data science for all : what is data science

Let’s talk about Data Science for all, what Data Science is, why it is so important to business, and whether you should become an expert in this field yourself. Briefly about one of the most in-demand professions in the world.

Definition of Data Science for all

Data Science is a set of disciplines, technologies, and techniques for analyzing the vast amount of information generated by business and non-profit organizations. Such a phenomenon, as Data Science, includes training for the collection of data, its processing, and presentation of mined information to the right people in the right way. For example, management to make decisions on the development of a product or investors to demonstrate the performance of your company.

The use of Data Science techniques involves software algorithms, advanced analytical tools, artificial intelligence, and other modern technologies. This is a complex procedure that requires special skills. In this regard, an entire direction in the field of analytics and a separate profession appeared – the data scientist.

The quality of data collection, accuracy of analysis, objective usefulness of the results, and their correct visualization largely determines the fate of individual projects and entire companies. That is why data scientists are so important and in high demand in the IT market.

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What do Data Science specialists do?

On the shoulders of the Data Science specialist falls the entire range of tasks related to the collection and processing of information, from the selection of data sources to their correct representation.

A specialist in this field must:

  • Apply mathematical structures, statistical knowledge, and algorithms unique to the data processing to manage gigantic amounts of information from a variety of sources.
  • Use a wide range of tools and techniques, from sorting rows in SQL databases to integrating data into third-party software products.
  • Use artificial intelligence and machine-learning models to extract the most critical pieces of information.
  • Create applications and utilities to process the information.
  • Visualize and present findings so that other team members, management, and investors have the answers to the questions being asked, within the scope of their expertise.
  • Explain to superordinate colleagues how the information can be used to improve existing products, company profits, or development efficiencies.

Such a set of skills in one employee is quite rare, hence the high salaries of data scientists coupled with the high demand for specialists from this field.

How the science of Data Science works

A standard workday for a Data Scientist usually includes one of the stages of data collection or processing. The entire workflow consists of 5 stages:

  1. Information Gathering. Includes processes for collecting structured and unstructured data from all relevant sources. Every tool at hand is used, from manual input and scraping web pages to collecting metrics from proprietary systems.
  2. Information Retention. Finding methods and means to store the data in a form in which it can be processed later, using mechanisms provided for that purpose in advance. The data scientist must also remove duplicates, filter out redundancies, etc.
  3. Pre-processing. At this stage, the expert should analyze the relationships between the different pieces of extracted data, and trace the patterns and consistency of the information obtained.
  4. Processing. At this point, the specialist connects all of his “magic” tools: artificial intelligence, machine learning models, analytical algorithms, etc.
  5. Communication. As a result, the specialist must present the data he or she has found in tables, graphs, lists, or in any other form preferable for demonstrating the information to various categories of consumers.

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Data Science Tools

Although Data Science professionals are not developers, they need to be able to program and create applications. Otherwise, they simply will not have enough tools to process data. Therefore, they have to learn at least one of the two most popular programming languages in Data Science.

  • R. It is an open-source language and software environment for creating statistical computations. R offers a large number of libraries and tools for filtering and preprocessing data. It can also be used to visualize data and train machine learning models to interact correctly with the information obtained.
  • Python. A general-purpose object-oriented programming language. Python is so versatile that it can be used in almost any application, including artificial intelligence and numeric processing.

Data Scientists also use tools such as Apache Spark, Tableau, Microsoft PowerBI, and dozens of others to help them interact with data.

How Data Science Connects to Cloud Solutions

In addition to the tools listed above, Data Science professionals need to familiarize themselves with how cloud solutions function.

The fact is that data scientists have to work with enormous amounts of data. It is too time-consuming to interact with them using local machines. Standard computers simply do not have enough power to run massive processes of data analysis and processing.

Cloud clusters allow you to run procedures for processing and collecting information on a network, using large-scale networks of computers connected.

Services like Amazon S3, Microsoft Azure, and Google Clouds are used for this purpose. They allow corporations to process an unlimited stream of data from various sources by running specialized software and AI models on powerful cloud computers in cloud clusters.

Dashboard with data.

Cloud solutions also simplify the work of Data Scientists because they don’t have to deal with software maintenance, upgrades, etc.

Examples of Using Data Science
So where does Data Science come into play and what patterns of use already exist? Here’s what IBM has to say about it:

  • International banks use applications that use cloud computing to automatically figure out credit risks for individual customers.
  • Data Science is being leveraged by technology companies to develop autonomous vehicles. Data science tools allow information to be processed on the go, helping AI vehicles move on their own.
  • Businesses often leverage tools developed in close integration with Data Science products. In particular, it plays an important role in the robotization of business processes.
  • Media corporations use Data Science to analyze consumer interests.
  • Police are creating AI-based systems that analyze crimes and generate digestible statistical reports. Systems are also being created to anticipate how to properly allocate police resources to reduce crime.
  • In healthcare, analytics-based tools are being developed to monitor patients remotely.

Is it worth becoming a Data Scientist?

This is one of the most in-demand professions at the moment. The market continues to grow, and the amount of data that needs to be processed increases, so there will be no decline in interest in analysts.

The salaries of data scientists in Russia vary from 100,000 to 500,000 rubles depending on the job specifics and the experience of the applicant.

Hundreds of open positions, and impressive budgets. Looks like a great career for anyone interested in a new direction for themselves. In addition, you can now study Data Science in specialized courses at such online schools as GeekBrains, Skillbox, and Coursera.

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