Career Trends: Must-have Skills for a Data Scientist

5 min read

Edition: October 22nd, 2021
Curated by the Knowledge Team of ICS Career GPS

Data Scientists are all about data, data, and yes, more data. (Image Source:

You must have understood by now the basics of what data is and how data explosion has taken place in recent years through digitalisation. 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

Skills in this field cannot be classified but can be outlined on the basis of:

  • How good you are with using the data?
  • How far can you see (Into the future, I don’t mean a time traveler)?
  • How accurate is the data map that you can form/predict?
  • How well you code i.e. your technical skills. 

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, realising how they can help in recognising a problem and figuring out solutions accordingly.

If you have a genuine interest to learn, coding is not even an obstacle rather it is going to be your best friend/companion.  

Here are the 7 must-have skills for a Data Scientist:

1. A good academic background

  • 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.
  • 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 realise the incredible potential of an individual.

2. Keen understanding of business

  • 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. 

3. Math skills

  • 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.
  • 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.

4. Communication skills

  • 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. 

5. Coding

  • 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, & Java.
  • 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.

6. Understanding of analytics tools

  • 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:
  1. To find and gather the data
  2. To analyse that data
  • 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. 

7. Ready to play with data

  • 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 optimised.
  • A Data Scientist must realise it and mould its solutions around this basic principle.

(Disclaimer: The opinions expressed in the article mentioned above are those of the author(s). They do not purport to reflect the opinions or views of ICS Career GPS or its staff.)

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