Do you agree with this? I don't, I think this Forbes article is using a provocative title to get you to read it. While assembler programmers in the seventies were eventually replaced by compilers and programming language interpreters, I believe that real statisticians and data scientists can't fully be replaced by machines or software. When they are, it results in software misuse and faulty analyses.
But data scientists do produce tools to fully automate a number of tasks, such as fraud detection, automated car driving, automated weather forecasts, and automated bidding strategies. Still, these tools require maintenance and permanent machine learning - something difficult to fully automate. Data modeling and data architecture (including identifying the right data sets and right fields) are also difficult to automate for new problems where templates don't exist. And production of automated tools itself is (sometimes, not always) difficult to outsource to a robot. That's an area where data scientists (which have many skills including engineering, computer science and business management) are very useful.
Along the same lines, do you believe that the following jobs can be replaced by tools:
Anyway, here's the article: The Data Scientist Will Be Replaced By Tools
We’ve barely started to use the term “data scientist” and the demise of this new profession is already predicted? Well, it’s not one more “rise of the machines” prophecy but instead the provocative title of a proposed panel for the upcoming SXSW.
The organizer of the panel, Scott Hendrickson of Gnip, has provided a useful run-down of some of the arguments for and against the possible disappearance of data scientists. Supporting the proposition are the current scarcity of data science talent and a slew of startups providing “data science as a service.” As an example of the opposition to the “democratization of algorithms,” Hendrickson quotes Cathy (Mathbabe) O’Neil who wroterecently that “if your model fails, you want to be able to figure out why it failed. The only way to do that is to know how it works to begin with. Even if it worked in a given situation, when you train on slightly different data you might run into something that throws it for a loop, and you’d better be able to figure out what that is.” In other words, machines will never have the deep understanding of the tools of data science that is required to practice data science.