Introduction to Data Science
Data science is not just about building complex models. It’s not about making awesome visualizations. It is not about writing code. Data science is the use of data to make as much impact as possible on the efficient operation of your company. Now, the impact can be in the form of multiple things. It could be in the form of insights or in the form of product recommendations for a company. Now to do these things, 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 they don’t care what kind of tools you use.
There are a lot of misconceptions about data science right now. So because of that, I want to make things clear. Mainly companies really emphasize using data to improve their products and services. Before data science, we popularized the term data mining in an article called From data mining to knowledge discovery in the database in 1996 which referred to the overall process of discovering useful information from data. In the digital world, the emergence of Internet-based data has made it impossible to keep up with the traditional database environment. That’s a lot of data so much data, it became too much to handle using traditional technologies.
So we call this big data. Simply data science almost involves everything that has something to do with data. That means collecting, analyzing, and modeling. But it also meant that the simplest questions require sophisticated data in fracture just to support the handling of the data 2010 sparked the rise of data science to support the needs of the businesses to draw insight from their massive unstructured datasets. Yet the most important part is its applications. Yes, all sorts of applications like machine learning.
This made it possible to practice with an approach similar to machine learning in almost all types of data-based applications. All the theoretical papers about recurring neural networks support vector machines become feasible. All the theoretical papers about recurring neural networks support vector machines become feasible. Something that can change the way we live and how we experience things in the world. Today, deep learning is no longer an academic concept. It becomes a tangible useful class of machine learning that affects our everyday lives.
So machine learning and AI dominated the media overshadowing every other aspect of data science. There is misalignment there, most of the 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 is not about how advanced your models are. It’s about how much impact you can have with your work. You are not a data cruncher you are a problem solver. You’re strategists. Actually, one of the most important things for companies is because you’re trying to tell the company, what to do with your product. Companies will give you the most ambiguous and hard problems and they expect you to guide the company in the right direction.
Let’s take a look at the best prospects when it comes to comparing data science and software engineering. The biggest difference is that data scientists all over the world are getting better salaries than software engineers. Data scientists have far more freedom to explore than software engineers. Simply put, they are the ones who explore the unknown. It is a great advantage that it can be used everywhere Data is everywhere. So there is data science everywhere. Data Science expertise can be applied in various fields. For example in finance, the biotech industry, internet-based companies, social media, food delivery apps, transportation, banking, stock market, e-commerce…, Data scientists generally do not feel bored because they are always doing new things. At the same time, the situation prepares us to learn new technologies.
Now we are going to discuss three important terms. They are artificial intelligence, machine learning, and deep learning to understand how they relate to the field of data science. The first one is Artificial Intelligence. AI is any code technique or algorithm, that enables machine learning to develop and demonstrate human behavior? We are in what many refer to as the era of weak AI the technology is still in its infancy and is expected to make machines capable of doing anything and everything the era of strong AI to transition from weak AI to strong machines, needs to learn the ways of humans. The techniques and processes that help machines in this broadly categorized under machine learning machines learn in predominantly two ways their learning is either supervised or unsupervised.
In supervised learning machines learn to predict outcomes with help from data scientists in unsupervised learning machines learn to predict outcomes on the go by recognizing patterns in input data when machines can draw meaningful inferences from large volumes of datasets they demonstrate the ability to learn deeply, deep learning requires artificial neural networks pay and ends which are like the biological neural networks in humans. These networks contain nodes in different layers that are connected and communicate with each other to make sense of voluminous input data. Deep learning is a subset of machine learning which in turn is a subset of artificial intelligence.
The three technologies help scientists and analysts interpret tons of data. We’re gonna be talking about how to get a data science job at you know a large company. So let’s get to the five tips on how to plan a data science job. Do an internship and have a technical degree. if you don’t have experience, then you need an internship. A degree is good It can be computer science, software engineering, economics, statistics, maths, system engineering, management engineering, and other engineering branches.
Learn SQL no matter where it is you are always gonna have to write a sequel because you have to get the data somehow no matter usually most companies set up their fracture so that you query data using sequel why because it is an easy language and even analysts or business intelligence people they use sequel.
Learn about success metrics and tracking metrics they are very important. Learn, python, Tableau Excel, or whatever to manipulate visualize, and interpret data visualizations, so use any of these technologies to show your results to interpret them. You have to learn to communicate really well because the whole point of a data scientist is to communicate their findings. you’re gonna be talking to a lot of people so a lot of cross-functional partners.
Technical interviews, The technical interview, is basically just SQL, python coding, and math interview. The math interview is sometimes they call it the quantitative interview, just asks you some simple probability questions or some simple descriptive and statistics questions
Product interview: In this, interview they will ask you a hypothetical question, regarding a product, and how you improve it.
Ref: Best Data Science Institute in Kochi
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