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How to Become a Data Scientist

What is Data Science?

Ready to start your career in Data? Find out more about BrainStation's Data Science Bootcamp

“Data Science” is an interdisciplinary field focused on extracting meaningful information from large sets of data. To discover hidden patterns, Data Scientists use math, science, algorithms, and systems to identify opportunities for increased efficiency, productivity, and profitability.

At its most essential, data science is about knowledge creation: it makes use of the most state-of-the-art techniques and tools the fields of computer science and statistics have to offer to turn a mess of data into knowledge that an organization can use to inform their business practices.

What Is a Data Scientist?

A Data Scientist is a data expert with the analytical and technical skills to solve complex problems. A data scientist's role involves using computer science, mathematics, and statistics to find patterns in data and develop actionable strategies for organizations.

Data Scientists spend a lot of time collecting, organizing, modeling, and examining data from various angles, including some that have not been looked at before. If it sounds like data science offers no singular road map from problem to solution, that’s because it doesn’t. As Biostatistics Professor Jeff Leek explains, “The keyword in ‘data science’ isn’t ‘data’; it’s ‘science’”—which is to say, by definition, data science is an exploratory field.

What Do Data Scientists Do?

The common perception that Data Scientists crunch numbers is not too far off the mark; they do work with large sets of data, deciding what data is needed, cleaning the data, building models of what the data can show, and organizing it to reveal latent information—and this effort is always directed toward some kind of goal.

Notably, those data sets aren’t always numbers. While most Data Scientists do work with numerical data (73 percent, according to the BrainStation Digital Skills Survey), there are other types of data as well. According to the same survey, 61 percent of respondents work with text, 44 percent with structured data, 13 percent with images, and 12 percent with graphics—even video and audio are ripe for analysis, with 6 and 4 percent (respectively) of respondents working with these media regularly.

These results hint at the way data science is expanding far beyond the world of financial tables, and exerting its influence in areas like maximizing customer satisfaction and extracting valuable insights from social media.

As a result, every industry has its own types of data, and its own ways to leverage that data to help meet desired outcomes. In every case, though, data science serves as a way to help leadership make better, more informed decisions—whether that’s improving a product, understanding a new market, retaining customers, effectively deploying a labor force, or making better hires.

Data Scientists, therefore, use a combination of techniques and concepts, including:

Descriptive Analytics

Studies large sets of data to understand the way things are, including correlations and even causations that aren’t immediately obvious.

Predictive Causal Analytics

Draws inferences from data using a variety of statistical techniques—including data mining, predictive modeling, and machine learning—to predict the possibilities of a future event.

Prescriptive Analytics

Provides intelligence-based recommendations to produce a desired outcome or accelerate the results of a given application or business process.

Machine Learning

A specific application of artificial intelligence, which typically uses multiple iterations of algorithms that are tried and tested repeatedly, making automatic improvements each time.

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