Guest blog post by Mirko Krivanek
Usually I tend to criticize this type of articles, but in this case I agree pretty much agree with BurtchWorks, the author of this article, even though the article is more than 6 months old. Note that BurtchWorks is a recruiting firm that recently posted interesting salary surveys for data scientists.
Below is the skills list they recommend:
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%).
- 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.
Doing a quick search for becoming a data scientist will provide tons of additional valuable information.
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