Five Essential skills to be a successful Data Scientist

• Read original article here

Here are the five key essential skills to be a successful data scientist.

In present times, becoming a data scientist is much more expeditious by attending a couple of online courses and obtaining some certifications. However, to be successful in the data scientist role, one has to focus on critical skills that take data scientists to new heights in their career.

Understanding the problem statement and turning into a data-driven solution is primary responsibility for any data science role, particularly for a skilled data scientist. In most of the cases, end-user doesn’t know what problems can be solved by advanced analytics vs what cannot be solved, and it is data scientist responsibility to make business teams mindful of what can be achieved.

One of the essential areas any data scientist should focus on is domain knowledge. As a data scientist, one has to deal with data in a more meaningful way to transform complex data into valuable insights & actions, and this can be achieved only with thorough domain knowledge. Having reasonable control of domain knowledge is very important in asking the right data to solve the problem and handling the data in a more precise way.

Excellent programming skills are utmost important to be called as ‘Data Scientist’ unless one chooses to be in managerial roles. Mastering coding skills remains shot in the arm for a data scientist to handle any problem that can be solved using advanced analytics techniques. I have listed some of the links to improve coding skills and helps in getting hands-on individual’s choice of programming language. Although there are many automated machine learning solutions out there, none of them can replace the need for programming based data science roles. One another way to hone programming skills that require for data science roles is participating in hackathons as this allows competing with fellow data scientists and help an individual to identify areas to focus.

Some of the excellent learning sources: https://www.topcoder.com/thrive/articles/List%20of%20awesome%20learning%20resources

Most of the times, typical data science role demands to explain the model outcome to non-technical or people with less knowledge in Machine Learning world. To be a successful data scientist, educating the end-user about the outcome of ML models are equally important, along with building Machine Learning models and getting perfect predictions. As traditional approaches are solving the majority of the problems, making end-user to accept and adopt the data-driven solutions is an uphill task for a data scientist to handle.

One of the critical areas most of the aspiring data scientists tend to forget to focus is how to operationalize the Machine Learning models. Along with the skills mentioned above, every data scientist should think at the very beginning of the project on how to make machine learning models deployable and maintainable.

To be a successful data scientist and lead by an example, one should have(but not limited to) above mentioned skills. Apart from these, a good understanding of Probability and distributions, Matrix decompositions, statistics, linear algebra would help comprehend the machine learning algorithms.

Please comment, feedback on my take !!!