You want to combine your experience in your current professions (called field X) for this article, with your rapidly increasing knowledge of data science. However, you're not sure if data science is more than a mixture of statistics and programming. From some of your readings, you're worried that data science is just a more proactive form of a data analyst and you're worried that a data scientists's work is pretty independent of the field it is applied towards.

**Domain knowledge is of utmost important**

Domain knowledge is very important to a data scientist as it will help them better understand the data they are looking at, how the system that generates the data works, how the recommendations and data products produced could actually help the organization the data scientist is working in.

In Drew Conway's "The Data Science Venn Diagram", the three main areas of "hacking skills", "math & stats knowledge", and "substantive expertise". It is in the third circle, "substantive expertise", that we can see how important a data scientists knowledge of their field can be for doing data science work. To wit, Drew Conway writes "To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods."

**"Business-centric" Definition of Data Science**

John Foreman, Chief Data Scientist at MailChimp, in his "Data Smart" book, shares a a "business-centric" definition of data science. He writes, "Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products". So if you look at a specific field, like X, then you can just add in the key word into the definition... Which gives us "Data science is the transformation of X systems data using mathematics and statistics into valuable insights, decisions, and products".

This leads to a positive answer to the question -> will I be able to combine my knowledge of field X with my rapidly increasing knowledge of data science? YES!

**Why this matters for a data science jobs**

In a data science job, you will be asked to do many many projects. And while yes, your knowledge of "hacking skills" and "math & stats knowledge" will be required and used, it is the third circle "substantive expertise" about your field that will set you apart and allow you to be more effective in your role.

So are you are preparing to look for a data science job and are in the process of looking at a data science job, make sure you think about whether you are interested enough in the potential companies and industries you are looking at that you will enjoy developing the "substantive expertise" required to succeed as a data scientist in that organization and industry.

**The next step**

To that end, start thinking about the types of books, news articles, meetups, organizations, and friends you interact with as this will give you a sense of the things you enjoy and have a leg up on developing the expertise. After all, it will be much easier to develop this area if it's something that you're already doing and enjoying. So go out there and think about what you already know and what you would like to learn about so that you can truly build your "substantive expertise".

Good luck!

Receive the Data Science Weekly Newsletter every Thursday

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

Easy to unsubscribe at any time. Your e-mail address is safe.