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Data science is a rapidly growing area; abound with career opportunities in an array of fields such as Information Technology, Healthcare, Finance and many more. Though data science is growing in popularity, what makes someone a data scientist still remains a foggy area for many.
People often assume that a data scientist is the one who gathers data regarding a chosen stream and interprets it. Though the essence of that idea is not completely wrong, it simply does not do justice to the many roles and characteristics that make a data scientist.
Here’s a list of some of the skills and qualities that a good data scientist is expected to possess:
Love for math
For a data scientist, numbers are the musical notes that create his/her best symphonies. Great data scientists aren’t afraid of spreadsheets full of complicated numericals, and are comfortable diving into the rows of conflicting data points.
For example, 350 million photos are posted, and 4.75 billion items are shared1 every day on Facebook. If having to deal with even a fraction of this data excites you, then this might be the path you should be taking.
A passion for the numbers and the ability to make artful sense of them makes a great data scientist.
Mastery of inferential statistics
Although it sounds intimidating, the concept is simple. In the world of data science, you can perform two major types of statistical analysis. Firstly, there’s Descriptive Statistics, which simply means organizing and representing your data in appealing charts and graphs. It doesn’t test hypotheses or attempt to draw conclusions. Secondly, and much more importantly, there is Inferential Statistics. This goes beyond describing the sample data and tries to make inferences about the population and possible relationships between the variables.
Being able to draw scientific inferences backed by data is a major step towards being indispensable as a data scientist.
Embracing the mess
Data isn’t always clean. There will be times when there are numbers and variables that appear where they don’t belong in a report. However, machines and people are bound to make mistakes. For instance, in a global survey, there might be discrepancies in the format of the date field entered by Americans and the Asians. It would now fall on the data scientist to not only clean up and organize this, but also use this information to draw valid conclusions.
It is simply a part of the job description to take incomprehensible, messy, and sometimes incorrect data and turn it into useful, actionable insights.
The ability to ‘break it down’
This is one of the most key skills to becoming a successful data scientist. Clients will often come with ‘the big picture’ of what they want. The briefs could be idealistic and generic. It could then become a puzzle that the data scientist needs to take apart and rebuild to make better sense of it. A data scientist’s role involves this breaking down of the client’s vision and transforming it into smaller, tangible missions, which when accomplished lead back to the bigger picture.
The ability to deconstruct the goal, in order to achieve it, is an important skill to possess in the world of data science!
What are some other skills you think are important for a data scientist to be successful? Tell us via comments!