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5 Key Data Science Skills: You Need in Your Data Scientists

Looking for companies who can handle your initiatives in big data can be very challenging. Now a days every IT company is trying to position itself as an expert in data science. But how much of that is actually true. The field of data science demands a wide array of skills. While hiring a vendor or consultant to guide your big data analysis you need to look beyond technical skills. Any technical knowledge or statistical skills will lay waste if the company is not approaching the problem with a data driven, open-minded approach. You need to find companies who have the business acumen to analyze business problems and figure solutions by using your data. By analyzing these 5 data science skills in vendors you can ensure that make a right choice. Make sure that you don’t miss out any of these while discussing with vendors about any project.

Read More: How to Create a Big Data Strategy for Business in 7 Steps

Key data science skills you need in vendors for successful execution big data projects:

1. Thinking as a data scientist:

This is the most in-demand skill in data science. When you plan to hire a data scientist or outsource development the first thing you need to see is whether the concerned person or company has a data-driven approach or not. Thinking as a data scientist refers to the intellectual curiosity that pushes people to look for solutions and relations they never thought would exist. The whole point of doing data analysis in the first place is to look at the problem in hand and figure out solutions using data. You need to test the acumen of the company by asking real life business problems and analyze their approach to solving it.

Read More: Top 5 Trends in Big Data

2. Ability to setup a layman language:

There are many issues regarding how a communication is established between data scientists and executives. The language involved in the communicating data insights is too complex to be understood by managers from a non-technical background. It needs to be ensured that whatever insights are generated are communicated properly in an easy to understand language. The big question is how to define that language. While outsourcing projects ensure that a person with requisite knowledge of both data science and business is involved. This will ensure that you fully understand the insights without getting into too many technical details.

3. Past track record:

There are many companies who are trying to get into big data analysis. Be it startups or MNCs, big data has become a buzzword for every executive. With so many companies defining themselves as big data experts it necessary that you separate clutter from the pile. What can really help you choose experts is select companies which have previously done big data analysis for some other clients. Alternatively, you can look at the LinkedIn profiles of persons you communicated with. This will also give you insights on the capabilities and market potential of company’s executives.

4. Technical skills:

Your data scientists should be well versed to handle the technical side of big data analytics. The technical knowledge can ideally be divided into statistics, advanced programming, cloud computing and big data tools.

  • Having knowledge of statistical analysis helps to make sense out of data and drive insights. This involves in-depth knowledge of statistical methods, multivariable calculus, and linear algebra.
  • Advanced programming involves working on complicated algorithms based on machine learning and data analysis. It involves hands on experience on languages like R and Python.
  • It is a great add-on if the company’s team also has knowledge of cloud computing solutions like Amazon S3 and working on data tools on cloud platforms.
  • Big data tools involve frameworks that are used to work on big data like Hadoop, Hive, Pig etc. It also includes knowledge of big data visualization tools like Tableau, QlikView, Plotly etc.

These technical skills collectively bring out the insights in big data. It is important to ensure that your vendor has these skills and is capable of dealing with all forms of data whether it is organized or unorganized. You can probably hire a consultant or involve someone from your company’s IT team in conversation with the prospective vendor.

Read More: 3 Reasons: Why most Big Data projects fail

5. Business Acumen:

While looking for data scientist you are essentially looking for people who can not only analyze data but also advice on selecting the right business problems to solve and how you should use your big data. This needs a solid understanding of the industry workings and the impact of insights on business decisions. In addition to identifying new ways in which you can leverage your data, you also need to prioritize business problems and identify data sets that can be analyzed to solve the issue. This guidance can only be given by people who have business acumen along with the key data science skills, to guide your efforts in right direction.

Based on your analysis of the above data science skills you can collect and evaluate vendor proposals. The main idea is to collect proposals from vendors who you think would able to match these skills and accordingly make your judgment. We have also created an ebook to help you get accustomed to the big data space. Download it for free here:

From customer segmentation to sentiment analysis, at NewGenApps we have worked on many big data projects.

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Anurag

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