What Makes a Data Scientist Different from a Data Analyst?




If you are an IT professional searching for the next big break in the industry, you may have already heard of data science certifications and the courses ad trainings associated with them. We are here to help you figure out if it’d be useful for you, or if data analysis is more your thing!

Do you know that every time you tweet, click a link, post a photo or even use Snapchat, you are generating data?

This means that the 3 billion global internet users are creating unimaginably large amounts of data every minute of every day, and this quantity is only going to increase exponentially as the number of internet users continues to rise.

Now as this data multiplies, so does our need to collect it, analyze it and decipher it.

This need has revolutionized the fields of Data Science and Data analytics. But what does it all mean?

Does it all fall in the realm of statistics? Is it programming? Is it really just all a science?

Well, could it be witchcraft and wizardry?

There’s a boom in the data industry. But the trouble is that many companies have entirely different definitions of a data scientist 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?

It’s an umbrella term that encompasses all the tools that are used to extract knowledge and insights from data sets. Now these tools can include mathematics, statistics and a variety of other scientific methods. These tools can tackle big data, make connections that might not be easily visible and sharpen the data sets down to meaningful and useful information.

Data scientists are the treasure hunters of the modern world. And that is why not too long ago, the Harvard Business Review acknowledge data scientists as the Sexiest Job of the 21st Century!

This is one job that isn’t getting redundant anytime soon. In fact, businesses are just waking up to the potential of Big Data and what a data scientist can do to unlock this potential. Organizations across the globe are increasingly in need of people who can efficiently manage data, and help make business decisions better with it’s help. From fighting cancer, to beating insider threats, data scientists are busy doing it all… and a lot more.

And 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. a specific goal in mind. 

You can get a data analytics certification online, and with it you’ll learn how to comb through the data sets to find exactly what you were looking for all along. 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.

So How Do Data Scientists Differ from Data Analysts, Again?

Okay. So normally a data scientist formulates the questions. And the data analyst dives headfirst into the data to find the solutions/ answers.

As expected, there is an overlap in the role and responsibilities of the two. Both the analyst and the 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 erect statistical models or indulge in advanced programming. 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. But they will not be asked to transform this into a roadmap for the business.

Why Is It Important to Make This Distinction?

Well, for starters wouldn’t you want to know these details if you are planning on changing your career path or making that crucial hire that could make all the difference to your business?

Fact of the matter is that 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. It’s 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 these terms denote detectives who sort through large collections of data looking for clues. Some, like the data analysts, already know the end result and are just looking for proof to create an iron-clad hypothesis, others, like the data scientists, go from data set to data set, discovering a whole new world hidden in the clues.

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

If you are interested in learning, perhaps a data science certification will serve you well. After all, there is so much more to it than just these two terms. 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. 

About The Author
James
Data Scientist (Growth) at QuickStart

James Maningo

James is a stochastic tinkerer with over 8 years of experience in digital analytics. His passion lies in providing meaningful impact through data, utilizing growth hacking techniques for business and "quantified self" for personal life. His weapons of choice are linux, python, tmux+vim and good old common sense.