ITA18FXIBLOver 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

  1. 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.
  1. 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

  1. Python Coding – Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++.
  1. 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.
  1. 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.
  1. 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.


Non-Technical Skills

  1. 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.
  1. 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.
  1. 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:


  1. 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.
  2. MOOCsCoursera, Udacity, and codeacademy are good places to start.
  3. Certifications – KDnuggets has compiled an extensive list.
  4. 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.
  5. KaggleKaggle 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.
  6. LinkedIn Groups – Join relevant groups to interact with other members of the data science community.
  7. Data Science Central and KDnuggetsData Science Central and KDnuggets are good resources for staying at the forefront of industry trends in data science.
  8. 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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21 Responses to “The Must-Have Skills You Need to Become a Data Scientist”

  1. Ross Bettinger

    You are addressing the new entrees to the field. You might also, in a subtle manner, indicate that as people age, they become less and less desirable to employers in today’s job market. Tell them not to grow old, and if they find a way to slow down the onset of entropy, ask them to share their knowledge with you. Perhaps you will find a way to bottle it and provide for your advanced years and those of your family and friends :^|

    Tell them that every job that they take must give them skills to take to their next job, and when they are cast out into the street because their employer wanted to make his first-quarter earnings look better, it’s no reflection upon them.

    I could go on in this vein, but you would think me bitter, and of course, I am not bitter. The most valuable thing that you can tell anyone starting out is to learn, as the Chinese phrase has it, “to eat bitterness.” End of rant.

  2. Kamraj Subramanian

    I would also like to add another resource here. Jigsaw Academy, the Online School of Analytics. They have some great courses and certifications in Data Analytics and Big data and have been rated among the best online analytics training institutes. Their blog also has some very interesting resources. I did a couple of courses with them and I must say my experience has been fantastic and as they focus on industry relevant skills, I think what you learn with them really allows you to hit the ground running as a data analyst.”

  3. George Danner

    Hi Linda–

    There’s one subtle thing I have noticed that separates the good data scientists from the really great data scientists: the ability to draw. If you ask one of them to explain their most recent project and they cannot wait to get on a whiteboard and draw it out in clear, simple graphics, then most likely you have an excellent candidate. If you don’t have those skills and aspire to become a data scientist, get a proficiency in technical drawing – Visio, Omnigraffle, or Illustrator – pick your tool.

  4. Ramani Natarajan

    Hi Linda,

    This is a good article to which I would like to add that one of the most important skill in a Data Scientist’s toolbox is to communicate the procedure and results. Visual creativity is a must and I don’t mean just creating pretty PowerPoint presentations. Having said this, these are my take on some requisites for a Data Science professional:

    Scripting Languages: Python, some Java.If the candidate has exposure to C the candidate would have learned the basics of memory manipulation and would have gotten an idea of computational complexity
    Command Line Scripting: sort, uniq, grap, awk, cowsay, set, cut, find, xargs, and lots more. Given the custom nature of projects Data Scientists handle, command line familiarity is a must. At the end of the day, ability to script from scratch allows for more control over the data and the algorithm that eventually gets implemented.
    Statistical Packages: R, SAS, ideally, one of the two at a minimum
    Data Mining: KNIME, Salford Predictive Modeler, SPSS Modeler, or some such package that will allow for rapid data mining with smaller datasets for a quick turnaround without having to sling code.
    Analysis: NumPy, SciPy, SciKit Learn, Celery, and some vicious googling skillz to unearth information already on the web
    Graphics: GIMP, Inkspace, ePix, Asymptote, etc.
    Web: Django, HTML5,
    Databases: MySQL, MongoDB, CouchDB, HBase, etc
    Big Data: The Hadoop ecosystem.

  5. LHM

    This newly evolving field is fascinating. My husband and I have always said that it sounds like the combination of me and him–I have a PhD in social science research with a quantitative emphasis and statistics expertise, and he is a computer programmer in the vein of C++ and scripting languages. He is an expert at manipulating unstructured data. Interesting to read about these positions as they grow.

    • Lynne Thomson

      I think you put your finger on the problems with this… they are looking for unicorns: people with awesome technical skills AND domain expertise AND visualization… I’m surprised they haven’t specified singing and dancing as well. Organizations function because the combination of several people is more powerful than any individual.

  6. Aaron McManus

    Thanks, this was valuable information! I didn’t realize that coding was essential – that’s interesting to know. I’d be curious to know more about the soft skills that you see as essential; it seems like emotional intelligence would be very valuable for data scientists to connect with their audiences and help apply the information they’re crunching.

  7. S Kirpalani

    Hi Linda,
    I think “An Eye For Detail’ is very important, along with good storytelling and visualization skills.
    As regards SAS and/or R, if you are catering to a SAP audience, then SAP tools like HANA along with R could be used.

  8. Anand

    I have completed many data science related MOOC’s and am good at it. However i got a rude awekening when I tried to apply data science in the real world. The data needed for analysis will not be readily available to you. So along with the skills mentioned in this article you definitely need the people skills to convince the data holders to give you the data and once you have the data you need the skills to parse the data (a very cumbersome process) to get the relevant information needed for the analysis.

    • hassan

      which MOOC’s did you take and i wonder whether courses in Edx and Coursea really benefit in terms of learning the concepts as effectively as in classroom. Please specify the courses that you took and how it benefited you with the level of expertise you already had in the data analysis when you joined these courses.

  9. Quantsauce

    This is an exciting new field and career path for individuals strong in Math and statistics.

    I wonder if there are many data scientist that have degrees in economics..



  1.  Linda Burtch on the Skills Data Scientists Need - Analytics Training Blog
  2.  Burtch Works 2015 Predictions - Analytics/Data Science Hiring
  3.  4 Ways to Spot a Fake Data Scientist - Burtch Works
  4.  Not Just a Title: How to Identify a Data Scientist - Burtch Works
  5.  Educating Future Data Scientists in Real-Time Analytics
  6.  Nate Silver and Skills for Data Science | Computing with Data
  7.  What is a Data Scientist? An In-Depth Look
  8.  What makes a great data scientist? | Aviana Global
  9.  How to Become a Data Scientist - IT-oLogy
  10.  The Skills You Need To Launch A Data Science Career | Udacity

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