Data analysis is performed in many different fields and on many different types of data. Most fields call it something different. The following list comes straight from Jeff Leek’s Data Analysis Coursera class.
Name of Data Analysis by Data Type
The type of analysis is very similar for all fields, but what separates data science and machine learning from the others is the 3 V’s of big data. Data science and machine learning deal with a greater Volume of data, Variety of data, and Velocity (speed at which new data appears) of data. Because it is becoming cheaper and easier to store massive amounts of data than ever before, I think the other fields are beginning to realize the potential in big data. Signal processing is definitely becoming an area with big data, due to the fact that electrical sensors are everywhere.
What are your thoughts? Do you see any real differences in the data analysis performed for the data types above?
The Syracuse University iSchool will be hosting a free, open online Introduction to Data Science course. The course will be focused around Professor Jeff Stanton’s data science ebook. If you are interested, please hurry because the enrolment is limited to the first 500.
Although Tobias Mayer may be known as the first data scientist, he did not coin the term data science. According to Wikipedia, the first use of the term data science was in 2001.
Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics was published in the April 2001 edition of the International Statistics Review. The author was William S. Cleveland, currently a Professor of Statistics at Purdue University.
The paper proposes a new field of study named data science. It then goes on to list and explain 6 technical focus areas for a university data science department.
- Multidisciplinary Investigations
- Models and Methods for Data
- Computing with Data
- Tool Evaluation
For the most part, the paper is still relevant. I did find a couple of good quotes from the paper that deserve comment.
The primary agents for change should be university departments themselves.
That did not happen. The driving agents for change in the data science field have been some of the newer technology/web companies such as LinkedIn, Twitter, and Facebook (none of which even existed in 2001).
…knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited. A merger of the knowledge bases would produce a powerful force for innovation.
I think this statement still applies today. The world is just starting to realize the benefits of merging knowledge from computer science and statistics. There is much more work to do. Fortunately, businesses and universities are working to address the merger.
Have you seen the paper before? What are your thoughts on it?
Wikipedia has a page for Data Science.
New Street Communications is looking for authors. According to the call for proposals:
…especially interested to hear from professionals in the fields of IT, Data Science, Big Data and Cloud Computing.
If you have ever thought about writing a data science book, now might be a good time.