Data science is a relatively new term for a relatively old discipline. Essentially, it is data analysis, particularly for large data sets. It involves techniques as wide-ranging as statistics, computer science, and information theory. What to know more? Stanford has a “Data Science Handbook” that you can read online.
Topics range from how to design a study and create an analytic plan to how to do data visualization, summarization, and analysis. The document covers quite a bit but is very concise.
Data science tends to use Python, although we aren’t sure why that is. However, you might look into the Python Data Science Handbook and Think Stats to apply what you’ve learned about data science to Python. Be sure, too, to check out Stanford Online’s playlist for Statistics and Data Science for many interesting seminars, including “How to be a Statistical Detective.”
Generating a lot of data is something sensors are good at, so it makes sense that data science and statistics techniques might apply. Data science is supposed to be new and shiny, but in reality, it has been going on for a very long time. Ask World War II statistician Abraham Wald.
Title graphic: by [Schutz] CC-SA-3.0.
