Lots of people are good with numbers. Accountants crunch financial figures, and statisticians pound numbers into shape so information can be retrieved. Data scientists look beyond the numbers to see trends and insights. If you’ve seen the film ‘The Matrix’ you might recall one of the characters claiming he could see beyond the columns of green characters scrolling down the screen. That is – in effect – exactly what a data scientist sees.
So, what do you need to become, or be regarded as (because you might already be) a data scientist?
You must love numbers
Not everyone does. Some people can crunch even gargantuan numbers together in their heads. If some other people try to do this, the numbers tend to fall out of their ears. If you’d rather spend an evening battling a sudoku than binge watching a box set, then data science may work for you.
You must have the ability to think logically
Numbers don’t lie. One plus one always equals two. There’s no pressure groups that will argue until they are blue in the face that one and one is three. You must be able to see logical patterns and treat data is the way the rules dictate.
You must be able to code
Computer and numbers are invariably linked. No matter how sophisticated computers get, they are – when striped bare – number crunchers. They will save you hours and hours of work, but you need to be able to tell them what you want them to do. You don’t have to be an expert coder – try an open source language like Python. Open source languages possess huge libraries that you can download and load up to help you along.
You need to be patient
Huge amounts of data means huge amounts of time, and a lot of data is simply superfluous and serves no purpose. You need to get the data to work for you, and that takes patience. As a data scientist, you will spend the majority of your time cleaning up your data. The rest of your time is actually spent analysing it.
You need to understand statistics
This is the real ‘meat’ of being a data scientist, and there’s no getting away from the fact the statistical models, concepts and formulas can be fiendishly complicated. There are plenty of methods that you need to be aware of, and if there are gaps in your statistical expertise, then you need to fill them.
You need to interpret results
Statisticians prepare data, bash it around a bit then give it someone else to look at. Data scientists need to work out the benefits and insights that can be gained from what the figures tell them. Without this ability, you’d be as useful as an art critic who can’t tell a masterpiece from a child’s potato print painting.
Data science can be a very rewarding career, and companies are always on the lookout for people who can give them insights on the data that’s available, and the reasoning behind them. If you are genuinely ‘good at numbers’, then data science is a career you need to seriously consider.