding real life data into a simulation model, scientists can improve the accuracy of a simulation drastically. In addition to the improved accuracy, the simulation model aided by this infusion of a large amount of real-life data can also significantly enhance its predictive power. For example, the field of Climatology is one of the beneficiaries of the recent progresses of big data analytics.
Now, meteorologists can predict future weather patterns with much more accuracy. In fact, it is becoming quite feasible to predict your weather with 95% accuracy 48 hours ahead of time.
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