4 Key Skills To Look For In a Data Scientist

In last week’s blog, we discussed what a data scientist does, and whether or not they’re necessary to a small business’s success. We also discussed how while sometimes it isn’t worth hiring a data scientist full time, contracting a data scientist for projects or periodic check-ups can be very beneficial for a small business. The problem with data science is that it’s still a relatively new field, and there is very little training offered at this point in time.

So what qualities separate the amateurs from the experts? While there are many conflicting opinions, we’ve identified 4 key skills that we believe every data scientist should have.

1. Machine Learning & Data Mining

A visual network of the skills a data scientist typically has. Image courtesy of dataconomy.com/
A visual network of various skills that a data scientist may have. Image courtesy of dataconomy.com

Machine learning and data mining in particular are two forms of analyzing data that are very similar, often using the same methods of approach, but differing wildly in their intent. Machine learning is based in logical patterns and analyses, and focuses on predicting future trends based on known data. Data mining, on the other hand, focuses on discovering new, previously unknown aspects of accumulated data in order to turn it into understandable patterns for future use. While it’s preferable for a data scientist to have experience with both machine learning and data mining, the fact that the methodology behind both systems is generally the same means that it’s fairly simple to pick up one, even if you only have experience with the other.

The other reason machine learning and data mining are grouped together is because they are heavily intertwined and essentially fall under the wider umbrella of quantitative thinking. Quantitative thinking refers to the ability to take mathematics, data sets, and algorithms, and derive practical meaning from them. One example of this is using fundamental statistical analysis to gauge the accuracy of a statistical study. Machine learning and data mining are simply different ways of acquiring and looking at this data.

2. Programming Skills

Having strong programming skills is what sets data scientists apart from other analyst type positions. Because data scientists work with data that’s been generated electronically, they often have to perform activities such as sampling and pre-processing the data, developing a model estimation, and post-processing. While there are many programs that are designed with the intention of automating this process, like IBM’s Watson, many businesses require algorithms tailored specifically to their needs and expectations. This means that data scientist need a strong background in at least one scripting language, such as Python, in order to automate repetitive tasks or execute specific routines. In addition to programming scripts, data scientists are also expected to be able to operate databases, such as Hadoop and SQL Servers.

3. Data Visualization & Communication

Once all the data has been analyzed and conclusions have been drawn, data scientists need to be able to present their findings. This isn’t just a case of creating graphs, flowcharts, or powerpoint presentations; they need to be able to communicate the details of their data in a way that’s understandable by everyone, especially to people who aren’t entrenched in IT, such as business managers and department heads. Right now, while many companies are eager to employ data scientists, they are far more hesitant to actually put their suggestions to good use. For this reason, it’s imperative for data scientists to be skilled and persuasive speakers. A good data scientist can generate a report that explains their findings, but a great data scientist will be able to convey just how important it is to implement solutions based on these findings.

A sample data visualization infographic. Image courtesy of videohive.net/
Infographics are quickly becoming the most popular form of data visualization. Image courtesy of videohive.net

4. Business Acumen

So how does a data scientist know what sort of solutions to suggest? By having a good sense of business acumen, of course! This doesn’t just refer to business in the typical sense, but to the industry they’re a part of, and even more so for their company. Because data scientists are hired to solve problems, they need to be able to grasp the core issue of the business they’re working for and how it functions. You can be an expert in any of the skills listed above, but if you don’t understand the problem you are looking to solve, you simply won’t be effective. When hiring a data scientist, it’s best to look for one with a background in your company’s industry. Having a thorough understanding of the industry, and the company they work for, will enable a data scientist to make more insightful observations and create better solutions.


Do some of these skills seem familiar to you?  That’s because they  aren’t associated with data scientists alone. There are many professionals in the IT industry who are fully capable of performing the same duties as a data scientist, such as a business analyst or a software engineer, who just haven’t adopted the title of data scientist. For this reason, many tech companies and contractors may not have a designated data scientist on their team, but will have someone who is still able to perform all the same duties and provide the same services with the same amount of quality and attention to detail. It’s all about finding that person, either in your organization or outside it, who can bring all four of these key skills to the table.

Are you an IT professional looking to expand your skills into data science? Are there some key skills you feel we’ve left out? Looking to include a data scientist in your next big project? Comment below, or reach out to us on our contact page! And don’t forget to like and share!


About the Author:

Andrew is a technical writer for Deep Core Data. He has been writing creatively for 10 years, and has a strong background in graphic design. He enjoys reading blogs about the quirks and foibles of technology, gadgetry, and writing tips.

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