Top 5 Skills Every Data Scientist Should Know in 2017


January 24th, 2017

Data is everywhere and companies are hiring Data Scientists to bring some meaning to this voluminous data. However, when it comes to day-to-day operations, most Data Scientists end up merely pulling data out of relational databases, handling data in Excel using Pivot tables and Charts, handling Google Analytics and working on other small-time analyses.

Data science is capable of much more when it comes to solving real problems. Read on to know the 5 important skills you need to become a master data scientist – the real problem solver and prepare yourself for the future.

  1. You should have a strong quantitative background

By quantitative background, I mean a post-graduate degree in a subject like mathematics, statistics, computer science or software engineering. If you hate math, data science is not a place for you to be in. Master data scientists are highly educated and have great knowledge.

  1. You should have analytical and programming skills

Analytics is a fast-paced and dynamic industry. Apart from your degree, you also need to have in-depth knowledge of at least 2 analytical tools such as R, SAS, Python, Perl, Java, Hadoop, Hive and Pig. You should learn some data mining and visualization tools as well.

  1. Your business acumen should be good

Business intelligence along with leadership and project management skills will no doubt leverage your chances to move up the corporate ladder in the field of data science. You should be able to understand the business behind an analysis. You should be able to align your business strategies with the findings of your analysis.

  1. You should have a great intuition about data

You need to understand data and should be able to get dirty and swim into terabytes of data. You should be willing to remove the mess in the data that is caused by noise, missing values, outliers, etc. and come out with clean data that makes sense. You should be able to report from both structured and unstructured data. You should be able to slice and dice the data and prepare intuitive reports.

  1. You should be a great communicator

Unless data is communicated effectively, it cannot be acted upon. You should be able to put forth your analysis in a presentable way that can pave way for some serious actionable items. You should be able to present a story out of the discoveries you have made in a language that your non-technical bosses can understand.

If you are at a crossroad where you have completed your education and are looking for a career in numbers, take a deep breath, roll up your sleeves and follow the above mantras to reach the top rung of your career ladder.


Suggested ReadsĀ 

5 Reasons to Learn Python if You Already Know R Programming

Confused About a Data Science with R vs. Big Data Analytics with Hadoop Course?


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