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Lets cut to the chase – being a data scientist isn’t a breeze. It’s honestly no wonder that there aren’t an abundant amount of data scientists in the workforce. We’re here to tell you that data science actually rocks regardless if it’s for the faint of heart or not. Here’s the thing though, the knowledge a data scientist knows should not only stay with the boundaries of their minds. In fact, if you’re a business owner or have someone within your office who is responsible for decrypting and sharing data then it’s important to know these basic skills every user should have.

It's Not Rocket Science, Just Data Science - IronRockKnow Your Stats

By this I mean, having good chops in statistics is vital. Hypothesis testing, probability, descriptive and inferential statistics are basic building blocks of data science. More than anything, having a good grasp on business analytics is a metaphorical difference between life and death. If you hate math, then this field may not be for you, being a data scientist or hiring a data scientist requires someone with great statistical background.

Flex Your Programing Skills

The world of data science is a fast-paced environment. It’s imperative that you or the data scientist you hire is skilled in not just one, but many different statistical languages. To be successful, a beginner must at least know SAS and R. There’s brownie points up for grabs if you are well educated in Python, Perl, Java, Hadoop, Hive or Pig as well. Your business doesn’t want to suffer just because you or your programmer doesn’t know these statistical programming skills. Knowing more than one is important to be able to handle any data environment your company may find itself in.

Have a Solid Business Acumen

If you want to be able to move up in the world of data science then you must also be well-versed in business intelligence as well. A successful data scientist should be able to understand the business behind analytics. In return your business must be able to align their strategies with the findings in an analysis.

Know Good Techniques and Algorithms

It’s a great advantage in data science to have hands on experience in different statistical techniques and a good understanding on the current happenings in the analytics industry. Some examples of useful statistical techniques and algorithms to know are as follows:

  • Linear Regression
  • Logistic Regression
  • Time Series Forecasting
  • Clustering
  • Decision Tree

Be a Communicator It's Not Rocket Science, Just Data Science - IronRock

In a way, a data scientist can make or break a business. If you are not able to clearly and concisely convey the data to others, tech savvy or not, then you’re more of a hurt than a help. How useful is all the data that you are culling if you’re unable to share what it means in an effective way? If data is not communicated effectively, then it cannot be acted upon. You or your data scientist can pave the way for many of the next steps of your business if they can communicate data findings effectively.

These are just 5 must-have skills a data scientist should have. If you’re a data scientist it’s paramount that you have an understanding of each point. If you’re looking to hire a data scientist, be sure they meet the criteria in each of these points.