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Top 5 Key Skills A Good Data Scientist Should Have

 Data scientist job roles are hugely in demand and are turning out to be a lucrative career option. However, candidates need to be adept in a wide array of skills from programming knowledge to be good in communication, and more. While the industry has varying metrics on what being a good data scientist is, here are the five key skills a good data scientist should have.

Analytical Mindset

One of the key requirements for a data scientist is to have an analytical mindset with a strong statistical background and good knowledge of data structures and machine learning algorithms. They need to be strong in Python or R and should be comfortable in handling large data sets.  Nearly 70% of a data scientist’s time is spent in data preparation – data cleaning and munging and preparing data such that machine learning algorithms can be applied on that data. So, it is important that they are comfortable with the 4 V’s of data – Volume, Velocity, Variety and Veracity.

Domain Knowledge

It is important for the data scientist to have sound domain knowledge. They need to understand the business problem and choose the appropriate data science model for the problem. They should be able to interpret the results of their models and iterate quickly to arrive at the final model. They need to have an eye for the detail. It also very important for them to have good communication skills as they need to explain their results in simple language that can be understood by a wider audience. They should be able to clearly document their approach so that it is easy for someone else to build on that work. They should be able to understand research work published in their area and apply it for their problems.

Problem Solving Skills

While it is important for a data scientist to keep themselves abreast on the latest tools and developments, it is mandatory for them to work on solving problems. A data scientist is like a doctor, the more problems they solve and more experience they have, they get better in their job. That is why companies value experience a lot more than the educational qualification. But it is important to have the basic educational qualification. A full-time course will be valued more than an executive course.

Statistical And Programming Skills

A data scientist is expected to have a good knowledge of statistics, mathematics and algorithms and good software engineering skills. They should start with a basic course on statistics and mathematics with a primary focus on probability, set theory, algebra, functions and graphs. Then they need to learn a programming language preferably python along with libraries such as pandas, numpy, scipy and matplotlib or R. They should then learn machine learning and if needed advanced topics in deep learning. There are a lot of free and paid resources to learn these topics.  There are free and paid beginner and advanced level courses in Coursera, Udacity and EdX. There are free short courses offered by Kaggle and Google’s AI team. They are a lot of free lectures on YouTube from universities like Stanford. Once they have completed these courses, they would need to apply their knowledge in solving practical problems. They can do this by participating in competitions hosted by sites such as Kaggle.

Solving Real-World Problems

If a student wants to choose data science as a career, they should start paying attention to subjects such as Statistics, Probability, Algebra, Set theory and Data Structures and Algorithms. If they are strong with the basic concepts, then they can use the technology tools to their advantage to build great models.

While a lot of theoretical knowledge can be gained by doing these courses, their learning would not be complete until it is applied to practical problems. Industry mentors can play a vital role in this aspect. They will also help in understanding the practical difficulties in applying their knowledge to real-world problems. This will also help them in building their domain knowledge that will help them to be a good data scientist.