The challenge for businesses now is how to read the Big Data tea leaves to actually understand what it’s trying to say.
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Poornima Apte

March 11, 2021

Being a data analyst gets you a front-row ticket to the next industrial revolution.

Your Facebook feed. Your Instagram feed. The ads you receive in your local mail circular. The seemingly endless ways Netflix uncannily predicts which series you might want to binge-watch next. Our world—the services we use—runs on data. So much so that Satya Nadella, CEO of Microsoft, labeled data the new electricity.

By 2020, nearly 1.7 megabytes of new information will be outputted every second per human being on the planet. That’s the equivalent of a one-minute YouTube video being uploaded every minute for every human on the planet! With such a flood, data’s transformative power is boundless.

The numbers are staggering, and it’s not just consumer behavior data that’s at play. There are baby socks that deliver data to parents about their little one’s body temperature or sleep patterns. On the manufacturing floor, machines embedded with sensors connect to the Internet and spit out volumes of data, such as temperature, pressure, etc., about their health. This in turn can be used as the basis for predictive maintenance; plant supervisors can be alerted to act on problem areas before they disrupt the manufacturing process.

The challenge for businesses now is how to read the Big Data tea leaves to actually understand what it’s trying to say. This is where data analysts come in. At its core, data analytics draws insights from raw information sources.

Why consider a master’s degree in data analytics

The problem is that data mining is like panning for gold. There’s a whole lot of work for a few flecks of treasure. The garbage-in-garbage-out theory holds true for data analysis; sifting through the data lake requires the ability to differentiate good data from bad—and bad data costs the United States $3 trillion annually, so businesses need to ensure the quality of their data samples.

Data analytics isn’t about staring at rows and rows of Excel sheets. Data visualization engineers and programmers present data in interactive, visually compelling mediums so that data scientists can detect pattern anomalies more easily and accurately. In turn, data scientists suggest ways in which companies can gather data for particular—desired—outcomes. The sheer ubiquity and crush of data, and the need to analyze the numbers meaningfully, make data analytics an increasingly powerhouse field to be in. With a master’s in data analytics, the career paths are robust.

What skills will a master’s in data analytics teach you?

The core curriculum in data analytics include data visualization and modeling; statistics and applied statistics; computer programming for analytics, machine learning algorithms and data mining. These courses set the foundation for you to know where to look for data, how best to gather the data, and how to use computer programs to identify trends and patterns.

Machine learning is a subset of artificial intelligence whereby computer programs learn from past history to become progressively more intelligent over time. For example, after data analysis if a machine consistently fails when it reaches its rotor reaches a temperature of 1600C, it can be programmed to shut down when the temperature reaches 1500. A master’s in data analytics will give you the knowhow to create complex machine learning algorithms powered by robust data—and then how to operate a whole host of machinery or devices intelligently.

Like other graduate-level programs, a master’s in data analytics might typically have a capstone project or thesis which either provides real-life experience or draws from industry examples to make your thesis argument.

After you complete your master’s in data analytics, you will be fully equipped for a variety of jobs helping companies define what’s essential to their business strategy: why is one product more successful than the other; who is our best customer; which machines fail regularly and why. Genetics data helps pharmaceutical companies tailor better drugs, and patient data helps detect patterns in disease cure and prevention.

Health analytics, finance analytics, and analytical tools and modeling are a few of the specific tracks you can pursue with a master’s degree in data analytics.

A lucrative career

Data scientists sharpen a company’s competitive advantage, and as such are compensated well.

Payscale reports that the median salary for big data analytics positions following a master’s degree ranges just above $100,000. With more companies eager to hire data scientists, the job market it strong. IBM predicts the demand for data analytics will increase by 28 percent in just a three-year span.

Being a data analyst gets you a front-row ticket to the next industrial revolution. And that promises to be a thrill ride all its own.

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