In simple terms, a data scientist’s job is to research data for actionable insights.

Specific tasks include:

Identifying the data-analytics problems that provide the best opportunities to the organization

Determining the right data sets and variables

Collecting large sets of structured and unstructured data from disparate sources

Cleaning and validating the info to make sure accuracy, completeness, and uniformity

Devising and applying models and algorithms to mine the stores of massive data

Analyzing the info to spot patterns and trends

Interpreting the info to get solutions and opportunities

Communicating findings to stakeholders using visualization and other means

In the book, Doing Data Science, the authors describe the info scientist’s duties this way:

“More generally, a knowledge scientist is someone who knows the way to extract meaning from and interpret data, which needs both tools and methods from statistics and machine learning, also as being human. She spends tons of your time within the process of collecting, cleaning, and munging data, because data isn’t clean. This process requires persistence, statistics, and software engineering skills—skills that also are necessary for understanding biases within the data, and for debugging logging output from code.

Once she gets the info into shape, an important part is exploratory data analysis, which mixes visualization and data sense. She’ll find patterns, build models, and algorithms—some with the intention of understanding product usage and therefore the overall health of the merchandise , et al. to function prototypes that ultimately get baked back to the merchandise . She may design experiments, and she or he may be a critical a part of data-driven deciding . She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations in order that albeit her colleagues aren’t immersed within the data themselves, they’re going to understand the implications.”

Source: O’Neil, C., and Schutt, R. Doing Data Science. First edition.

Would you create an honest Data Scientist?

To find out, ask yourself: does one . . .

Hold a degree in mathematics, statistics, computing , management information systems, or marketing?

Have substantial work experience in any of those areas?

Have an interest in data collection and analysis?

Enjoy individualized work and problem solving?

Communicate well both verbally and visually?

Want to broaden your skills and combat new challenges?

If you answered yes to any of those questions, you’ll find tons to love within the field of knowledge science.

Data scientists require a knowledge of math or statistics. A natural curiosity is additionally important, as is creative and important thinking. What are you able to do with all the data? What undiscovered opportunities lie hidden within? You want to have a knack for connecting the dots and a desire to look out the answers to questions that haven’t yet been asked if you’re to understand the data’s full potential.

Data scientists also are highly educated. consistent with industry resource KDnuggets, 88 percent of knowledge scientists have a minimum of a master’s degree and 46 percent have PhDs.

You also need some background in programming so you’ll devise the models and algorithms necessary to mine the stores of massive data. Python and R are two of the premier programming environments for data science.

You must be something of an entrepreneur. A head for business strategy is vital . Although you’ll work with other data specialists or maybe with an interdisciplinary team of execs , you’ll not achieve success if you can’t devise your own methods and build your own infrastructures to slice and dice the info which will lead you to your new discoveries and new visions for the longer term .

You must even be ready to communicate complex ideas to your nontechnical stakeholders during a way they will easily understand. Data-science software tools can assist you visualize your findings, but you’ll also need the verbal communication skills to inform the story clearly.