Job Of A Data Scientist: What A Day at Work Looks Like

For upon |An article published in Forbes magazine claims that by 2020, 1.7 megabytes of new information will be created every second for every human being! Does this figure surprise you? Well, I am not awestruck looking at our daily schedule and then looking at the figure again.

We all spend hours on our smartphones, either scrolling down newsfeed on Facebook, watching videos on Youtube, messaging one another on Whatsapp or searching queries on Google. How can I forget to mention the photographs we click and circulate or post every day? By the end of the day, we all have contributed our shares for generating reams of data.

What does a data scientist do?

Nothing extraordinary! A data scientist just feeds on the huge amounts of data we generate. He is a pioneer in coding and executing algorithms, performing statistical operations, doing mathematical calculations and putting all his knowledge in the right direction to give miraculous and astounding results. He works on all sorts of structured and unstructured data in order to bring best out of the waste. Every single time he works on the data, he brings something new out of it. Thus, a data scientist is an expert who has the capability to solve all the complex real-life problems.

Skills required to be a data scientist

A data scientist begins his career as a data analyst or statistician. But what brings his career to the stage of becoming a data scientist is a set of skills which he learns with time and experience. However, whether you are a newbie in this field or a professional, here are some of the capabilities you must have as a data scientist:

• Good Hold Over Statistics: Since your days will start with loads of unstructured data in front of your eyes, the only way you’ll have to get out of the web is using statistical and mathematical skills precisely and accurately.
• Acquaint Yourself with one of the Programming Languages: There are many programming languages which are helpful in creating algorithms for execution of daily tasks such as R, Python, Java, SQL, Tableau, C and many others. You should have a good command on any one or two of these.
• Machine Learning: Computers work on their own but humans have to design algorithms for them. If you have been working in companies such as Google, Uber or Facebook, you must be knowing that almost all their operations are data-driven and a data scientist has to design algorithms to drive that data. Although libraries of Python and Java offer techniques to implement these functions, having knowledge about them can be a good addition to your existing skills.
• Data Wrangling: there will be times when you will have to face the data where either some of the entries are missing or the mentioned entries are ambiguous and make no sense. To get out of such a puzzling situation you will have to learn to deal with such imperfections of data and get out of web through smart guesses.
• Data Communication: you fail to call yourself a data scientist if you cannot explain the findings of your data to your audience even if it includes people who are not aware of the concept of data science. Thus, good communication is a necessity to be a data scientist.

Thus, a typical day of a data scientist revolves around data. Merging data, analyzing data, looking for common trends, using algorithms, simplifying data problems and finally unearthing something productive out of the whole data completes your job profile.