You must have understood by now the basics of what data is and how data explosion has taken place in recent years through digitalization. Your interest levels must have risen and yes everyone new to the industry wants to know about the “skills” required for a data scientist to make it work in this industry of the future.
If you don’t know the basics or are yet to know about the evolution of the big “Data” boom, do visit our blog on “Data Science – Where it all started”.
Data Scientists are all about data, data, and yes, more data. They strive to clean the data, process it and apply various algorithms to extract valuable insights on areas like how the Paytm food delivery services became popular (You didn’t know about it until you read this, right?).
Or Are we alone in the universe? (An interesting breakthrough in analyzing NASA data using neural network algorithms).
Skills in this field cannot be classified but can be outlined on the basis of:
But, if you think it is the tech guys that are going to be up for the grabs, you are mistaken.
Recent surveys show that a relative portion of the data scientists don’t start off as amazing coders but rather, start by understanding the data sets first, realizing how they can help in recognizing a problem and figuring out solutions accordingly. Basically, if you have a genuine interest to learn, coding is not even an obstacle rather it is going to be your best friend/companion.
Data Science has become such a concept that its applications are outspread and not just limited to IT, health, FMCG, retail or agricultural sector. One way of complying with the growing demand is to have a good academic background (Not good grades, but slightly good grades)
Essentially, a bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics would be good. One can also enroll for a certification program that will help industry experts realize the incredible potential of an individual.
While it is important for a Data Scientist to keep updated with the latest developments, it is also important for them to have a knack for trying to learn and get to know what the company is, what the company does, and most importantly how can they solve the problem the company is facing.
A data scientist is expected to develop complex operational models for companies or businesses. Generating a hypothesis based on how a system will behave with changes, defining metrics to understand objectives, measuring success, and drawing accurate conclusions requires having the interest to play around with math, especially statistics. Hence, a promising data scientist should be endowed with the ability to handle math without passing out.
A person aspiring to become a data scientist should make sure handling data for them is as simple as reciting the alphabet. PS: Sometimes forwards, backward or maybe from the middle too.
Being a good communicator is of prime importance, someone who can clearly and fluently distill challenging technical information into a complete, accurate, and easy-to-present format.
A Data Scientist should help the company by providing quantifiable insights, translate the statistical output into actionable recommendations and also be well-versed in soft skills. Finding out a solution for a problem sometimes requires more than one brain to do so and they should be a team player in this scenario.
Nearing the most important skillset, coding is mandated by the companies to help the scientists working with real-time data, cloud computing, unstructured data, as well as statistical aspects.
A successful data scientist needs to have the knowledge of programming languages like Python, Perl, C/C++, SQL, and Java—with Python being the most common coding language required. So, keep python on the top of your to-learn list as it is soon going to outstrip the other languages due to its flexibility and relatively easy to use features.
A good data scientist figures out the right tool for the right job. Sometimes Hadoop is total overkill, and sometimes a simple statistical regression will outperform Tensorflow. The tools can be broadly classified into two types:
The technologies associated with the first skill are SQL, Databases (Postgres, SQL, MySQL, MongoDB), shell scripting for ETL processes, and a host of others like Hadoop, Spark, Kafka, etc.
The technologies associated with the second skill are R, Python, Julia, Tensorflow, etc.
In the end, it all boils down to the love for data. A Data Scientist should have the inquisitiveness to view information as clues of a map, manipulate them to form a roadway for multiple outcomes and extract all the possible paths.
But the stark difference is that every problem requires a unique solution. The methods might seem similar, but the outcome should always be faster, better and more optimized. A Data Scientist must realize it and mould its solutions around this basic principle.
Well, these are the skills which we at INSOFE, consider to be the defining key indicators for a data scientist. Do comment your views about any other skills that an aspiring Data Scientist should look out for.
Also, check out our PGP in Data Science course to know more about how to kick–start your career in Data Science.