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Data Science 101- A Conversation with Dr. Sai Moturu

Wednesday, June 6, 2012

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The phrase ‘data science’ has come into vogue recently but the idea that data mining would prove valuable has been around for a while. The Economist came out with an article a couple of years ago about the ocean of data that threatens to drown the everyday user. But, the current health tech revolution offers ways to harness the power of data to our advantage. A variety of skills are needed to study data science, but a mastery of one is essential. Data scientists usually have a core skill in statistical analysis, machine learning or visualization and are “good enough” in other supporting areas to get the job done.

The ability to recognize, analyze and utilize patterns in data is important, as well as being able to draw meaning out of data. Skills and experiences from a range from academic backgrounds can be relevant. Data scientists can come from a variety of academic fields, from physics and machine learning to statistics and sociology. A physicist, academic or otherwise, might have a background in modeling networks, which is directly applied to say location or health data. An academic computational social science researcher (CSS) would be more focused on traditional statistics, since their goal is to convince traditional social scientists, of new sociological ideas, using large amounts of behavioral data that was not available before.

There are some interesting discussions on the web about what the terms data science or data scientist mean at Zero Intelligence Agents, O’Reilly Radar, and the Wikibon Blog.

For you aspiring data scientists, our co-founder, Anmol, led an interesting thread on Quora that sheds more light on the topic.

Q: What’s the difference between data and big data?

A: Today, The term big data means something entirely different than it did 10 or 15 years ago. Automated data collection, the advent of web technologies and the introduction of smart phones with sensors into the market all have created millions and millions of records and data points. The difference is scale.

Q: How could data science most change public health?

A: In the traditional healthcare system, you went to the doctor and the doctor told you what to do. A couple of points of data are collected about the health of the patient and the doctor makes a decision. Now, however, we are in a stage where technology is part of our lives. It’s in everything we do. The population of patients with smartphones is only growing. Other devices that have sensors are becoming more pervasive as well. Data science will open the door for patients to monitor their own health and enable them to become more involved in their own care.

Q: What is most interesting to you about working with Ginger.io on improving public health?

A: Ginger.io understands that a good quality of life consists of a healthy mind and body. Insight into the mental and social well-being of a patient is hard to come by, but through data science Ginger.io is extracting the important information. The idea is to empower both patients and doctors by giving a 360 degree view of a patient’s health. A complete mental, social and physical picture of health will create a higher level of collaboration between doctor and patient, resulting in better health care overall.

Q: If there was one thing you would want the general public to understand about data science, what would it be?

A: Data is everywhere. The science behind its analysis connects people with relevant search queries, coupons even movie titles. It’s used everyday, running behind the scenes, powering the software that automates analysis and suggestions. In the future, when your doctor decides treatment based partly on the data from your smartphone and other sensors, there will be data science behind that.