Data Science and How it is working right now in present.

Data science does not make difficult models. It doesn’t make the best visibility, It’s not about coding. scientific data is about using data to create as much impact as possible on your company. Impact can now be in the form of many things. It can be in the form of data in the form of data products or in the form of company product recommendations. Now to do those things, then you need tools like complex models or data recognition or code. As a data scientist your job is to solve real company problems using data and what kind of tools you use that we don’t tire of.

There are now a lot of misconceptions about data science, especially on YouTube and I think the reason for this is because there is a huge discrepancy between popular talk and what is needed in the industry. So for that I want to make things clear. I know some data scientists and they working for GAFA and those companies really focus on using data to improve their products. So this is my take on what data science is.

How it’s started

Before the science of data, we expanded the term data mining into an article called data removal to data retrieval. In 1996 when it referred to the whole process of obtaining useful data from data In 2001, William S. Cleveland wanted to bring a data mine. At some point He did that by combining computer science and data mining. Basically He did math with so many technologies that he believed would increase the chances of data mining and generate more creative power.

This is also the time when web 2.0 emerged where websites are no longer just a digital pamphlet, but a way of sharing the experience between millions and millions of users. We can now work with these websites which means we can provide post, comments, like, upload, quotes. What’s happening in the digital space we call the Internet and help create and create the environment we now know and love today. And guess what?

That’s too much data, too much to handle using traditional technology. So we call this Big Data. That opened up the world of data acquisition opportunities. But it also meant that simple questions required complex data infrastructure to support data management. We needed the same computer technologies as Map Download and Spark so the rise of big data sparked an increase in data science to support business needs. In its large informal sets.

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Data Science Analysis

Data science journal has described data science for almost everything related to data Collection of modeling analysis. So, in 2010 with a lot of data it made it possible to train machines in a data-driven way rather than a knowledge-driven method. All the theory papers about repetitive networks that support vector machines become a possibility that can change the way we live and the way we see things in the world.

In-depth reading is no longer a concept of the subject in thesis papers. It has become a useful practical tool for machine learning that can affect our daily lives. Machine learning and AI therefore dominated the media overshadowed. All other aspects of data science such as experimental analysis, testing and skills that we traditionally called business intelligence, So now the general public thinks of data science as researchers focus on machine learning and AI but the industry employs scientists of data as analysts.

Having Problems in saving data

The reason for the ambiguity is that yes, most of these data scientists may be working on a lot of technical problems but big companies like Google Facebook Netflix have a lot of fruit hanging to make their products without any high-level machine learning or mathematical knowledge to get these effects in their analysis. How much you have developed It’s about how much you can contribute to your work.

You are not a data cruncher. Companies will give you some mysterious and very difficult problems. And we expect you to direct the company in the right way. Now I want to conclude with real examples of data science works in Silicon Valley But first I have to print some charts. So let’s go do that (the conversation isn’t directly related to the topic) (the conversation isn’t directly related to the topic).

So this is a very useful chart that tells you about data science requirements. Now, it is very clear but sometimes we forget about it. Now under the pyramid we have collected it is clear that you have to collect some kind of data in order to use that data. So collect to save all these data engineering efforts is very important and actu- the big one we talked about is how hard it is to manage all this data.

Data Science and Products

About the same computer which means it’s the same with Spark Stuff. We know about this. Now the lesser known are the intermediaries that are all here and surprisingly this is actually one of the most important things for companies because you are trying to sell about your product. What do I mean by that? So I am an analytics that tells you using data what kind of understanding can tell me what is happening to my users and metrics this is important because what is happening with my product?

These are just some of the goal setting shareware that you can use to get the most out of your life. So these things are really important but they are not so covered in the media. Included in the media is this section. AI, in-depth learning. We’ve heard it over and over again, you know But when you think about it as a company, in the industry, it’s actually not something that is a priority or at least not something that produces more results than a very low cost effort.

That’s why in-depth learning of AI is beyond the realm of needs management and these things that may test analytics are actually very important in the industry which is why we hire a lot of scientists who do that. So what do data scientists actually do? Well that depends on the company for them as size So your start is kind of needy resources So you can have one type of single DS. For one data scientist to do it all.

So you probably see all of this as data scientists. Maybe you will not be doing AI or deep learning because that is not the most important thing right now but you may be doing all this. You have to set up all the data infrastructure You may also need to write some software code to add login and then you have to do the math yourself, then you have to create your own metrics, and you have to check yourself with A / B. That’s why for beginners when they need a data scientist this whole thing is data science, so that means you have to do everything. But let’s look at middle-class companies.

Now, they finally have a lot of resources. They can distinguish data engineers and data scientists. So often in the collection, this is probably software engineering. And then depending on whether the medium company makes many models of recommendations or things that require AI, DS will do all of this Right.

In The End

So as a data scientist, you have to have a lot of expertise. That’s why they only hire people with a PhD or masters because they want you to be able to do very complex things. So let’s talk about a big company now because you get too big. Then you can have many different employees working with different things. Thus the employee does not have to think about the things he does not want to do and can focus on the things that are most important to him.

For example, I and my large non-profit company will be on analytics so that I can focus on my work on analytics and metrics and things like that. So I don’t have to worry about data engineering or in-depth AI learning materials. of large companies Instrumental logging sensors. All of this is handled by software engineers? And here, cleaning and building data pipelines.

That’s what it’s called. However, once we get into AI and in-depth learning, that’s when we have research scientists or we call it the core of data science and they are supported by engineers who are now machine learning engineers. Of course it is, so in a nutshell, as you can see, the science of data can be all of this and it depends on the company you are in. And the definition will vary.

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