Data Science Research Centre

Introduction

The world is reshaping according to the latest technological trends and one of the major such trends in the modern era is Data Science. This is one of the most important domains in every sector of the industrial revolution since the last decade. Emerging of multinational companies to small scale start-ups, every organization requires data scientists for maximum exploration of large amount of data. There is a broad scope of data science in the present and future scenarios.

The present century is ruling by data and this is a reality; Data is running like a blood in the body of industrial growth in this technology-driven era. The upsurge of data on a global platform predicts that it is going to dictate the world for upcoming decades; all credit goes to digital media platforms, the IoT, and intelligent systems.

“The whole human civilization is producing such a massive amount of data in just 48 hours that it is compared with the data since the dawn of civilization until 15 years before”, true words from Eric Schmidt states for today’s technical scenario.

That is exactly what data scientists do. With the help of an algorithm and consumer behaviour, they manage to build customized recommendation charts. In today’s scenario, the huge amount of data is giving birth to great future scope for data analytics.

Major Scope of Data Science:

Healthcare Services:

One of the important service sectors, there is a huge requirement of data scientists in the healthcare services sector because they create a lot of data on a daily basis. Tackling a massive amount of data is not possible by any unprofessional candidate. Hospitals need to keep a record of patients’ medical history, bills, staff personal history, and much other information. Data scientists are getting hired in the medical sector to enhance the quality and safety of the data.

Transport Service Sector:

he transport sector requires a data scientist to analyse the data collected through passenger counting systems, asset management, location system, fare collecting, and ticketing.

Educations Services:

Data Science in Education helps you to have central control over the complete student data for evaluating the performance of the students and take suitable actions. This analysis will help you to make the changes that will benefit the students and will help them in all possible ways to solve their problems. Insurance Services: Big Data technologies are applied to predict risks and claims, to monitor and analyze them in order to develop effective strategies for customer attraction and retention. Undoubtedly, the Insurance companies benefit from data science application within the spheres of their great interest.

Banking Services:

Banks use data science in the areas of customer service, fraud detection, forecasting, understanding consumer sentiment, customer profiling, and target marketing, among others. ... Data science helps banks get a full segment-wise view of their customers.

Tourism Industry:

Data science is changing the face of the travel industry. It helps travel and tourism businesses to provide unique travel experiences and high satisfaction rates, preserving personal touch. In recent years data science has become one of the most promising technologies bringing changes to various industries.

E-commerce:

The e-commerce industry is booming just because of data scientists who analyze the data and create customized recommendation lists for providing great results to end-users. Thus, it is scientifically proven that data science will lead the technological developments in technology trends for upcoming decades.

Skills required being a Data Scientist:

Skills play an important role when it comes to data science. Most of the recruiters need candidates who have experience in tackling real-life problems regarding data analysis. For having a better scope of data analytics, degrees do not matter but both experience and skill matters the most. It is not that fresher’s have a low chance of getting hired, though top multinational companies prefer recruiting applicants who are experienced and skillful at the same time. There is no ‘idiot’s handbook’ that can turn a person into a successful data scientist. Students need to devote their time and effort to get a good hold on this subject. Here are five skills for a data scientist.

Multivariable linear algebra and calculus:

The majority of the data science model, machine learning is developed with various variables. A deep understanding of multivariable calculus is proven to be a boon while creating a machine learning model. Here are a few topics in mathematics that will be helpful in acquiring data science skills.

  • Cost function
  • Vector and scalar
  • Tensor and Matrix functions
  • Finding values of a function (maximum and minimum)
  • Stepwise function and Rectified Linear Unit Function
  • Gradients and Derivatives

The wrangling of data: Raw data is not ready for modeling purposes. So the scientists need to prepare the data for further examining i.e., transforming and mapping the data from raw to cooked form. For wrangling the data, one needs to acquire and combine them with the related area, and then cleanse it. Just by learning this skill, one can have a great data science future scope. What is the importance of data wrangling in data science, you ask?

  • It helps data scientists concentrate more on the analysis process than the cleansing process
  • This solution is beneficial in revealing good quality data from multiple sources
  • It curtails extraction time, response time, and processing time
  • This leads to the solution that is data-driven as well as supported by accurate data or information

Cloud computing:

The practice of data science comprises cloud computing. Data scientists need the products and services of computing to process data. The daily chores of data scientists include visualization and examination of data that is found in cloud storage. Cloud computing and data science go hand-in-hand because it enables data scientists to avail themselves platforms, like Google Cloud, AWS, and Azure. This is helpful in providing access to operating tools, Databases, Programming languages, and frameworks.

Basic understanding of Microsoft Excel:

Microsoft Excel has become one of the basic requirements for any job related to the back and front office. It is the core platform for a defined data algorithm. Excel proves to be the best editor in 2-dimensional data and also enables a live contact to an ongoing excel sheet in Python. It also makes the manipulation of data relatively straightforward than any other platform. So, having a good understanding of Microsoft Excel can recoup someone’s data science future without much effort.

DevOps:

Half of the population believes that DevOps has no relevance to data science and a person skilled in it can never switch to data science. This is a myth because the DevOps board nearly works with the developers for managing the cycle of applications. DevOps team provides highly accessible clumps of Apache Spark, Apache Hadoop, Apache Airflow, and Apache Kafka for handling the collection and transformation of information.

Future Scopes of Data Science:

The scope of Data Science is growing with every passing year. From 2008 to 2020, people across the globe have stepped into the digitalization age. The massive growth of data provides a glimpse of the future scope of data science in India and the rest of the world.

Laboratory of business analytics:

The objective of the learning: The process of designing and analysis of the structure of business processes using various algorithms with optimal results and recommendations to implement for maximum output. The objective of the subject of research: To develop the technologies to make life better with all constraints. Also designed to eliminate violations of the rules for building a business process structure using various modeling notations.

Data analysis:

Operational Analytical Processing (OLAP):

Support from OLAP cubes is greatly helpful and accelerates the processing of requests and execution of data calculations, providing analysis in different.

Indicator Cards:

This allows you to control the progress of the implementation of strategic plans based on certain key indicators also displayed on control panels. Thus, operational indicators are attached to the target strategic parameters. For further, more in-depth analysis, these indicators can be decrypted with additional reports. Such mechanisms allow the implementation of various management methodologies, in particular, such as the Balanced Scorecard (BSC), the Six Sigma, and the like.

Developed visualization:

Maximum visual representation of data using various interactive images, charts, and graphs.

Modelling, forecasting, and data research:

These tools are developed to help the enterprise to classify data, form their own nominal and measurable scales, and also use statistical concepts and tools for its analysis.

Decision support system

  • Reporting: The ability to create interactive and real-time reports, with developed mechanisms for their distribution and updating. The BI system must support different reporting styles (such as financial or operating control panels).
  • Control panels (dashboards) - a special type of reporting that allows you to represent data in a visual, intuitive way, using different scales, indicators, indicators, etc. With these control panels, users can monitor the current status of key indicators and processes and compare them with targeted goals. These panels allow you to receive business information from business applications and make it available in real-time.
  • Operational (ad hoc) queries - the ability to create and perform unique, non-standard queries for the respective users independently (without the involvement of IT specialists). In order to realize such capabilities, the BI platform should have a semantic layer that allows you to locate and retrieve the necessary information from existing sources. In addition, the system must have appropriate means for auditing these requests to verify the correctness of their execution.
  • Integration with Microsoft Office applications - In some cases, the BI platform is used as an intermediate tool for performing analytical tasks in compliance with the rules of correctness and security of data. In this case, as a client part of the BI-system can be products of the family of Microsoft Office in particular Excel. For these cases, the BI vendor needs to be fully
  • Integration with Microsoft Office applications - In some cases, the BI platform is used as an intermediate tool for performing analytical tasks in compliance with the rules of correctness and security of data. In this case, as a client part of the BI-system can be products of the family of Microsoft Office in particular Excel. For these cases, the BI vendor needs to be fully

Data Science & Machine Learning:

One of the most common confusions arises among modern technologies such as artificial intelligence, machine learning, big data, data science, deep learning, and more. While they are all closely interconnected, each has a distinct purpose and functionality. Over the past few years, the popularity of these technologies has risen to such an extent that several companies have now woken up to their importance on massive levels and are increasingly looking to implement them for their business growth.
However, among aspirants, there seem to be clouds of misconceptions surrounding these various technologies. This post will help you get a clear picture of what the two diverse yet closely associated technologies are all about.

Data Science:

In simple words, data science is the processing and analysis of data that you generate for various insights that will serve a myriad of business purposes. For instance, when you have logged in on Amazon and browsing through a few products or categories, you are generating data. This data will be used by a data scientist at the backend to understand your behaviour and push you retargeted advertisements and deals to get you purchase what you browsed. This is one of the simplest implementations of data science and it keeps getting more complex in terms of concepts like cart abandonment and more.

  • Data science involves the processes of
  • Data extraction
  • Data Cleansing
  • Analysis
  • Visualization
  • And actionable Insights generation

A data scientist is responsible for being as inquisitive as possible with the data set in hand to make the weirdest of business connections. Tons of insights lie unnoticed in massive chunks of data and it is the data science that sheds new light on areas like customer behavior, operational shortcomings, supply-chain cycles, predictive analysis, and more. Data science is crucial for companies to retain their customers and stay in the market.

Machine Learning: For simple comprehension, understand that machine learning is part of data science. It draws aspects from statistics and algorithms to work on the data generated and extracted from multiple resources. What happens most often is data gets generated in massive volumes and it becomes totally tedious for a data scientist to work on it. That is when machine learning comes into action. Machine learning is the ability given to a system to learn and process data sets autonomously without human intervention. This is achieved through complex algorithms and techniques like regression, supervised clustering, naïve Bayes, and more. One of the simplest applications of machine learning can be found on Netflix, where after you watch a couple of televisions series or movies, you could find the website recommending you shows and films based on your preferences, likes, and interests.

To become a machine learning expert, you need to possess knowledge of statistics and probability, technical skills like programming languages and coding, data evaluation and modeling skills, and more. Relevance of research is due to the need to analyze the models of business processes and to support their correctness because early detection and elimination of errors allow avoiding the costs of eliminating their consequences at the subsequent stages associated with the implementation and implementation of the business process. Within the framework of the research are considered the most In the considered literary sources, there are no formal methods that would allow identifying and eliminating violations of the rules for building a business process structure in different modeling notices. Thus, work is underway to develop a method for analyzing and improving the structure of business processes, the application of which will allow identifying and eliminating violations of rules for building a business process structure using different modeling notices. To do this, we propose appropriate models that allow us to formulate recommendations for eliminating violations of the rules for constructing a business process structure using various modeling notices. The development on the basis of the existing models of information technology, which allows to identify and eliminate violations of the rules for building a business process structure, aims to improve the process of constructing and analyzing the structure of business processes.

The Objective of the learning is to eliminate violations of the rules for building a business process structure based on developing a method for analyzing and improving the structure of business processes.

In the framework of the research the following tasks shall be performed:

  • An overview of the current state of the problem of constructing and analyzing the structure of business processes represented by various modeling notations.
  • Formation of the method of analysis and improvement of the structure of business processes, which will allow identifying and eliminating violations of the rules of its construction.
  • Development of models designed to formulate recommendations for eliminating violations of the rules for building a business process structure using various modeling notations.
  • Development of appropriate information technology models and their application for the verification of the efficiency of the developed method of analysis and improvement of the structure of business processes, to analyze the results.

Research in the direction of developing a method for analyzing and improving the structure of business processes is based on the use of:

  • Formal methods and notations of business process modeling;
  • Structural analysis and functional modeling;
  • The theory of sets;
  • The theory of graphs;
  • Mathematical programming;
  • Machine learning.

Classification of tools for creating BI-systems

Instrumental software for the creation of information and analytical systems, which is used today at enterprises, companies, and organizations, can be classified by the following features:

  • By functionality;
  • Full-function;
  • Partial (implementing one or more functions);
  • In the form of submission of processed data;
  • For working with structured data (digital data);
  • For working with unstructured data (text, video, graphics); combined
  • By the degree of automation of logical operations:
  • Complexes of search and data collection (search engines);
  • Analytical complexes (containing automated procedures or methods of data analysis);

This is not the complete report but an initial step towards a successful journey, we have many investors in India and abroad, need to develop a system and team first, Now Main Objective of Our Work as Indo Ukraine Turkey Group:

  • To develop a team in India
  • Initially some small live projects from Ukraine Turkey workgroup
  • Research and Training
  • To explore markets of India and other countries for big projects. Small countries like Nepal, Bangladesh, Sri Lanka, Myanmar, etc on priority in the first phase to get the projects.

Commercials for Group:

  • We want to share the profit on a profit-sharing basis as per the discussion in the combined discussion in the meeting.
  • One or two members shall be included in the board of directors
  • Accounts updates will be shared daily basis to all shareholders.

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