What Makes a Data Scientist Different from a Data Analyst?


What Makes a Data Scientist Different from a Data Analyst?

Every time you tweet, click a link, post a photo or even use Snapchat, you generate data.

That's 3 billion global internet users creating unimaginably large amounts of data every minute of every day. The quantity of this data will increase exponentially as the number of internet users continues to rise.

As this data multiplies, so does the need to collect it, analyze it and decipher it to give companies useful information on their customers.

The need for actionable data has revolutionized fields like Data Science and Data analytics. The trouble is that many companies have entirely different definitions of a data scientists and what they do. And since these are relatively new terms, we are all just figuring out how to make it all work.

What Is Data Science, Anyways?

Data science is an umbrella term that encompasses all the tools that are used to extract knowledge and insight from data sets. These tools can include mathematics, statistics and a variety of other scientific methods. They can tackle big data and make connections that might not be easily visible.

Data scientists are the treasure hunters of the modern world — one reason why the Harvard Business Review recently designated the position as Sexiest Job of the 21st Century.

This is one job that won't soon grow out of style. In fact, businesses are just waking up to the potential of Big Data and what a data scientist can do to unlock its potential. Organizations across the globe are increasingly in need of people who can efficiently manage data and help make more informed business decisions. From fighting cancer to beating insider threats data scientists are busy doing it all.

What Is Data Analytics?

Consider data analytics to be a more concentrated form of data analytics. It simply involves analyzing data with a better focus, i.e., with a specific goal in mind. 

A data analytics certification can teach you how to comb through the data sets to find exactly what you were looking for. You will be able to frame a business question as a hypothesis and then test it using statistical methods.

Data analytics is a multi-million dollar industry on its own. From sports teams to MNCs, every organization needs data analysis in one form or another.

How Do Data Scientists Differ from Data Analysts?

Typically, a data scientist formulates the questions. By contrast, the data analyst dives headfirst into the information to find the solutions/answers.

As expected, there is an overlap in the role and responsibilities of the two. Both the data analyst and the data scientist can be expected to write queries, source the relevant data and getting the data ready for interpretation and analysis.

However, the data analyst isn’t usually expected to build statistical models or program advanced models. Instead, they are often found working with simple SQL databases or other BI tools.

The role of a data scientist, on the other hand, calls for powerful data visualization skills as well as a knack for extracting an interesting business story out of plain old data.

Why Is It Important to Make This Distinction?

Data science and analysis are a major part of the advances in machine learning and artificial intelligence. The ability to connect the dots hidden within data, form algorithms and create functions that help us learn from data is in huge demand across the world. These skills are fetching premium price in the global job arena.

Google set their sights on machine learning and big data a couple of years ago. Take Google DeepMind, for example. Its ability to learn behaviors and even beat humans at board games is a prime example of data science done right. Recently, they have also acquired Kaggle, an online community for all this data science.

There is no denying that fact the future is hidden within data science. Now, are you the scientist or analyst who will help unravel the mysteries of this future?

At the end of the day, both data science and data analytics professionals sort through large collections of data looking for clues. Some, like data analysts, already know the end result and are just looking for proof to create an iron-clad hypothesis. Others, like the data scientists, enjoy discovering deeper trends within complicated sets.

Data science has always been around, but it has taken on a whole new meaning in the age of the internet. And thanks to these recent developments, defining simple terms like data scientist and data analyst has become a monumental task.

If you are interested in learning more about data, a data science certification will serve you well. After all, there is so much more to data than simply science and analysis. Data is important and it has taken a prominent place in our lives. It is present in everything from sports to medicine. All decision-making tasks in industries and organizations across the globe, are data-driven and will remain so for the foreseeable future.

That’s why building a strong base in Big Data can prove very beneficial for your career as an IT professional. With the right qualification, you can start on a new career path as a data researcher, data developer, analytics engineer or a data architect. You have unlimited options when it comes to carving out a niche for yourself working with Big Data. 

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