From 441010d1873313fab021badd9b6925ebcbcb3183 Mon Sep 17 00:00:00 2001 From: Charles Iliya Krempeaux Date: Sun, 3 Dec 2023 04:58:47 -0800 Subject: [PATCH] artificial intelligence --- data scientist.html | 139 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) create mode 100644 data scientist.html diff --git a/data scientist.html b/data scientist.html new file mode 100644 index 0000000..3d4f7fa --- /dev/null +++ b/data scientist.html @@ -0,0 +1,139 @@ +
+

Data Scientist

+
+
+ by + +
+
+
+

+ A data scientist is someone who does data science. + And typically doing it while working at a company. +

+
+
+

Data Scientist Role and Title

+

+ The title of "data scientist" does not exist in-a-vacuum — does not stand alone, cut off from other influences, without links to the outside world. + "Data Scientist" is a title associated with a type of role (or roles) that companies hire for. +

+

+ Who is a data scientist‽ + Although an unsatisfying answer to some — in practice a data scientist is anyone a company gives the title of "data scientist" to. +

+

+ What types of skills and specializations are more likely to get someone hired as a data scientist‽ + It depends on the company, but — +

+

+ When working at a company, many data scientists' — +

+
    +
  • do A-B testing,
    • +
  • do fraud detection,
    • +
  • work on online advertising, or
    • +
  • create visualizations for others at the company they are working at.
    • +
+

+ Not all data scientists do this type of work — but many do. +

+
+
+

Data Analysts

+

+ In practice, the name "data scientist" at many companies was just a new name for "data analyst". + Many data analysts became data scientist after the title of data scientist became popular — continuing to do the exact same job at the exact same company just with a new newly popular title. +

+

+ This type of changing of titles is common in the tech-industry. + It is similar to the how system administrators (sysadmins) became operations (ops) specialists, and then became DevOps people, and then became site reliability engineers (SRE). +

+

+ At many companies the same people doing the same work for the same job simply had their title changed to whatever became popular at the time. +

+
+
+

Software Developers

+

+ Although "data science" and the title of "data scientist" hadn't been coined yet — in the 2000s, it was more common for some of the more technical things that data scientists do to just be part of software development. + Especially for the things that would have been called "artificial intelligence" at the time. +

+

+ However, this started to change around 2012. + Some of types of activities started to be done by people with little to no (often no) software development background. +

+

+ From a software developer point-of-view, most data scientists are 'users'. + The vast majority of data scientists will never, for example, implement an artificial neural network, or implement a training algorithm, or implement a visualization rendering algorithm, etc. + And the vast majority of data scientists will never do these things because they do not have the skills to do it. + The vast majority of data scientists use tools created by software developers. +

+

+ Most data scientists cannot program. + But some can. +

+

+ But the minority of data scientists who can program, the vast majority of data scientists who can program do not program like a software developer. + These data scientists use programming languages (such as the r programming language or the python programming language) in a way that a software developer would use bash from a computer-terminal. + I.e., most of the data scientists who can program use programming languages to just issue commands. +

+

+ For an analogy that may help make this clearer — + When a software developer writes a program, they are (metaphorically speaking) creating a robot that is expecting to run independently 24 hours a day, 7 days a week, and be able to function independently in the world. + However, when a data scientist writes a program, it is a (from a software developer's point-of-view) a very very 'hacky' tool that the data scientist would have to manually use themselves — and perhaps modify each time they use it. +

+
+
+

Physicists

+

+ There has been a type of scam that has been going on in universities with the study of physics. +

+

+ Many (probably most) people who go to and pay money for university do so for career and work related reasons. + They either want to get a higher paying job, after they graduate from university, than they would have been able to get without having gone to university. + Or they want to avoid doing physical labor, and want to be able to do a different type of job (that doesn't involve physical labor). + And they expect to do it doing whatever they studied. +

+

+ The vast majority of people who study physics at university will never get a job as a physicist. + Never. + And the people working at the universities know this! +

+

+ The people working at the universities know there are zero job prospects for the vast majority of these physicists they are graduating. + But not only do the people working at the universities not warn these students about this — but they happily take their money while they effectively letting them (and even encouraging them to) waste 4 to 10+ years of their lives getting BSc, MSc, and PhD in physics. +

+

+ What happens to these physicists‽ + In the past, some became software developers. + But since the title of data scientist got coined, many of them have become data scientists. +

+

+ In fact, so many physicists have become data scientists that they caused a culture change. + Some ways data science culture seemed t haveo changed as a result of all these physicists flooding data science is: +

+
    +
  • + the physicists who flooded data science brought credentialism to the hiring of data scientists, even making it the norm, where before it wasn't common and previously there was more focus on whether a candidate data scientist did or did not have the skills the company was looking for, +
    • +
  • + the physicists who flooded data science made it so that the skills associated with installing and operating a database became uncommon among data scientists, where before that wasn't the case — this resulted in the creation of a new role to compensate for this — the data engineer, +
    • +
  • + the physicists who flooded data science made it so that software development skills became uncommon. +
    • +
+

+
+

Universities

+

+ Once the title of data scientist became popular, many universities rushed to exploit this for financial gain. +

+

+ Many universities quickly created data science programs. + Many of these university data science programs were created by people who never actually worked at a data scientist. + And thus it was difficult for them to know what skills companies, who wanted to hire a data scientist, actually wanted data scientists they hired to have. +

+
+