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What is Data Science

04 December 2021, Saturday By Priyanka Boruah
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Let's talk about what Data Science is, why it is so important for business and whether it is worth becoming a specialist in this field yourself. Briefly about one of the most demanded professions in the world. 

Definition of Data Science

Data Science is a set of disciplines, technologies and methods for analyzing the huge amount of information generated by business and non-profit organizations. Such a phenomenon as Data Science involves preparing for collecting data, processing it and presenting the extracted information to the right people in the right way. For example, a guide for making decisions for the development of a product, or for investors to showcase your company's performance.

The use of Data Science methods implies the use of software algorithms, advanced analytical tools, artificial intelligence and other modern technologies. This is a complex procedure that requires special skills. In this connection, a whole direction in the field of analytics and a separate profession - data scientist - appeared. 

What is Data Science

The fate of both individual projects and entire companies largely depends on the quality of data collection, the accuracy of the analysis, the objective usefulness of the obtained values ​​and their correct visualization. That is why data scientists are so important and in great demand in the IT market.

What do Data Science specialists do?

The Data Science specialist is responsible for the entire range of tasks related to the collection and processing of information, from the choice of data sources to their correct representation. 

A specialist in this area should:

  • Apply mathematical structures, statistical knowledge, and algorithms unique to data processing to manipulate huge amounts of information from different sources.
  • Use a wide range of tools and techniques, from sorting strings in SQL databases to integrating data into third-party software products.
  • Use artificial intelligence and machine-learing models to extract bits of the most critical data from the information received.
  • Create applications and utilities for information processing yourself.
  • Visualize and present the received data so that other team members, management and investors get answers to all questions asked within their competence.
  • Explain to upline colleagues how the information can be used to improve existing products, increase company profits, or improve development efficiency. 

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

How Data Science Works

A typical working day for a Data Scientist usually includes one of the data collection or processing steps. The entire workflow consists of 5 stages:

  1. Collection of information. Includes processes for collecting structured and unstructured data from all relevant sources. All available tools are used - from manual input and scraping of web pages to collecting metrics from proprietary systems.
  2. Information storage. The search for methods and means for storing the received data in such a form in which they can later be processed using mechanisms provided for this in advance. The data scientist must also remove duplicates, filter out unnecessary ones, etc. 
  3. Preprocessing. At this stage, the specialist must analyze the connections between different pieces of the extracted data, trace the patterns and the correspondence of the information received.
  4. Processing. At this moment, the specialist connects all his "magic" tools: artificial intelligence, machine learning models, analytical algorithms, etc.
  5. Communication. As a result, the specialist must arrange the found data in the form of tables, graphs, lists or in any other form that is preferable for demonstrating this information to different categories of consumers. 

Data Science Works

Data Science Tools

Data Scientists, while not developers, must be able to program and create applications. Otherwise, they simply won't have enough tools to process the data. Therefore, you will have to learn at least one of the two programming languages ​​most in demand in Data Science. 

  • R - It is an open source language and software environment for creating statistical calculations. R offers a large number of libraries and tools for filtering and data preprocessing. You can also use it to visualize data and train machine learning models for correct interaction with the information received.
  • Python - General purpose object-oriented programming language . Python is so versatile that it can be used in almost any field, including working with artificial intelligence and processing numerical values. 

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

How Data Science relates to cloud solutions

In addition to the tools listed above, data scientists need to become familiar with how cloud solutions work. 

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

Cloud clusters allow you to launch procedures for processing and collecting information on the network using large-scale networks of computers connected to each other. 

It uses services like Amazon S3, Microsoft Azure and Google Clouds. They allow corporations to process an unlimited stream of data from various sources by running specialized software and AI models in cloud clusters on powerful cloud computers. 

Data Science relates to cloud solutions

Also, cloud solutions simplify the work of Data Scientists, since they do not have to deal with software support, updating, etc.

Data Science Use Cases

Where is Data Science involved and what application patterns already exist? Here's what IBM has to say about it:

  • International banks use applications that allow, using cloud computing, to automatically find out the lending risks for individual customers.
  • Data Science is being leveraged by tech companies to develop autonomous vehicles. Data science tools allow you to process information on the go, helping AI cars move independently.
  • Business often uses 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.
  • The police are creating AI-based systems that analyze crimes and generate digestible statistical reports. Systems are also being developed to predict how to properly allocate police resources in order to reduce crime.
  • In healthcare, tools are being developed based on analytical indicators that allow monitoring patients remotely. 

Should you become a Data Science Specialist?

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

Data Scientists' salaries in India range from 100,000 rupees to 500,000 rupees, depending on the specifics of the job and the applicant's experience. 

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

Discuss

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