Data Days Video Series: What is Data Science?


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I like sharing my knowledge about the power of data and analytics. This knowledge can can help business leaders and citizens understand how the world works, improving how they manage their business and make better decisions to improve their personal lives, respectively. Toward that end, I am creating a video series called, Data Days, to explore the world of data and analytics. In these short videos (around 3 minutes long), I will cover different data-related topics, including data science, the people who practice data science, predictive analytics, machine learning and many more.

My goal is to help both aspiring and practicing data professionals understand how they can best use their skills to improve how they use data and analytics to generate insights. Additionally, I would like to inspire the non-data professionals who want to participate in and contribute to data projects in their work and personal lives. I hope you’ll join me on this exploration of data and analytics and how they can help you make better decisions. In the first video below, I talk about the practice of data science.

What is Data Science?

In its simplest form:

Data science is way of extracting insights from data using the powers of computer science and statistics.

Extracting these insights requires three skill sets. First, you need knowledge and skills in the content domain that is related to the problem you’re trying to address. If you’re problem is in the domain of finding cures for cancer, you need skills and knowledge in oncology. If you’re addressing problems of customer churn, you likely need to possess business acumen. Second, you need skills in the areas of technology and programming; these types of skills allow you to get access to the data you need to answer the questions. Last, but not least, you need solid math and statistical skills to analyze the data in a way that will solve your problem.

Data science requires three broad skills (content domain, technology/programming, statistics/math). Data professionals tend to specialize in one of the three skill areas. Click image to enlarge.

Finding a data professional who is proficient in all data science skill areas is extremely difficult. The chance of finding a data professional with Expert level of proficiency in all data science skill areas is akin to finding a unicorn; they just don’t exist. In a study I conducted a couple of years ago, we found that data professionals who practice data science tend to specialize in one of the three data science skill areas.

As you might have noticed, I have not used the term, “data scientist.” As it is being used right now, the term “data scientist,” has become somewhat ambiguous. It is ambiguous with respect to the varying types of skills different “data scientists” possess.

Even though my twin brother and I possess different skills, we both engage in the practice data science. Click image to enlarge.

For example, my twin brother and I are both considered to be data scientists in our current job roles. While we both hold this sexy moniker, we have vastly different skill sets. He has a background in computer science and is highly proficient in technology and programming. I, however, have a background in industrial-organizational psychology and quantitative methods and my expertise falls in the areas of statistics and research methods. So, using the term, “data scientist,” to describe our roles really masks our skill differences.

So, rather than use the term data scientist, I prefer the term, “data science,” to focus more on the process of getting insights rather than the job titles of those who are getting the insights

In summary, I think of data science as a way of thinking about a problem using data. And when you’re approaching a problem with a data science eye, you need to bring three skills to bear on the problem: 1) Content domain knowledge, 2) technology and programming and 3) math and statistics.

Next time, I’ll discuss the process of data science.

Republished with author's permission from original post.


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