
Data Science: Reality Doesn't Meet Expectations - danielfriedman
https://dfrieds.com/articles/data-science-reality-vs-expectations.html
======
hectormalot
> Moreover, you may quickly realize much of this work is repetitive and while
> time-consuming, is “easy”. In fact, most analyses involve a great deal of
> time to understand the data, clean it and organize it. You may spend a
> minimal amount of time doing the “fun” parts that data scientists think of:
> complex statistics, machine learning and experimentation with tangible
> results.

This. Universities and online challenges provide clean labeled data, and score
on model performance. The real world will provide you... “real data” and score
you (hopefully) by impact. Real data work requires much more than modeling.
Understanding the data, the business and value you create are important.

As per #6, better data and model infrastructure is crucial in keeping the time
spent on these activities manageable, but I do think they’re important parts
of the job.

I’ve seen data science teams at other companies working for years on topics
that never see production because they only saw modeling as their
responsibility. Even the best data and infrastructure in the world won’t help
if data scientists do not feel co-responsible for the realization of
measurable value for their business.

Training integrative data professionals could be a great opportunity for
bootcamps. Universities will (understandably) focus on the academically
interesting topic of models, while companies will increasingly realize they
need people with skills across the data value chain. I know I would be
interested in such profiles. :)

~~~
kqr
I took a data visualisation class in uni that handled this really cleverly.
The second assignment sounded very easy. The teacher provided links to the
sources where we could find data.

Most people figured that with such a simple assignment (not significantly
harder than the first one, which was also easy-ish) they could put off doing
it until the last moment.

Most people failed.

This real world data needed hours upon hours of cleaning before it was in any
way useable. Of course, the teacher knew this, gave bonus points to the ones
who did start in time, and then extended the deadline as he had expected to
from the start.

Never again will I underestimate the dirtiness of real world data. One of the
best teachers I had.

~~~
starpilot
This is universal to STEM degrees I think. In mechanical engineering classes
you analyze a beam, in real life you analyze an assembly with 50 components
that have undergone 100 revisions with 20 different materials and loading from
4 directions that vary with time. Oh, and you have 4 sensors to give you
information to analyze critical stresses. But one of them is broken, and Bob
who can fix it is on PTO until next Monday, so...

Internships are supposed to fill this gap but it'd be nice if all students
could get a taste of real world systems and data. For tech, maybe if they
could partner with the IT department at the school to get them exposed to
real, messy data. Maybe there are some teaching datasets with over a billion
rows that people could play around with.

~~~
mcrad
> This is universal to STEM degrees I think. In mechanical engineering classes
> you analyze a beam, in real life you ...

Hard to believe this. Don't these degrees require rigorous laboratory
assignments where the student learns to differentiate best case scenario with
real world uncertainties? STEM is not just some IT certification

~~~
Underqualified
As a mechanical engineer : No, my education didn't.

The problem is that most real world problems take too much time to really
solve to fit in any modern ciriculum.

~~~
mcrad
Hmmm. We had a whole course on measurement systems that get to the heart of
understanding that source of your data and inevitable bias/error is more
important than just crunching the data as given. For example, from a typical
four year degree.

------
throwaway713
As a research-oriented data scientist at one of the larger tech companies, I
can confirm that even here, a lot of people are unsure about what exactly data
scientists are supposed to do. My most frequent request is "tell us why metric
X dropped", to which the answer is often a subtle combination of many
different factors (often random fluctuation) that doesn't lead to a pleasing
actionable result in the sense of "here's why it dropped; go do this to fix
it".

The really interesting research type work (Bayesian modeling, convolutional
neural networks, etc.) takes a _long_ time to implement and may produce no
useful results, which is a really bad outcome at a company that measures
performance in six month units of work and highly values scheduled
deliverables and concrete impact. Many of the data scientists I work with tend
to stick to methods that are actually quite simple (e.g., logistic regression,
ARIMA) because these at least deliver _something_ quickly, despite the fact
that many of my coworkers come from research-heavy backgrounds.

In my org, there's nothing stopping anyone from pursuing advanced machine
learning; for the most part we set our own agenda (in fact, determining
priorities is part of the job role). And some people do in fact go after
state-of-the-art ML, with some really cool results to show for it. But in
terms of career progression and job safety, the risk is just way too high, at
least for me personally. I save the highly mathematical stuff for a hobby.

Edit: while this may sound a bit negative, I will add that my description of
data science isn't a complaint _per se_ ; I am mainly trying to inform those
who are seeking a career in data science of what to expect compared to what is
often promised. The work that is most valuable to a business is not exciting
all of the time, but I don't think there is another job in the tech industry
that I would find more enjoyable than my current one at the moment.

~~~
apohn
>But in terms of career progression and job safety, the risk is just way too
high, at least for me personally. I save the highly mathematical stuff for a
hobby.

I think the sad truth is that this is the reality of work no matter if you are
a Data Scientist or not. What you thought you would be doing to show your
worth and climb the ladder gets blurred in with KPIs you didn't set, politics
you didn't create, goals and deadlines you had no input into, etc. One of the
unique challenges you can face as a Data Scientist is that you may interface
with people in many different groups, all of which have different goals which
may be in conflict with each other. Compare this to other roles where you
ultimately only follow the goals of the organization you report into.

~~~
mattmanser
Sounds more like it simply doesn't work very well, rather than any of the
reasons you listed.

It's often the case, I remember when that stupid Amazon infographic was going
around about decreased load times meaning big upswings in conversions.

A client paid for a significant project to reduce load times, which we
succeeded in to a huge degree with most of the pages going from 1.5-3 seconds
secs down to 250-500 ms. Absolutely no meaningful swing in conversions at all.
I've done this a few times since, but never seen conversion move at all when
I've done performance improvements.

Nada, zilch. I honestly think it's absolute bullshit. I've always suspected
since that it was someone massaging figures in Amazon to justify their job.

~~~
natalyarostova
Load times might not effect conversion linearly. People deal with 3 second
loads until one day a competitor does .3 second loads and gives a better
experience, then in a matter of months you lose your customer base.

~~~
tomrod
I think this is where causal inference and experimental design are important.

------
resolaibohp
This article is pretty spot on. As someone who has worked in data
science/analytics for over 6 years I have found that the field is filled with
hype, managers who are not sure what data science actually is, and an absurdly
wide amount of skills jobs expect you to be able to do well.

Apply for and interviewing for data science jobs is a total nightmare. You are
competing against 100s or even 1000s of applicants for every job posting
because someone said it was one of the sexiest careers of the 21st century.
Further exacerbating this, Everyone believes that data is the new oil, and
large profit multipliers are just waiting to be discovered in this virgin data
that companies are sitting on. All that is missing is someone to run some
neural network, or deep learning algo on it to discover the insights that
nobody else can see.

The reality is that there is an army of people who know how to run these
algos. MOOC's, blogs, youtube, etc have been teaching everyone how to use
these python/R packages for years. The lucky few who get that coveted data
science job can't wait to apply these libraries to the virgin data only to
find that they have to do all kinda of data manipulating to make the algos
even work, which takes days and weeks of mundane work. Finally they find out
the data is so lacking that their deep learning model does very little in
providing actual business value. It is overly complicated, computationally
expensive, and in the back of your mind know you can get the same results
using some simple logic.

Managers who don't understand data science fundamentals learn from the news
and have their data scientist implement those buzz words so they can look good
in front of their bosses.

I think there is a place for data scientists who understand the fundamentals
of the models out there, and know when you should not use them. Data science
is also increasingly a subset of software engineering and a good data science
in a tech company should be able to code well. I also think that there is not
some huge unmet demand for data scientists. Just a huge amount of hype and
managers wanting to look good by saying they managed a data science team.

~~~
rixed
Any work is dull and depressing when done under the supervision of idiots.
Some companies, although probably less than claimed, are genuinely data driven
rather than HiPPO driven, though. This might be particularly important to look
for theses to do interesting stuff in the fields of data science.

------
wenc
Data science is correctly valued when you realize how relatively unimportant
it is. It is a small cog in a larger machinery (or at least it ought to be).

You see, decision-making involves (1) getting data, (2) summarizing and
predicting, and (3) taking action. Continuous decision-making -- the kind that
leads to impact -- involves doing this repeatedly in a principled fashion,
which means creating a system around the decision process.

For systems thinkers, this is analogous to a feedback control loop which
includes sensor measurements + filters, controllers and actuators.

(1) involves _programmers /data engineers_ who have to create/manage/monitor
data pipelines (that often break). This the sensor + filters part, which is
~40% of the system.

(2) involves _data scientists_ creating a model that guides the decision-
making process. This is the model of the controller (not even the controller
itself!), which is ~20% of the system. Having the right model is great, but as
most control engineers will tell you, even having the wrong model is not as
terrible as most people think because the feedback loop is self-correcting. A
good-enough model is all you need.

(3) involves _business /front-line people_ who actually implement decisions in
real-life. This is where impact is delivered. ~40% of the system. This is the
controller + actuator part, which makes the decisions and carries them out.

Most data scientists think their value is in creating the most accurate model
possible in Jupyter. This is nice, but in real-life not really that critical
because the feedback-loop inherently moderates the error when deployed in a
complex, stochastic environment. The right level of optimization would be to
optimize the entire decision-making control feedback loop instead of just the
small part that is "data science".

p.s. data scientists who have particularly low-impact are those who focus on
producing once-off reports (like consultant reports). Reports are rarely read,
and often forgotten. Real impact comes from continuous decision-making and
implementing actions with feedback.

Source: practicing data scientist

~~~
arborism
Had to make an account to upvote this. Absolutely dead-on. I think you can
generalize this comment to almost any specialist skill. "No Silver Bullet"
should be a business doctrine as well as a technical one. You need to do a lot
of things well to succeed in business. Specialists just provide you a
capability. You have to implement and use those capabilities as part of a
larger system if you want to create a machine that generates profit.

------
scottlocklin
> I attended a 12-week data science bootcamp in mid-2016. ...

Yeah, well there's your problem, my dude. I've been doing what might be
described as "data science" since I quit physics in 2004. Aka before the term
existed. It's a great area to work in for intelligent people who want to use
their brains to impact the real world; vastly better than what people get paid
to do in physics. If customers don't know what the tools can do, it's because
_you_ as the data scientist have failed to explain it to the customer. If your
work product isn't in front of the decision makers, you've also failed: they
can tell the bottom line impact and will reward you accordingly. Sometimes
there is no data in their data; they should know that up front.

As for whining about poor data quality: n00b. What do you think they're paying
you for? Nobody gives a shit what people do in Kaggle competitions.

~~~
chosenbreed37
I don't think the op would care much for your delivery but you make some great
points.

> If your work product isn't in front of the decision makers, you've also
> failed: they can tell the bottom line impact and will reward you
> accordingly.

This one in particular stood out. There is an aspect of salesmanship (or
navigating corporate hierarchies) to the role. Things will not be obvious to
the decision makers. Perhaps the data scientist has to take some
responsibility in bringing their work to the fore.

------
danmostudco
I stood up a data science operation at my company over the last few years, and
have noticed a key difference in data-science projects that have been
successful and those that have failed. It hits on a number of points brought
up in the article, namely where does data science "fit" in an organization
delivering software and how is the value realized by the business.

The worst cases I have seen is when executives take a problem and ask data
scientists to "do some of that data science" on the problem, looking for
trends, patterns, automating workflows, making recommendations, etc. This is
high-level pie in the sky stuff that works well in pitch meetings and client
meetings, but when it comes down to brass tacks this leaves very little vision
of what is trying to be achieved and even less on a viable execution path.

More successful deployments have had a few items in common

1\. A reasonably solid understanding of what the data could and couldn't do.
What can we actually expect our data to achieve? What does it do well? What
does it do poorly? Will we need to add other data sets? Propagate new data?
How will we get or generate that data?

2\. The business case or user problem was understood up front. In our most
successful project, we saw users continuously miscategorized items on input
and built a model to make recommendations. It greatly improved the efficacy of
our ingested user data.

3\. Break it into small chunks and wins. Promising a mega-model that will do
all the things is never a good way to deliver aspirational data goals. Little
model wins were celebrated regularly and we found homes and utility for those
wins in our codebase along the way.

4\. Make is accessible to other members of the company. We always ensure our
models have an API that can be accessed by any other services in our
ecosystem, so other feature teams can tap into data science work. There's a
big difference between "I can run this model on my computer, let me output the
results" and "this model can be called anywhere at any time."

While not exhaustive, a few solid fundamentals like the above I think align
data science capabilities to business objectives and let the organization get
"smarter" as time goes on as to what is possible and not possible.

~~~
kavalg
As a person doing data science / ML in the last 4 years, I mostly agree with
your points. Especially about the hype driven demand for DS/ML. One thing that
is often neglected though is the exploration part it. There really is a lot of
data out/in there that your company knows anything about, but can probably
benefit from knowing. E.g. even a simple crawl of a popular jobs/ads/... site
done diligently for e.g. 6 months can reveal many interesting insights about
market structure and trends. Google and its mission to organize all data in
the world exist for a reason. This however is in stark contrast with the
approach that most executives take. Instead of managing it as a well thought
strategic/long term investment, they want to time-box it, to get immediate
value and to show off to senior management or customers. I've seen this
tendency in both big corporations (mid-level management) and startups, which
makes me think that the confounding variable is the fund/incentive management
process. In both big corps and startups, there is a limited time&budget to
show meaningful results and people optimize for that, which often involves
taking shortcuts, neglecting strategy and outright lying. In contrast to that,
I've seen projects driven by wealthy individuals, who don't look for immediate
value, but are scratching an itch (e.g. curiosity). These usually fare better
than the former as long as budgets don't get out of hand (to exhaust the cash
cow). I would argue that these are most successful, because of better
alignment of motivation (person paying the bill) and execution (person driving
the process).

------
kristjansson
Teams being small, data being crummy, infra being hard, and yet expectations
being high aren't so much complaints as the they are the job description.

The point of data scientists and the related roles listed in the article are
not to just churn out the fun stuff, but to wade through the institutional and
technical muck and mire it takes to bring the fun stuff to bear on a relevant
business problem and to communicate the results in a way that people of all
walks can understand.

~~~
tqi
Yeah this guy seems to think Data Science work should be like doing a problem
set for CS class. Sorry that you have to deal with messy data, fragile infra,
and limited resources - I know it's not "fun", but frankly that's what the
money is for.

~~~
nerdponx
That's the whole point of the article. Expectation (in this case, his own
coming out of the bootcamp) vs. reality (what data science is actually like).

------
Barrin92
I'm generally confused by the hype around ML and 'data science'. it seems like
CS has somehow regressed to the behavourism era of psychology or economics
before the Lucas critique.

The problem with all this data talk isn't just about implementation or bad
structure, the limitations of putting all your bets on inductive reasoning are
systemic.

The insights that economists had in the 70s and 80s was that reasoning from
aggregated quantities is extremely limited. Without understanding at a
structural level the generators of your data, trying to create policy based on
outputs is like trying to reason about inhabitants of a city by looking at
light pollution from the sky.

My guess why data science so rarely delivers what it promises is because you
can't get any value from historical data if your circumstances change to the
point where past data is irrelevant. Which in the world of business happens
pretty quickly. To have a competitive advantage, one needs to figure out _what
has not been seen yet_.

And trying to exploit signals suffers from the issue laid out above. There was
a funny case of an AI hiring startup trying to predict good applicants, and
the result was people putting "Oxford" in their application in a font matching
the background color

~~~
itsmefaz
> CS has somehow regressed to the behavourism era of psychology or economics
> before the Lucas critique?

Can you please elaborate on this please?

~~~
antonvs
See:
[https://en.m.wikipedia.org/wiki/Lucas_critique](https://en.m.wikipedia.org/wiki/Lucas_critique)

At a high level, it argued that basing predictions on historical data is
problematic. The details of the argument are somewhat specific to economics,
but the principle is more general. That's also why people recommending stocks
say "past performance is no guarantee of future results."

One of the key issues is that circumstances change, and information about such
changes will often be external to a data set.

In the Lucas critique, policy changes are an example of this. You can't
predict future economic performance based on past economic performance if
relevant policies have changed. But any complex situation has such factors
that are external to the data that one can easily collect about it.

------
analog31
As a scientist, I've worked with data for decades. There's always been a
prevailing belief that scientists and engineers with specialized domain
knowledge are mostly fumbling in the dark and can be replaced with a general
purpose technique.

This was certainly the vibe that I got from "design of experiments" when it
was the statistical method _du jour_. Then from "Bayesian everything" and now
"data science." I remember "design of experiments" studies being conducted
with great fanfare and success theater, while producing zero results.

The long term theme is that science is hard for reasons that managers don't
understand, can't manage, and are reluctant to reward.

------
rafiki6
I've seen a few similar articles now. Does this represent the general view of
folks working in data science? "Data Science" is such as meaningless catch all
term. The reality is in many organizations it's simply advanced business
intelligence or advanced business analytics. There are some industries that
lend themselves well to this whole practice, and they tend to be industries
that have been borne out of the internet age (e.g. social media, internet
advertising, etc.)

Some other industries have been doing "data science" for ages. Credit Risk
Modelling, insurance and so on.

Every time I read one of these articles, I feel it's just an individual who
entered a kind of crummy situation and they're learning what it means to work
in a corporate environment. Some are better than others. Some are more
motivated than others. Some have better cultures than others. Some are more
willing to make technology a key part of their business strategy. Some are
more data driven than others.

My recommendation is to always ask the fundamental question before joining:
what are you trying to achieve with data science, and is it actually
achievable?

~~~
smeeth
I always thought the non-specificity of the term Data Science was a strange
criticism for those in the tech industry to make. How many types of SWE are
there? Front-end, back-end, full-stack, devops, security, QA...

I agree wholeheartedly with your recommendation. Like any other job, each
company has different needs and expectations and if you want something else
out of the role you'd best avoid that company.

~~~
rafiki6
Frankly I have the same criticism of those who use the term software engineer.
Engineering is a pretty established profession with a set of standards, ethics
and practices. Most of us who work in software are not engineers. We are
developers. Similarly, a scientist is one who follows the scientific method to
do research. So by that logic a data scientist should be a person who uses the
scientific method to do research on data. Does that make any sense? And let's
be serious, is that what most data scientists are being hired to do?

~~~
mr_toad
> the scientific method to do research on data

Exploratory data analysis is often overlooked and underrated.

~~~
FridgeSeal
Ppphhh we don’t need to do exploratory data analysis or prepare the days,
don’t you know that neural networks will do all that themselves!

Doesn’t yield the right results? Clearly not enough data.

Still doesn’t work? Change to whatever the latest model google or fb is using
and try again.

/s

~~~
beckingz
And the model will train itself right? That means that you'll have all that
empty time to do more data science!

/s

------
Optimal_Persona
As a data dude in public/nonprofit healthcare-landia I agree with all this,
plus:

\- It's essential to have/develop domain expertise in your industry.

\- Beware plausible, but incorrect (or poorly interpreted) data that supports
yours (or others') assumptions/biases.

\- Add on to #4 - at least as bad as this is having well-intentioned people on
your team who "know enough (a bit of SQL or low/no-code data tool") to be
dangerous. Um, why are you joining unnecessary tables, or using a different
alias for the same columns/tables in different queries, with no comments or
standard formatting?

\- Hold your nose, but anything you do in SQL/R/Python/even fancier
programming tool/language is going to pass through MS Excel at least once
sooner or later which can irreversibly bastardize CSVs (even just opening
without saving!), truncate precision to 15 digits, change data types, etc.

\- So glad for the callout in #7 - there are clearly devs/data folks out there
who are happy to take on an "interesting programming project at a great paying
job" \- that isn't serving the best interests of humanity.

------
Icathian
This rings very true to me. I'm working on moving over to an SWE role in the
next few years for many of these reasons.

I'll just add one: the business absolutely doesn't care how you get your
answer, only if they're reliable enough (hand grenade close is better than
most companies have today).

While this seems obvious enough to anyone with a few years under their belt,
to the new DS grad who has their time series analysis canned in favor of
slapping a simple moving average in place and shipping it can be rather
disillusioning.

~~~
Der_Einzige
Nothing reliably consistently beats ARIMA models in time series forecasting
_to this day_

That's pretty sad when you think about it, but it's painfully true.

~~~
wenc
> Nothing reliably consistently beats ARIMA models in time series forecasting
> to this day

Not sure this is true in practice. In some situations, Holt-Winters (ie.
algorithms in the ETS family) may do better, and it's often a good idea to try
both.

There's a claim that Holt-Winters is a special case of ARIMA (the claim is
ARIMA is more general), but this is actually not the case. There is
equivalence in only a subset of cases. [1]

I've fitted Holt-Winters models that beat ARIMA models. ARIMA models can have
trouble generalizing from training data with long horizons because they tend
to overfit to the distant past. Holt-Winters on the other hand has a natural
"forgetting factor" built-in which moderates this.

As well, my experience is that stacked models with well-chosen exogeneous
variables (if you have causal variables) tend to outperform pure time-series
methods because they are anchored on more independent variables than just t.
Pure time-series models bank on the assumption that patterns have a repeatable
time-dependence, and most of the time this is just not true, so have to be
augmented with other variables.

[1] [https://otexts.com/fpp2/arima-ets.html](https://otexts.com/fpp2/arima-
ets.html)

------
Vaslo
I’ve been doing an MS in Data Science very slowly due to work and 2 new kids.
Finishing the degree this year in year 4. I was very excited about the
prospect of doing something different. A few things have changed for me.

1). I am hearing about Data Science Teams being furloughed during these times.
That isn’t happening in my function (Corporate Finance). I am glad to be
secure even though I enjoy much of the data sci work.

2) I’m able to apply Data Science concepts in my current role, and it’s adding
a lot of job security and providing me with exposure. I am much less
interested now in moving to straight Data Science and instead am applying my
learnings in my current role as a sort of in-house Data Science guy. But I
have a lot to learn to be honest.

3). There seem to be a lot of “thought leaders” acting like they are big
experts in the area and really don’t know anything many of us amateur
scientists don’t know. They pull perfect clean datasets and show these magic
transformations they just copy from others to get YouTube hits or Twitter
followers. That just never happens in real life, and many leaders are seeing
this and losing interest in this function in the returns they are getting from
sole data science folks.

~~~
RayVR
This isn’t unique to data science. I personally know people in finance that
are poor coders and even worse quants, yet they go around lecturing at
universities.

------
s1t5
I work as a data scientist. Some of the author's points are workplace-
specific: lack of leadership, being the only data person, ethical concerns.
The others are just aspects of the job - communicating about your job and
impact, dealing with vague specs or managing low-quality datasets.

Neither of those quite match the articles title, perhaps it just refers to the
author's personal expectations. Neither of them seem that specific to data
science, or without parallels in other software jobs. And neither of the
points read like a slight towards data science to me, like some of the other
commenters here suggest.

------
UweSchmidt
One issue might be that organizations subconsciously resist the data
scientist, or more generally, the nerd in his/her attempt to take over
decisions. If these decisions are invariably tied to the goals and careers of
managers, how can the data scientist have a "seat at the table" in all but the
most enlightened and technical companies? The disorganized state of data and
infrastructure suits the ambitious manager well, who can just put in enough
effort to find data to have their project greenlightened or to answer one
specific question.

Progress may only come slowly, ideally through products bought from 3rd
parties whose results are understood and controlled by management.

------
mirimir
I did "data science" for about a decade, consulting with plaintiffs firms and
state AGs on antitrust and fraud cases. For each case, the work flow was
roughly this:

\-- write discovery requests

\-- review production, and check out data and documentation

\-- write supplementary discovery requests

\-- review production, and check out data and documentation

[repeat as needed]

\-- analyze data, and write deposition questions

\-- help attorneys wring answers from deponents

[repeat as needed]

\-- analyze data, and produce required output

\-- write parts of briefs and expert reports

I generally did that in consultation with testimonial experts and their data
analysts. Sometimes that didn't happen until we'd documented the case enough
to know that it was worth it. And occasionally small cases settled with just
me as the "expert".

It's a small industry, and not easy to get into, unless you know key players
at key firms. But the money's pretty good, and the work can be exciting. I
loved being that guy in depositions whispering questions to the attorneys :)

This all involved pretty simple calculation of damages, through comparing what
actually happened vs what would have happened but for the illegal behavior.
But-for models were typically based on benchmarks.

After data cleanup in UltraEdit, I did most of the analysis in SQL Server. I
used Excel for charting and final calculations.

~~~
kevin_thibedeau
I would expect "data science" is doing some form of numerical analysis.
Otherwise it's just record keeping... with computers.

~~~
beckingz
Record keeping is 90% of data projects.

The second 90% is basic math at high speeds.

~~~
mirimir
Right, record keeping. But when it's not your data, things get complicated.
Imagine trying to understand how another firm's data systems work. You can
talk with managers, who know how the business uses data. But they have no clue
how the data are stored or managed. And you can talk with IT people, who know
how data are stored or managed. But they have no clue how the data are used.

And yes, speed. Aggregating hundreds of gigabytes was nontrivial to do
quickly. I started with Access, and then learned to manage and use SQL Server.
And eventually a multi-Xeon server with lots of RAM and SAS-attached storage.

------
avip
This reads like Indiana Jones teaching Archeology. Yes, as a data-scientist
you actually have to work, most of the work is digging in dirt, and mostly you
won't find anything of interest.

------
op03
It works well when subject matter experts exist in the org and
collaborate/supervise/drive data folk, to solve some issue the sme's have
spent enough of their own time thinking about.

If its just data folk by themselves getting dumped with org data and told to
find pirate gold...then its a crap shoot.

------
codingslave
The real issue with data science, from the perspective of ML pipelines/using
ml in products, is most people are straight up not smart enough for it. The
second the problem falls outside the bounds of a commonly used model, 90% of
data scientists are ill equipped to come up with a profitable solution. So
they stumble around in the dark, producing nothing of real value. People
underestimate the degree to which extreme mathematical maturity and skill can
bend the results of commonly used ml models.

------
agentofoblivion
This reads as a series of bad job experiences and I think is explained by a
wide variety of job functions that all can have "Data Scientist" as a title.
Someone else's experience could be totally different. You have to know what to
look for and what to avoid. If you're trying to find a DS job, one of your top
priorities is finding out what the actual job consists of. For instance, a
Data Scientist at Facebook might be called a Data Analyst at many other places
--no modeling required.

I know this because I've been on that journey. But there's no reason to expect
some department head that's never been exposed to DS to know this. They just
copy/paste some other company's job req. If you're more junior, here are my
tips:

\- If it's a "new DS team" that supports a variety of teams: beware. Bolt-on
DS doesn't work well, as it's really hard to build a meaningful solution
that's not deeply integrated.

\- If it's an old company or in a conservative industry: beware. There are
likely to be data silos and difficult ownership models that make it nearly
impossible to get and join the data you need.

\- If it's a small company: beware. You're likely going to need a broad set of
knowledge that's won with several years of experience to be able to build end-
to-end solutions that are integrated into the rest of the tech stack.

\- If it's not an engineering-driven culture: beware. DS will often be used to
provide evidence to someone else whose already made up their mind and pretend
they're being data-driven, and you'll be the disrespected nerd that's expected
to do what it takes to deliver the answer they want. Most companies claim to
be "data-driven", few are, and even fewer understand data-driven isn't always
possible or desirable.

Industry is still trying to figure out how to use ML and are still learning
that it's not as easy as hiring someone that knows about all the algorithms,
but rather it takes deep technological changes to data infrastructure to
enable the datasets that can then be used by the ML experts. But you don't
have to be the person that helps them figure this out the hard way (i.e. by
being paid to not accomplish much due to problems outside of your control).
Better to find a place with a healthy data science team that can help you
learn and contribute. They exist.

~~~
antipaul
Agree with your points on "old company/conservative industry" and "non-
engineering culture"

I'm at a place that is both, and both are huge pains.

On the engineering side, it's a bit different though: technical roles are
looked down on, and there is no engineering culture, eg, for data. Data is
just a bunch of flat files everywhere, across many silos. No leadership to put
it together into logical buckets for easy access and interoperability

------
deppp
Great read. A lot of those problems are real, and some of those I’ve
experienced myself. But I think at least some of them are related to the
immaturity of the field. We’re only at the beginning of creating the tools and
platforms to facilitate DS, making it more reproducible and easier to measure.

For example, I’m working on the tool to make data management easier and
convert datasets into a structured representation. If you have experienced
that you spend a lot of time on preparing and analyzing data, and it is
tedious, please reach out to me michael at heartex.net, would love to get your
feedback on the product we have built so far.

~~~
leto_ii
> But I think at least some of them are related to the immaturity of the
> field.

I agree. More so, I sometimes feel that in the end the field will break up
once things start settling down. Some roles will migrate more towards
engineering, some will go back towards data analysis.

The expectation that a data scientist is a funnel that can turn anything into
magical insights and tools can't last forever.

------
AndrewKemendo
A really easy way that I try to explain things to people is like this:

You can't compress information until you have it in a format that is
appropriate for compression.

That is:

You can't compress (apply/create algorithms) information (data) until you have
it (instrumented data collection) in a format (schema) that is appropriate for
efficient compression (structured logging/cleaning).

99% of that is Data Engineering and building good engineering practices which
have good data practices as a priority.

For any organization that has more than a handful of employees and more than
one product, that is a non trivial task and gets more difficult the larger the
organization gets.

~~~
antipaul
Totally agree. Non-tech companies that think they need "data science" should
instead put same effort into (data) engineering.

It's not quite 99% of the effort but close enough ;)

Search "data science hierarchy of needs"

------
minimaxir
I wrote a blog post along similar lines in 2018
([https://minimaxir.com/2018/10/data-science-
protips/](https://minimaxir.com/2018/10/data-science-protips/) );
unfortunately, the industry hasn't changed much since then.

As noted in the submission, there's a lot of flexibility in what a "data
scientist" is. Normally that's good and healthy for the industry. However, it
contradicts a lot of optimistic bootcamps/Medium/YouTube videos, and many
won't be prepared for the difference.

~~~
freehunter
My industry (information security) is the same way. Far too broad of a
category, leading a lot of people to get confused and frustrated. I see a lot
of analysts (defensive security) who thought they were going to be pen testers
(offensive security) and didn't realize those jobs were in two completely
separate career paths.

------
stuxnet79
I've been a data person for the past year and a half and I'm very disappointed
with the bewildering array of titles out there and the rather vague meanings
behind them (Data Analyst, Data Scientist, Data Engineer, ML Engineer).

It's overall hurting my ability to build my personal brand and seek roles that
are a fit for my existing skillset and aspirations.

What exactly does 'ML Engineer' communicate to employers in terms of baseline
skills? Is the role closer to that of a data engineer or an analyst?

~~~
dtjones
I've been working in data roles for 10 years and hold a masters in ML. I've
hired and managed each of the roles you mentioned. I think of the
responsibilities of each of those roles as:

-ML Engineers as building software infrastructure to scale machine learning inference and training.

-Data engineers focusing on data infrastructure and pipelining into either model inference, training, or other business intelligence platforms

-Analysts consume the product of the data engineer in the BI platform or excel, where the results would be consumed as a report in some form.

-And ML Researchers would be those inventing novel machine learning algorithms to deploy in the ML Infrastructure managed by the ML Engineers

-And data scientists to deploy well-known ML algorithms or statistical inference on varying datasets on the ML Infrascturue or as a slide deck.

~~~
beckingz
How hard could it be to find one person who can do all that?

~~~
aeyes
Depends on the amount of data, reports, pipelines... If the company is small
you might not have any of these problems. Every Mom&Pop store has some sort of
data to run the business but they don't need a "data" person.

Once you have 10s of datastores + pipelines, 100s of reports and a "data lake"
in the TBs you'll likely be needing specialized people.

~~~
stuxnet79
So far I've spent my career in small teams / startups and it's starting to
become apparent that a lot of what's assumed in these titles only applies in
larger corporations where resources are abundant and it makes business sense
to have a specialist focused on a single aspect.

Unfortunately I'm at a point where I have 'jack of all trades master of none'
syndrome and it's causing me to fall in between the cracks professionally. I'd
like to move to a larger company where I can develop deep expertise in a
narrow topic.

~~~
proverbialbunny
ymmv, but as a data scientist at young startups, I often am the one giving new
tasks to the software engineers, and facilitate teaching and training if they
need help.

Most of those roles a software engineer can do.

------
arafa
From my perspective as a data person, everything on this list is true. I would
add on to #4 to say "You're likely the only data person" to say "You're likely
the only data person and expected to do everything you need to do your job
yourself" (from sourcing the data to deploying your model).

High Data Scientist salaries and expectations combined with a shortage of
qualified people often mean you're expected to be a one-person band, which I
find to be miserable.

------
ajeet_dhaliwal
Point 5, “ Your impact is tough to measure” is also shared by Quality
Engineering and SRE, and not unique to Data Science. The point about being a
support role holds true for them and it is thoroughly frustrating when a
front-end dev makes a small change to a visual element is praised to the roof
while complex automation projects by the quality team, ingenious recovery and
reliability projects by SRE, and massive and fascinating inferences by data
science are undervalued by leadership. The truth is most leaders just can’t
connect the dots. I’ve worked as a full stack engineer btw do not taking a dig
at front-end work, but it’s clearly easier to measure impact. I’ve worked in
quality too and when you’re only called in to ask why one bug got out and
never asked about the thousands you’ve stopped it’s demoralizing. It’s part of
the reason I started Tesults
([https://www.tesults.com](https://www.tesults.com)), if you’re in one of
these support roles, measure, measure and measure and throw those reports into
the faces of leadership. It shouldn’t have to be done but without it, the
point the author is making here will take place.

~~~
FridgeSeal
> and it is thoroughly frustrating when a front-end dev makes a small change
> to a visual element is praised to the roof while complex automation projects
> by the quality team, ingenious recovery and reliability projects by SRE, and
> massive and fascinating inferences by data science are undervalued by
> leadership.

I feel this in my bones lol.

The frustration when the results of weeks/months of hard work are glossed over
with a “oh that’s nice” in favour of endless praise for the front end team
putting a picture backdrop on the search page or something.

Didn’t matter how many times we sold them on the benefits, or explained the
work that went into it (at both executive summary level and detail) or did all
those things you’re supposed to do, if it was more than one step away from
directly causing it, or slightly more abstract than “we moved the button” it
was wasted on leadership/management.

Spent a couple of weeks fixing data pipelines and ETL/database infrastructure
and processes and now everything runs faster, and runs on a smaller and
cheaper cluster and as a result managed to put together some analysis and
modelling on customer behaviour that shows if you do xyz you’d expect to see
uptick in this thing. Doesn’t matter, Bob changed where the button sits and we
saw 20% more sales, good job Bob, everyone: be more like Bob.

------
smitty1e
Coase: "If you torture the data long enough, it will confess to anything."

via
[https://www.reddit.com/r/QuotesPorn/comments/b76ujr/if_you_t...](https://www.reddit.com/r/QuotesPorn/comments/b76ujr/if_you_torture_the_data_long_enough_it_will/)

------
eanzenberg
I guess I'm in the minority in these threads..? I've been doing machine
learning / model-building / pushing models to prod and maintaining for about 6
years now. It's still 50/50 understanding the data and
building/tweaking/training/testing models. But it sounds like most people with
this title are analysts? At least that's what posts and threads lead me to
believe. I've also met a lot of people with titles like "ML Engineer" or "Data
Scientist" who don't do machine learning. They are analysts, engineers, or
maintaining data pipelines.

~~~
proverbialbunny
Pushing models to prod is often MLE work depending on the organization, though
MLE is often slang like how dev is slang. MLE an be a job title just as
Developer can be a job title, but more common than not the job title is
Machine Learning Software Engineer, or just Software Engineer for short.

I suspect a lot of people want the sexy Data Science job title, which is why
there has been such a push for it, and why most new "data scientists" take the
title but do Data Engineer / Infrastructure Software Engineer work or MLE work
instead.

I think MLE is more sexy in a lot of ways, and it often pays better than DS
work, so it's odd that many haven't flocked to that job title, but maybe the
whole software engineer part turns people off for some sort of reason.

Me, I'm more a classic data scientist / research engineer, which involves a
lot of digging through data and research and generalized learning, then
presenting my findings. I'm not using any ML on the job right now, but often I
have in the past. It's just a tool, not an end.

------
FridgeSeal
> You’re likely the only “data person... Because people don’t know what data
> science does, you may have to support yourself with work in devops, software
> engineering, data engineering, etc.

Nothing has summed up my entire working experience more than this, it’s almost
painfully accurate.

On one hand it’s an exciting challenge, you learn a lot and you get good at
adapting to these situations.

On the downside I have practically no senior data science people to turn to
for help when I do need it, which is frustrating.

------
mikorym
I am sorry to sound like I am being obstinate, but my opinion about this is
that as a society, since the early 90s, we have put way to much focus on
"tech" than we have put on plain old mathematics or foundational science.

I don't mean manufacturing (which is doing really well), but companies like
Microsoft, Google, Facebook (and even Apple) and others do encourage you to
try to compete against their founders (or maybe society does that) rather than
focusing on being solid mathematically. Yes, Google pays people well with
those skills, but movies portray mostly their founders, emphasising how rich
they are, while mathematicians are generally portrayed as weird. Society as a
whole puts more emphasis on Bill Gates than on fundamental researchers.

In fact, if you really want to have a rich representative, you can pick the
Simons guy. (See, I don't even know his name.) His Medallion hedge fund was
built on mathematics. Ironically, Bill Gates is these days one of the biggest
financial supporters of people with science skills that he doesn't have.

It is a fad to be a techie. Mathematics is not a fad, although it does have
internal fads.

------
worik
I do not understand. Have never understood. "Data Science" is, surly,
newspeak. The appropriate term, surly, is "statistics".

~~~
_jal
A new title means a new opportunity to ask for more money and influence. See
also, "devops".

~~~
beckingz
Going from IT to devops is a great way to double your salary.

------
erdos4d
I worked as a data scientist for 4 months at a VC firm. I have a PhD and
thought the work might be legit when I was hired. After the 4 months I quit
when it became apparent that my credentials were being used for managerial
intrigue and the work was essentially a joke, with no rigor at all. This
article hits the nail on the head, unfortunately, these positions are not
often real jobs.

------
johndoe42377
There is a philosophical principle which says that any model superimposed on
reality could be seen as reality itself, while it is merely a superimposed
interpretation, in principle.

Korzybski formulated these principles, among other things.

Most of data science models are as wrong as astrology and numerology. They
have no connection to reality, or rather inadequate.

This principle explains abysmal failures of all Model-based "sciences",
stating from financial markets and up to virus spreading models.

Simulations of non-discrete, non-fully-observable (AI terminology) system has
exactly the same relationships with underlying reality as a Disney cartoon to
a real world.

This is why expectations will never be meet, except for natural (non-
inaginary) pattern recognition.

A drop of proper philosophy worth years of virtue signalling.

------
zeveb
> internally, you can make inroads supporting stakeholders with evidence for
> their decisions!

The problem is that this can all too easily become motivated reasoning: one
provides a stakeholder with support for the decision he already made. From his
point of view, this is a valuable service, but it does the _organisation_ a
disservice: decisions should be made after considering the data, rather than
consider only those data which support a decision.

Also, while ethical issues certainly arise, I think that Greyball is not a
good example. Uber evading police enforcing the taxi monopoly is no more
unethical than the Underground Railroad evading fugitive-slave agents. The
taxi monopoly is itself unethical, and evading it increases the common good.

------
sgt101
Disclaimer: I use the term Data Scientist throughout this post; however,
popular titles such as Data Analyst, Data Engineers and BI analyst are
randomly applied by people who know nothing, and these people share none of
the responsibilities of a Data Scientist.

I have never had hopes about the potential impact of being a Data Scientist. I
felt every company should be a “data company”, but everything I knew told me
that companies are political institutions bounded by the pressures of late
stage capitalism. Anyone who things different is dim, anyone who blogs about
it is a moron.

My expectations did meet reality.

Where did my expectations come from?

I attended a four year Computer Science degree, followed by four and a half
years of earning a Ph.D. I then spent 20 years in industry. 19 of the 20
weeks’ focus were not on machine learning (ML) and artificial intelligence
(AI).

I figured I’d spend most of my time buried in code and data, I was right, I
had to find shit buried in it, and dig it out with my teeth. Executives hated
me because I was a threat, but they needed me so I continued to get paid. I
continue to be able to create insight and predictions that almost no one else
can, and until this stops I will get a 200k a year salary, benefits and a
Tesla.

All of this happened, I can't be bothered to waste my time commenting on this
moronic blog post.

~~~
vasili111
How important is Ph.D for data science?

~~~
sgt101
I think it's quite important - or an equivalent.

From about 2012 to 2018 I went round a lot of universities, conferences and
companies doing presentations and I used to often ask the audience for a
definition of data science (in the hope of getting a good one). The best one I
heard came at the University of Bath where someone (I know who, but he didn't
say it to back it with his reputation so it's not fair to name him - it wasn't
me though) said "Just drop the data, it's science".

I totally think that - Data Science is about doing Science with found and
evolving data sources, we aren't often able to construct our experiments from
scratch, but we often get to augment them, but we always start from the data
we are given - which is why it's a sub-field.

In any case - the Ph.D's I have employed have almost all known how to do
Science, and it has really helped. Some people without a Ph.D. learn to do it.
Experimental Ph.D's are best.

Maths and theoretical Physics Ph.D's are generally not able to do this!

~~~
vasili111
Will you employ data scientist that have articles and years of expirience as
data analyst in University but not Ph.D? How much role will be lack of Ph.D in
this case?

~~~
sgt101
As I said - if the person is capable of independent scientific investigation
then I think they'd be good. I think that a Ph.D is formal training for that -
but not the only way to learn.

------
Ididntdothis
Work reality rarely meets expectations. I am sure a lot of “UX” people also
got a little disappointed that reality is much more mundane than the fancy
title suggests. Or the people whose last algorithm work was the interview.

------
DrNuke
It really depends on the niche or the industry considered, though: I can
happily say that I can do materials informatics from my basement at home now
and much faster and better than as a cog in any lab anywhere in the world.
Same for a great number of STEM applications, if you ask or follow high-level
practitioners through conferences, journals and social media. The elephant in
the room is the Intellectual Property generated through STEM applied data
science, which is hot and even dangerous as you can see from superstars like
OpenAI, DeepMind or politically-motivated aggregations.

------
kovac
The most common complaint I've heard from the data science team is that there
isn't enough data to work with.

I'm not fully convinced that data science with ML and more modern techniques
are applicable across domains out of the box. I think there is value to be
added if data scientists can specialise in domains.

If we take humans as an analogy, even with the kind of general intelligence we
have, we need domain expertise to be able to have advanced intuitions and make
predictions about the future. I believe this is true for data science as well.

------
starchild_3001
I was a data scientist for one year. Experienced many of the adverse
situations explained in the article, plus I thought it isn't for me. I joined
my next job as a software engineer (after an extensive interview prep).
Couldn't be happier. Still doing plenty of data science. But my product is
actually a product, not the analysis (as is often the case with DS). I feel
"more central" to the project, to the company. I'm still building ML models,
features etc for a living.

------
StonyRhetoric
As the lead data scientist at a small-ish fintech, I can confirm many of the
frustrations and disappointments in the OP. But my trajectory was slightly
different - from being the only "data science guy" in 2016, to now leading an
autonomous team of four, with quarterly meetings with the CEO, and monthly
meetings with our tech leadership. I decide tech stack, workflow, and hiring.
Execs decide priorities. Sure, some of it was dumb luck, some of it was
actually having a CEO that cares about data strategy, but I like to think at
least some of it was me.

So here's what I think I did right:

1\. Provide indisputable, obvious business value every month. You should
consider yourself an in-house consultant to whichever cost center your salary
is drawn from. If you're product development, prove value to them. If you're
operations, or sales, or marketing, prove value to them. After about two
months, you should be able to justify your existence in two sentences. Just
remember, most of your company probably thinks of you as a optional add-on.

Your first few projects should attack high-impact pain points with the
simplest solutions possible. My first projects were basically ETL into some
basic regression into a dashboard. No machine learning required. But it was
better then what they had (which was often nothing), and it was STABLE and
RELIABLE. And that leads to the next point...

2\. Build trust. With my dead-simple models, nothing ever blew up, there were
no nonsensical answers, and there wasn't much brittleness when new categorical
features or more cardinality was added. It mostly just worked. And that built
my reputation for me. They didn't have to understand what was going on in the
model, but they knew, from experience, that they could trust the result. Once
I had the credibility, I could start building more complex, more elaborate
models, and asked them to trust those as well. If they don't trust your
models, then no business value has been created, and your job is worthless.

3\. Recognize that data science is being done everywhere in the organization,
and respect it. Every department has someone who has built a monster
spreadsheet that contains more embedded domain knowledge then you could hope
to learn in a month. As data scientists, we like to think that we're helping
the organization by building critical metrics to improve performance. But
here's the catch. If the metric was truly critical, someone has built it
already. It might be ad-hoc, use poor-methodology, and be somewhat wrong, but
it works and is good enough. You have to find that person, learn from them,
and improve on it.

4\. Be as self-contained as possible. Ideally, your critical path should not
depend on other teams doing things for you (except for IT setting up data
access). You should be able to do it all. From front-end dashboards, to ETL,
to DevOps. Remember, you're an in-house consultancy. You should be able to
take problems and just handle them, rather then be a perpetual bother and
distraction to other teams.

There's more, but if you do these four things, I think you can build the
reputation in your company for creating useful, accurate data tools that help
other people do their jobs better. After that's achieved, people will breaking
down your door to get your help. That's where my team is now - we've got a
backlog for at least 18 months, with our work priorities often being set
directly by the CEO.

------
simonkafan
My feeling is that a lot of companies think: "We need a data scientist because
all the big players also have one!"

In fact, they actually don't need a data scientist. At best they need someone
who cleans data, creates pie charts or even worse, they relabel the database
admin job as "Data scientist".

------
Rainymood
Can definitely relate to this. Work for big consulting firm (F500) as a data
scientist, end up in this weird software engineer/ml engineer hybrid role.

I personally love it but am doing more pure software engineering now as the
infrastructure is not there and I need to build it myself.

------
alixedi
To point #5 in the article, in my experience, ascending order of potential to
generate value for business:

An astonishingly large fraction of Data Science output goes to die in pretty
presentations.

From what's left, a large fraction ends up in Spreadsheets.

A disappointingly small fraction ends up in live services.

------
new_learner
Completely agree. I made a post couple of weeks ago trying to find some
solutions for this:
[https://news.ycombinator.com/item?id=22673236](https://news.ycombinator.com/item?id=22673236)

------
dzonga
it's a problem with tech in general. some things come over-hyped. and in the
process people forget what's the actual problem to be solved because they fell
in love with tools | tech. maybe the solution could easily be done in excel
but then that's not sexy. I personally prefer to handle most parts in Python
because of automation. writing functions in python is easier than writing
functions in SQL or Excel(macros)

------
mjparrott
Maybe I have a narrow set of experience, but in my mind a “data engineer” is
not a substitute for a “data scientist”.

------
graycat
The situation sounds similar to ones years ago for statistics, operations
research, optimization, and management science.

I view all of such work as applied math.

My experience is that applied math, from the fields I mentioned and some more
recent ones, and more, with emphasis on the more, can be valuable and result
in attention, usage, and maybe money.

I've had such good results and have seen more by others.

Some examples:

(1) Airline fleet scheduling and crew scheduling long were important, taken
seriously, pursued heavily, with results visible and wanted all the way up to
the C-suite.

(2) Similarly for optimization for operating oil refineries: So, here is the
inventory of the crude oil inputs and the prices of the possible outputs. Now
what outputs to make? The first cut, decades ago, was linear programming, and
IBM sold some big blue boxes for that. More recently the work has been
nonlinear programming.

(3) The rumors are, and I believe some of them, that linear programming is
just accepted, used everyday, in mixing animal feed.

No surprise and common enough, IMHO what really talks is money. If can save
significant bucks and clearly demonstrate that, then can be taken seriously.

But from 50,000 feet up, tough to get rich saving money for others. If they
have a $100 million project and you save them $10 million, then maybe you will
get a raise.

What's better, quite generally in US careers, is to start, own, and run a
successful business. If that business is to supply the results of some applied
math, and the results pass the KFC test, "finger lick'n good", then charge
what the work is worth.

Maybe now Internet ad targeting is an example.

I'm doing a startup, a Web site. The crucial enabling core of what I'm doing
has some advanced pure math and some applied math I derived. Users won't be
aware of anything mathematical. But if users really like the site, then it
will be mostly because of the math. So, it's some math -- not really
statistics, operations research, optimization, machine learning, artificial
intelligence, or management science -- it's just some math. The research
libraries have rows and rows of racks of math; I'm using some of it and have
derived some more.

Generally I found that the best customer for math is US national security,
especially near DC. E.g., now some people are building models to predict the
growth of COVID-19. Likely the core of that work is continuous time, discrete
state space Markov processes, maybe subordinated to Poisson processes. Okay:
One of the military projects I did was to evaluate the survivability of the US
SSBN (ballistic missile firing submarines) under a special scenario of global
nuclear war limited to sea -- a continuous time, discrete state space Markov
process subordinated to a Poisson process. Another project was to measure the
power spectra of ocean waves and, then, generate sample paths with that power
spectrum -- for some submarines. There was some more applied math in nonlinear
game theory of nuclear war.

Here's some applied math, curiously also related to the COVID-19 pandemic:
Predict revenue for FedEx. So, for time t, let y(t) be the revenue per day at
time t. Let b be the total market. Assume growth via _virality_ , i.e., word
of mouth advertising from current customers communicating with remaining
target customers. So, ..., get the simple first order differential equation,
for some k,

y'(t) = k y(t) (b - y(t))

where the solution is the logistic curve which can also be applied to make
predictions for epidemics. This little puppy pleased the FedEx BoD and saved
the company. Now, what was that, data science, AI, ML, OR, MS, optimization?
Nope -- just some applied math.

I have high hopes for the importance, relevance, power, fortunes from applied
math, but can't pick good applications like apples from a three.

