How To Stand Out As A Data Scientist In This Crowded Market

Stand Out as a Data Scientist

Despite being in high demand, the sexiest job of the century, data science is suffering from a talent shortage. Irrespective of one being a data scientist or not, one might have observed an increasing trend in people around them opting for a data science career. With the market being flooded and the talent getting scarce, it is becoming difficult for anyone to get a job in the data sector, but not if one stands out in a crowd of data scientists and bridges the gap desired by the hiring experts.

How To Stand Out?

Standing out in a data science crowd is like climbing a ladder, one has to be careful with every step of the process.

Here are some tips: (For a fresh candidate)

Getting a sense of the Data Science Sector:

For a candidate who is fresh out of grad school or is someone who is switching careers, the first and foremost thing to do is to get an acute idea of what does the data science field offer and the skills required for it.

Acquiring the Skills:

When one has gained knowledge about the relevant skillset needed, the next step is to acquire them; the sooner one gets started with them, the better it is.

Below are listed the most fundamental skills that one needs to have and perfect to make themselves stand out in the vast competition:

Statistical Modelling: Statistical modelling is a must for any aspiring data science candidate. One must be an expert in techniques like linear regression, logistic regression, classification, forecasting, principal components analysis, factor analysis, and resampling methods.

Machine Learning: One must understand the process and the way to run gradient boosting machines, decision tree, CNNs, etc.

Programming Languages: The most common programming languages to which one needs to know are R and Python. Tools like SQL must be known. Apart from that, it is also recommended to know languages like C++, Java, and others.

Database Warehousing: Understand how relational and non-relational databases means.

Storytelling & Visualisation: Have an understanding of tools like Qlik View, Qlik Sense and SAS etc. An absence of narrative might make it difficult for the clients to read the enormous amount of data out there; the best data scientists also know how to communicate this data with them.

Specialisation; After one has gained enough knowledge about the skills and the data science sector, now, they must decide what area to choose.


Get on LinkedIn and start networking with industry experts to understand their needs better. Linkedin is a great way to find industry leaders and to interact with various communities and possibly try to find a competent mentorship. Networking does not only bring one the precise idea of the job market but also helps one to know about the various resources and the trending skills that one can not only acquire for a hunt but also hone their skills more and more to climb the ladder.

Another great way of networking is to attend some of the well-known data science conferences and workshops. When one attends certain workshops and conferences, they can interact with researchers and other industry leaders. Not only the interactions but also one can get a concrete idea about data science as a career from the career track talks.

Networking also helps one to find a mentor. A mentor, aside from being a motivational force, will also show the correct path that can be taken in order to succeed in the data science sector. A mentor assesses one’s progress and might give one an easy track to connect to other professionals.

Join data science groups on LinkedIn and social media platforms to share viewpoints, seek the best jobs out there and have discussions in the groups to have an overall understanding of the data science field and skills. 

Attending the Conferences:

The sectors like data science and AI are continuously having breakthroughs and evolving at a staggering pace. To become someone who needs to have a complete understanding of everything new and everything that is already going on, one needs to attend the best conferences. These conferences not only give one every detail about the latest development of the field but also attracts some of the best leaders and innovators from the field.

Below are some of the best conferences that happen in India:


Hackathons aren’t only a way for tech-enthusiasts and the beginners to test their skills and get some actual experience of the field, but also a great way to get the taste of the real-world problems that can be solved by the tools at hand and their programming skills. Apart from upskilling, it is one of the best ways to show out in front of hiring professionals who also attend these events.

Some of the best hackathons that happen in India:

Grinding It Out In Data Science Bootcamps:

Going to bootcamps is an absolute necessity when one wants to upskill and further enhance their knowledge about data science skills and tools. One can sharpen their skills and apply their knowledge to real-world problems. Below are some of the best data science bootcamps that one can take up; these data science bootcamps are available both online and offline:

Studying Some of The Best Presentations:

Studying presentations give a concrete idea about what skill is needed and what the real-world problems actually demand from a data scientist. 

For example, the presentation; ‘Moving Machine Learning Models To Production’ gives one the understanding of how to move ML models from research to production.

Going through this presentation, one should be able to figure out how important it is to move the machine learning research (because research is the key to moving forward with the new tech) to production. And how being able to write production-level code is one of the most sought after skills out there.

Below are listed some of the best presentations:

  • Moving Machine Learning Models To Production
  • Lesson Learned From Practical Deep Learning
  • Data Standardization With Web Data Integration

Building Projects to Create a Strong Portfolio:

One must start building projects that will not only reflect their skill on their resumes but will also give them a complete understanding of how the whole technology works. Some of the popular projects are:

  • Movie Recommendation System Project
  • Customer Segmentation using Machine Learning
  • Sentiment Analysis Model in R
  • Uber Data Analysis Project

Including these, it is advisable to build and show some projects that reflect the needs of the real-world or have real-world applications that are different from which are already there.


One thing is that getting into the field of data science is hard. Among the massive competition, one needs to be active in all the areas of the sector and whatever rejections in an interview or during the projects one deals with, they have to be tackled with a growth mindset. Whatever ideas one has, be it about GANs, RL, CSS or JS, put them to test and get feedback from someone. Lastly, be patient about the process; learning every skill related to data science, from statistics to programming, to get better at them isn’t a simple job.

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Sameer Balaganur
Sameer is an aspiring Content Writer. Occasionally writes poems, loves food and is head over heels with Basketball.

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