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Data science is not about making complicated models. It's not about making awesome visualizations
It's not about writing code data science is about using data to create as much impact as possible for your company
Now impact can be in the form of multiple things
It could be in the form of insights in the form of data products or in the form of product recommendations for a company
Now to do those things, then you need tools like making complicated models or data visualizations or writing code
But essentially as a data scientist
your job is to solve real company problems using data and what kind of tools you use we don't care
Now there's a lot of misconception about data science, especially on YouTube
and I think the reason for this is because there's a huge misalignment between
what's popular to talk about and what's needed in the industry. So because of that I want to make things clear. I
am a data scientist working for a GAFA company and
those companies really emphasize on using data to improve their products
So this is my take on what is data science
Before data science, we popularized the term data mining in an article called from data mining to knowledge discovery in databases in
1996 in which it referred to the overall process of discovering useful information from data
In 2001, William S. Cleveland wanted to bring data mining to another level
He did that by combining computer science with data mining
Basically
He made statistics a lot more technical which he believed would expand the possibilities of data mining and produce a powerful force for innovation
Now you can take advantage of compute power for statistics and he called this combo data science. Around this time
this is also when web 2.0 emerged where websites are no longer just a digital pamphlet, but a medium for a shared experience
amongst millions and millions of users
These are web sites like MySpace in 2003
Facebook in 2004 and YouTube in 2005. We can now interact with these web sites
meaning we can contribute post comment like upload share
leaving our footprint in the digital landscape we call Internet and help create and shape the ecosystem
we now know and love today. And guess what?
That's a lot of data so much data, it became too much to handle using traditional technologies. So we call this Big Data.
That opened a world of possibilities in finding insights using data
But it also meant that the simplest questions require sophisticated data infrastructure just to support the handling of the data
We needed parallel computing technology like MapReduce, Hadoop, and Spark
so the rise of big data in
2010 sparked the rise of data science to support the needs of the businesses to draw insights from their massive unstructured data sets
So then the journal of data science described data science as almost everything that has something to do with data
Collecting analyzing modeling. Yet the most important part is its applications. All sorts of applications.
Yes, all sorts of applications like machine learning
So in 2010 with the new abundance of data
it made it possible to train machines with a data-driven approach
rather than a knowledge driven approach. All the theoretical papers about recurring neural networks support vector machines became feasible
Something that can change the way we live and how we experience things in the world
Deep learning is no longer an academic concept in these thesis paper
It became a tangible useful class of machine learning that would affect our everyday lives
So machine learning and AI dominated the media overshadowing every other aspect of data science
like exploratory analysis,
experimentation, ... And skills we traditionally called business intelligence
So now the general public think of data science as
researchers focused on machine learning and AI but the industry is hiring data scientists as analysts
So there's a misalignment there
The reason for the misalignment is that yes, most of these data scientists can probably work on more technical problems
but big companies like Google Facebook Netflix have so many low-hanging fruits to improve their products that they don't require any
advanced machine learning or
statistical knowledge to find these impacts in their analysis
Being a good data scientist isn't about how advanced your models are
It's about how much impact you can have with your work. You're not a data cruncher. You're a problem solver
You're strategists. Companies will give you the most ambiguous and hard problems. And we expect you to guide the company to the right direction
Ok, now I want to conclude with real-life examples of data science jobs in Silicon Valley
But first I have to print some charts. So let's go do that
(conversation not directly related to the topic)
(conversation not directly related to the topic)
So this is a very useful chart that tells you the needs of data science. Now, it's pretty obvious
but sometimes we kind of forget about it now
At the bottom of the pyramid we have collect you obviously have to collect some sort of data to be able to use that data
So collect storing transforming all of these data engineering effort is pretty important and it's actu-
It's actually quite captured pretty well in media because of big data we talked about how difficult it is to manage all this data
We talked about parallel computing which means like Hadoop and Spark
Stuff like that. We know about this. Now the thing that's less known is the stuff in between which is right here
everything that's here and
Surprisingly this is actually one of the most important things for companies because you're trying to tell the company
what to do with your product. So what do I mean by that? So I'm an analytics that tells you
using the data what kind of insights can tell me what are happening to my users and then metrics this is important because
what's going on with my product?
You know, these metrics will tell you if you're successful or not. And then also, you know a be testing of course
Experimentation that allows you to know, which product versions are the best
So these things are actually really important but they're not so covered in media. What's covered in media
is this part. AI, deep learning. We've heard it on and on about it, you know
But when you think about it for a company, for the industry,
It's actually not the highest priority or at least it's not the thing that yields the most result for the lowest amount of effort
That's why AI deep learning is on top of the hierarchy of needs and these things may be testing analytics
they're actually way more important for industry
so that's why we're hiring a lot of data scientists that does that. So what do data scientists actually do?
Well that depends on the company because of them as of the size
So for a start-up you kind of lack resources
So you can only kind of have one DS. So that one data scientist
he has to do everything. So you might be seeing all all this
being data scientists. Maybe you won't be doing AI or deep learning because that's not a priority right now
But you might be doing all of these. You have to set up the whole data infrastructure
You might even have to write some software code to add logging and then you have to do the analytics
yourself, then you have to build the metrics yourself, and you have to do A/B testing yourself. That's why
for startups if they need a data scientist this whole thing is
data science, so that means you have to do everything. But let's look at medium-sized companies. Now, finally
they have a lot more resources. They can separate the data engineers and the data scientists
So usually in collection, this is probably software engineering. And
then here, you're gonna have data engineers doing this. And then depending if you're medium-sized company does a lot of
recommendation models or stuff that requires AI, then DS will do all these
Right. So as a data scientist, you have to be a lot more technical
That's why they only hire people with PhDs or masters because they want you to be able to do the more complicated things
So let's talk about large company now
Because you're getting a lot bigger
you probably have a lot more money and then you can spend it more on employees
So you can have a lot of different employees working on different things. That way
the employee does not need to think about this stuff that they don't want to do and they could focus on the things that they're
best at. For example, me and my untitled large company
I would be in analytics so I could just focus my work on analytics and metrics and stuff like that
So I don't need to worry about data engineering or AI deep learning stuff
So here's how it looks for a large company
Instrumental logging sensors. This is all handled by software engineers
Right? And then here, cleaning and building data pipelines
This is for data engineers. Now here, between these two things, we have Data
Science Analytics. That's what it's called
But then once we go to the AI and deep learning, this is where we have
research scientists
or we call it data science core
and they are backed by and now engineers which are machine learning engineers. Yeah
Anyways, so in summary, as you can see, data science can be all of this and it depends what company you are in And
the definition will vary. So please let me know what you would like to learn more about AI deep learning, or A/B testing,
experimentation,... Depending on what you want to learn about
leave a comment down below so I could talk about it or I could find someone who knows about this and I can share the
insights with you
So yeah, if you like this video, don't forget to like and subscribe
So, yeah. Hope you have a wonderful day. Hope this was helpful. But yeah, thanks for watching
Peace.
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What REALLY is Data Science? Told by a Data Scientist

92 Folder Collection
安東尼 published on April 22, 2019
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