Over the past year, interest in data science has soared. Nate Silver is a household name, companies everywhere are searching for unicorns, and professionals in many different disciplines have begun eyeing the well-salaried profession as a possible career move.
In our recruiting searches here at Burtch Works, we’ve spoken to many analytics professionals who are considering adapting their skills to the growing field of data science, and have questions about how to do so. From my perspective as a recruiter, I wanted to put together a list of technical and non-technical skills that are critical to success in data science, and at the top of hiring managers’ lists.
Every company will value skills and tools a bit differently, and this is by no means an exhaustive list, but if you have experience in these areas you will be making a strong case for yourself as a data science candidate.
Technical Skills: Analytics
- Education – Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. Their most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). You can learn more about this in our Burtch Works Study: Salaries of Data Scientists, which can be downloaded here.
- SAS and/or R – In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred.
Technical Skills: Computer Science
- Python Coding – Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++.
- Hadoop Platform – Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial.
- SQL Database/Coding – Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL.
- Unstructured data – It is critical that a data scientist be able to work with unstructured data, whether it is from social media, video feeds or audio.
- Intellectual curiosity – No doubt you’ve seen this phrase everywhere lately, especially as it relates to data scientists. Frank Lo describes what it means, and talks about other necessary “soft skills” in his guest blog posted a few months ago.
- Business acumen – To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.
- Communication skills – Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately. Check out our recent flash survey for more information on communication skills for quantitative professionals.
The next question I always get is, “What can I do to develop these skills?” There are many resources around the web, but I don’t want to give anyone the mistaken impression that the path to data science is as simple as taking a few MOOCs. Unless you already have a strong quantitative background, the road to becoming a data scientist will be challenging – but not impossible.
However, if it’s something you’re sincerely interested in, and have a passion for data and lifelong learning, don’t let your background discourage you from pursuing data science as a career. Here are a few of the resources we’ve found to be helpful:
- Advanced Degree – More Data Science programs are popping up to serve the current demand, but there are also many Mathematics, Statistics, and Computer Science programs.
- MOOCs –Coursera, Udacity, and codeacademy are good places to start.
- Certifications – KDnuggets has compiled an extensive list.
- Bootcamps – For more information about how this approach compares to degree programs or MOOCs, check out this guest blog from the data scientists at Datascope Analytics.
- Kaggle – Kaggle hosts data science competitions where you can practice, hone your skills with messy, real world data, and tackle actual business problems. Employers take Kaggle rankings seriously, as they can be seen as relevant, hands-on project work.
- LinkedIn Groups – Join relevant groups to interact with other members of the data science community.
- Data Science Central and KDnuggets – Data Science Central and KDnuggets are good resources for staying at the forefront of industry trends in data science.
- The Burtch Works Study: Salaries of Data Scientists – If you’re looking for more information about the salaries and demographics of current data scientists be sure to download our data scientist salary study.
I’m sure there are items I may have missed, so if there’s a crucial skill or resource you think would be helpful to any data science hopefuls, feel free to share it in the comments below!
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