It stands to reason that if you’ve been researching technology careers you’ve come across this ‘data science’ thing all the kids are talking about. The hype is at least somewhat justified, as it turns out. Data scientists and data analysts are in more demand and therefore commanding larger salaries than ever before.
And since there’s more data than at any point in history, there’s pretty much an unlimited number of fascinating problems to work on.
If you’re looking to transition into a job in data science, you’ll probably want to achieve clarity on some basic questions. No doubt you’ve wondered about how data science is related to big data, or whether there’s any difference between data science and data analytics.
What Is Data Analysis?
Defining data analysis is relatively straightforward. In basically every field, analysts are responsible for using the appropriate means of making sense of statistical information, answer basic questions, pose interesting new ones, check the quality of data, and communicate the results of their work.
The tools used by a data analyst can be both qualitative or quantitative. I know more than one consultant/analyst who uses no statistics, no metrics, no data to help their clients solve problems; instead, they utilize lengthy conversations with relevant personnelle, questionnaires, and thought experiments to get a sense of where things are going wrong and what solutions might exist.
Naturally a ‘data’ analyst is basically guaranteed to be using a fair amount of statistics and math in their work, but the analysts on my team make some of their biggest contributions through their qualitative understanding of the major players in the cryptoasset space.
What Is Data Science?
Data science isn’t quite statistics, or machine learning, or artificial intelligence, or computer science, or programming. It’s something born of all of these fields, and has therefore proven of value in solving a staggeringly diverse set of problems.
It should come as no surprise, then, that data science can be a little hard to pin down. Ask 50 data scientists what a data scientist is, and you’ll get as many different answers.
Personally, I’d say that a data scientist is a person that takes responsibility for acquiring, cleaning, processing, and understanding data. They might use statistics, cloud computing, or any of the thousands of algorithms for machine learning to accomplish these goals.
How Is Data Analysis Different from Data Science?
The truth is that there is no hard and fast division between a data scientist and an analyst. The same person could often correctly be described as either.
If I were pressed, I’d say that a data scientist probably covers more theoretical and practical ground and draws from a wider range of disciplines. You’d probably expect a data scientist to be better at coding than an analyst, and I’d be surprised to find an analyst building a neural network or doing natural language processing.
Of course, none of the above is written into stone. One of the most exciting things about both data science and data analytics is how flexible the roles are. As either, you’ll find yourself routinely picking up new skills, exploring new ideas, and conquering new heights.
Maybe that’s why people are flocking to data science bootcamps in such large numbers!
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