Good data scientists are familiar with machine learning libraries and algorithms. It is akin to being an amazing pilot of an airplane, with skills that go beyond flying and borders an airplane mechanic. But to be a great data scientist, those skills will have to surpass the mechanics and thus require a greater understanding. The great data scientist knows how those libraries and algorithms work under the hood. The great data scientist understands the mathematics behind the science. With the speed of technology, there may come a day when the algorithm itself replaces the data scientist.

One doesn't need to have an innate talent to become a successful data scientist. Yet, some skills are required to be successful in data science. All those key skills can be acquired by anyone with proper training and practice. In this article, I am going to share some of the important skills, Why they are considered important for a data scientist. Also, How those skills can be acquired. Data Scientists should develop the habit of critical thinking.

How do you keep excelling at it and prove your value to produce meaningful business impact? I will be sharing general summary of the qualities/ traits/ mindsets that I have observed /picked from some great data scientists in their work and also from reading the (The Data Science Handbook: Advice and insights from 25 Amazing Data Scientists by Carl Shan, William Chen, Henry Wang, and Max Song), that makes them stand out and differentiates them from good data scientists and how these traits can be learnt/acquired. They do all these because they care about the impact of their work and not because they want the attention or boost their career. They don't start a discussion just for the sake of having a discussion and they are able to step back when their opinions don't resonate with others. Kennedy Wangari is a highly talented and dedicated Natural Language Processing Engineer, an AI Community Leader, and Innovator.

The data science job market is changing rapidly. Being able to build machine learning models used to be an elitist skill that only a few distinguished scientists possessed. But nowadays, anyone with basic coding experience can follow the steps to train a simple scikit-learn or keras model. Recruiters are being flooded with applications, because the hype around the "sexiest job of the century" has barely slowed down while the tools are becoming easier to use. The expectation of what a data scientist should bring to the table has changed and companies are beginning to understand that training machine learning models is only a small part of what it takes to be successful in data science.

With big data ushering us all into the era of IoT and artificial intelligence, every company looking to succeed in the data-driven future must fill its team with competent data scientists, engineers, and developers. The task of finding talented individuals may seem daunting at first, but with these skills on the top of your watch list, it will become easier to spot top talent and hire it for yourself and your company. The best and brightest data scientists should all be aware that machine learning and deep learning are the pathways to our artificially intelligent success. As all our machines and devices become linked up, the data they take in becomes crucial to the learning they do. Data scientists must understand how to use this data to guide machines forward on the path to true artificial intelligence, and your company must find the data scientists with these critical skills.

Data Science is a comparatively new domain which most of us is not thorough with, agreed? In fact, it is such a vast area that companies go through a hard time determining the prerequisites needed in a good candidate. Hiring and interviewing them is a challenge which surely follows. So we decided to interview our top Data Science expert, Saikat Sarkar, who has been working and teaching in this domain since the last 10 years. He is presently acting as the Subject Matter Expert of Python in Aegis School of Data Science.