Whether you can call yourself a data scientist if you can't code is as hotly debated as Brexit. Type the question "Can you be a Data Scientist without coding?" into Google and you'll get a hundred different answers. The opinion will vary wildly depending on whether the author is a coder, or a non-coder. Search the job listings, and you won't find a definitive answer there either. A Glassdoor survey on the skills required for data science job postings showed that for every 10 job postings, nine required at least Python, R, and/or SQL skills. So while coding is a "common" requirement, is it a necessity in today's ever changing machine learning landscape?
Rather than fill the internet with yet another opinion (as I am not a coder, my opinion would be quite biased), I thought I would perform a little meta analysis. For this post, I pooled data from about sixty different sources, to uncover current thinking on what is quite a debatable topic. You won't find all sources listed below, as I have no wish to bloat out a blog post with a dissertation worthy reference list. But my methodology was simply to type the question into Google, and click on the first 25 search results for each category (Opinion Sites/Business/*.edu/). I figured that would get me the current/popular thoughts. Any "Wiki" style sites were filtered out (as it would be difficult to ascertain whether those posts were the opinions of a business person, academic, or other), as were multiple posts from the same site.
Opinion sites (like newspapers and magazines) and bloggers are firmly split down the middle of the "coding or no coding" debate. The analytics dude Eric Hulbert sums up the answer to the question "Can you be a data scientist without knowing how to code?" with a single word "Nope" (although he does go on to explain the "nuances" of that statement). Rachael Tatman, writing on freeCodeCamp states that every data scientist should be able to "write code for statistical computing and machine learning."  Ronald Van Loon agrees, giving a rather lengthy list of required technical skills including knowledge of programming languages like Python, Perl, C/C++, SQL and Java plus expertise in tools like SAS, Hadoop, Spark, Hive, Pig.
Also in the "No" camp is the Executive recruiting company Burtch Works, which lists the following "Must-Have Skills that employers are looking for: Python coding (along with Java, Perl, or C/C++) and machine learning. Experience with Hadoop, Hive, or Pig is a "strong selling point."
In education, many university websites tend to be planted firmly in the "no" camp, but many of the articles are outdated when you consider that DS has only been a "thing" for a decade. For example, this article on Columbia University's website (from 2013) is titled "Statistics is the least important part of data science".
With today's ever changing DS realm, one could argue that statistics is one of the most important parts of data science. In 2018, it's certainly not the least important.
Georgetown university's Advice to Future Data Scientists also stands firmly in the No camp; "Write Code, Any Code." That said, further down the page the article states "Focus on what you’re good at. Not everyone is a programmer. Not everyone is a statistician...Whatever interests you, whatever talent you have, augment your assignments with that." At first, that sounds like you might be able to get away with just being a statistician. But notice the word augment in there: they are still telling you to code, code, code--and augment it with other things (like programming or statistics).
Blogger Tom Wentworth's opinion, writing for Rapid Miner: "Yes, you can do real data science without writing code. " Perhaps the most important question here is: why don't you need to be able to code to be a data scientist? Many people give solid reasons why.
