Data Science versus Data Analytics
Here you can easily measure the difference between Data Science versus Data Analytics- The best option between them.
Data science is not about making complicated models. It’s not about making awesome visualizations.
It’s not about writing code but data science is about using data to create as much impact as possible for your company. These both are unique fields among the whole system.
Now to do those things, then you need tools like making complicated models or data visualizations or writing code.
But very much essential as a data scientist your job is to solve real company problems using data.
How can define :
What kind of tools you use is not dependent. Now there’s a lot of misconception about data science, especially on Social media.
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 company and those companies really emphasize on using data to improve their products.
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 computing power for statistics and he called this combo data science.
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 comments like upload share leaving our footprint in the digital landscape.
We call the 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. So then the journal of data science described data science as almost everything that has something to do with data Collecting analyzing modelling.
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 this thesis paper.
It became a tangible useful class of machine learning that would affect our everyday lives. Data Science versus Data Analytics is major confusion topics at this time.
So machine learning and 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 thinks of data science as researchers focused on machine learning. 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 has so many low-hanging fruits to improve their products that they don’t require any advanced machine learning or the 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 in the right direction.
All of these data engineering efforts are pretty important and It’s actually quite captured pretty well. 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.
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.
That’s why deep learning is on top of the hierarchy of needs and these things may be testing analytics they’re actually way more important for the industry so that’s why we’re hiring a lot of data scientists that do 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 of resources.
So you can only kind of have one Data Science. Data Science versus Data Analytics biggest confusion may be solved. So that one data scientist he has to do everything.
So you might be seeing all this being data scientists. Maybe you won’t be doing 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 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.
Finally, they have a lot more resources. They can separate data engineers and data scientists.
So usually in the collection, this is probably software engineering. And then here, you’re gonna have data engineers doing this.
So as a data scientist, you have to be a lot more technical.
That’s why they only hire people with a PhD or masters because they want you to be able to do more complicated things.
So let’s talk about a large company. 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.
They don’t want to do and they could focus on the things that they’re best at. I don’t need to worry about data engineering or deep learning stuff.
So here’s how it looks for a large company Instrumental logging sensors. This is all handled by software engineers Right?
Then here, cleaning and building data pipelines. This is for data engineers. Data Science versus Data Analytics difference.
Now here, between these two things, we have Data Science Analytics.
That’s what it’s called But then once we go to and deep learning, this is where we have research scientists.
Yeah Anyways, so in summary, as you can see, data science can be all of this and it depends on what company you are in And the definition will vary.
So please let me know what you would like to learn more about deep learning, or testing, experimentation,…
Depending on what you want to learn about leave a comment down below.