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Sunday, June 16, 2024

What is Data Science in simple words

Data Science or data science is a vast area of the IT industry, the principle of which works in one article is quite difficult. After all, the more you delve into it, the more confusing it becomes. But I will still try, and in this article we will deal with the critical definition of this industry and better understand its relevance in modern society.

What is Data Science?

The technological world is growing exponentially in the digital universe. There is an innumerable stream of data in the form of searches, content, images, photos, and more. This stream used by our digital devices (smartphones, TVs, PCs, etc.) needs to be processed and controlled. So, the study of this data and the acquisition of experience to control and change it, the formulation of hypotheses and forecasting, as well as the use of them for certain purposes is called data science. This is a “digital” analogue of such sciences as physics, chemistry and biology. We cannot call it a full-fledged science; it does not teach us unique technologies and does not talk about the products of high engineering. Data Science is something else entirely. It does not study physical phenomena, but only studies intangible information present in the digital world.

The Importance of Data Science

Most developers of modern digital devices, software and technologies (smart devices, applications, AR/VR, game consoles, etc.) use data science to develop them. You may have heard of terms like machine learning, artificial intelligence, and data analytics. These are all branches of data science and with its help you can achieve a lot in the digital dimension. Data rules the world, and if you are able to control it, then there will be limitless possibilities for you. But let’s not get ahead of ourselves and first consider where modern companies successfully apply Data Science.

FORECASTING

Created by Midjourney neural network

Prediction is when the information collected is used to determine the next user action. Accuracy largely depends on the amount of data available. For example, some business intelligence programs use it to predict the potential outcome of a business tactic or marketing campaign. The more information the forecasting module contains, the better results it can demonstrate.

The most understandable example of this concept is the text input field in an Internet browser. Enter a query in the search engine, immediately several relevant phrases will appear for you in the drop-down list. And just don’t say it’s magic!

Classification

Recognizing a data type and grouping information by those types is a classification. You also store your personal files in certain folders for later use. So it is here.

For example, your smartphone categorizes images in one folder and videos in another, and an email box quickly determines what’s spam and what’s not.

Recommendations

Modern recommendation algorithms sometimes know the interests and preferences of the user better than he himself. They display ads for products you were once interested in and show you videos on Youtube similar to the ones you recently watched. Data Science studies human behavior, tracks patterns and predicts the final result in order to derive the most optimal recommendation.

Automation

Automation allows you to reduce the need for human participation in the work, initiating an automated process of any action. For example, something as simple as ringing an alarm on your phone in the morning is also automation, because it works without direct human intervention.

Machine Learning

Machine learning is the collection of data used by a piece of technology, or more simply, the science of how to train artificial intelligence to work independently and expand its knowledge of the world. At the moment, this concept has become an integral part of the digital ecosystem and is used in medicine, construction and other sectors of human activity where robots are used.

Artificial intelligence

Many people associate machine learning with artificial intelligence, but it’s a little different. Artificial intelligence uses more data streams for comprehensive collection. It allows technology to learn, predict, and even think using machine learning and other user experiences.

Data Analytics

Today, the use of data analytics has become indispensable for achieving success in any endeavor. For example, an e-commerce platform can use it to determine a user’s behavior and offer them something that really suits them. And the support service will write answers to the bot to common customer questions.

I want to work with data, but by whom?

The countless string of vacancies in this area can be confusing. However, breaking through all the bizarre incomprehensible names, we can distinguish three main professions that are extremely popular when working with data:

Data Analyst

The data analyst examines and analyzes the collected information and communicates the information received to the final recipient (client, development department, etc.)

Data Engineer

A data engineer is often confused with an analyst and a data scientist. This person observes the introduction of adjustments and changes to the data, uses special software to find solutions to emerging problems, improves the quality of new data or edits old ones.

Data Scientist

The main responsibility of a Data Scientist is to create new data. This is a scientist who puts forward hypotheses and tests the effectiveness of new theories and mechanisms. These people are responsible for thinking outside the box and make discoveries, just like scientists in other fields.

Advantages of the concept

The study and development of data science is extremely useful for modern business, because with its help you can:

  • Predict current revenue and business performance, and understand in which direction the company is moving.
  • Model the new tactics and strategies you want to implement.
  • Automate any processes.
  • Provide customers with solutions developed on the basis of artificial intelligence.

Imagine that all of the above processes you will be able to control from your phone. And now tell me – How is this not the technology of the future? And behind all this are data specialists.

Why is Data Science a data science?

Are you wondering why Data Science is called “science” and not some kind of “expertise” or “study”? This is due to the fact that the basis of working with data is science, without which no work in the technological industry is conceivable – mathematics! Without knowledge in this area, you will not be able to control the working algorithms of Data Science. It is also necessary to master one of the programming languages (Python, R, Java, etc.) If you combine these two aspects, you will get fundamental skills for working as a data scientist.

And if the basic concepts of a programming language can be mastered quickly enough, then difficulties may arise with mathematics.

  • prepare for the entrance exams to the Yandex School of Data Analysis;
  • Delve into mathematical analysis, linear algebra, combinatorics, probability theory and mathematical statistics;
  • Learn the role of numbers, formulas, and functions in developing machine learning algorithms.
  • master the special terminology and be able to read articles on Data Science without constant calls to the search engine.

The course is suitable for both beginners and existing programmers and analysts who want to improve their level or move to a new field.

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