PRACTICAL USAGE OF DATA SCIENCE MODELS IN BUSINESS PROCESSES

2021 ◽  
Vol 3 (2) ◽  
pp. 12-16
Author(s):  
Lada Evgenevna Gonchar
Keyword(s):  
Data Science ◽  
Business Processes ◽  
Science Models

scholarly journals Behaviour of the Customers of an Insurance Company

2020 ◽  
Vol 6 (12) ◽  
pp. 336-341
Author(s):  
Dr. ML Sharma C Narinder Kaur and Mayank Singhal
Keyword(s):  
Health Insurance ◽  
Data Science ◽  
Consumer Behaviour ◽  
Real Life ◽  
Research Paper ◽  
Insurance Company ◽  
Health Issues ◽  
Time Data ◽  
Insurance Sector ◽  
Science Models

In today’s world as health issues among people increases, people become more aware for the health insurance. It’s a positive thing for the health companies but as the no. of customers increases, it is come into light that people are not punctual for paying the premium of the policy. This paper helps the policy companies to highlight and point out the defaulters who haven’t paid their premium. Mostly people forget about it, and some of them not paying the premium on time. In this research paper, I tried to understand the consumer behaviour in Insurance sector. The main objective of this paper to identify customers behaviour of paying the policy premiums and will they pay their next premium on time or not. Even will they pay their premium or not irrespective of time. Data was collected by various sites and some previous years data of some policy companies. Frequencies, Tabulation and some Data Science models have been used for the analysis. The objective of this project is to summarize is to make a predicting algorithm that can be used in real life applications to derive meaningful and accurate prediction based on the various aspects of data that is accessed.

Data Science Tools Application for Business Processes Modelling in Aviation

2019 ◽  
pp. 176-190
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Analytics-as-a-Service (AaaS)

Web Services ◽  
2019 ◽  
pp. 1262-1281
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Analytics-as-a-Service (AaaS)

2017 ◽  
pp. 26-45
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Data Science Tools Application for Business Processes Modelling in Aviation

2021 ◽  
pp. 617-631
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Big Data Science: Models and Approaches, Characteristics, Challenges, and Applications

2021 ◽  
pp. 157-196
Author(s):  
Riyanshi Gupta ◽  
Kartik Krishna Bhardwaj ◽  
Deepak Kumar Sharma
Keyword(s):  
Big Data ◽  
Data Science ◽  
Science Models

Data Science Techniques in Knowledge-Intensive Business Processes

2020 ◽  
Vol 1 (1) ◽  
pp. 52-67
Author(s):  
Matthias Lederer ◽  
Joanna Riedl
Keyword(s):  
Fuzzy Logic ◽  
Business Intelligence ◽  
Data Science ◽  
Business Processes ◽  
Due Diligence ◽  
Fuzzy Logic Systems ◽  
Investment Bank ◽  
Front Office ◽  
Management Techniques ◽  
Knowledge Intensive

The processes of an investment bank are considered to be particularly knowledge-intensive, because analysts need to extract or generate relevant knowledge from a variety of data. With increasing digitization, modern data science and business intelligence techniques are available to support or partially automate these activities. This study presents concrete use cases for front office processes of an investment bank as how knowledge management techniques can be used. For example, the article describes how expert systems can be used in the due diligence review or how fuzzy logic systems help in deciding whether to buy or sell securities. The article is based on 1079 texts (e.g. documented cases and articles) and serves researchers as well as practitioners as an application overview of data science techniques in the example area of knowledge-intensive banking processes.

Advanced Data Science Models for Player Behavioral Prediction

2019 ◽  
pp. 109-140
Author(s):  
África Periáñez ◽  
Anna Guitart ◽  
Pei Pei Chen ◽  
Ana Fernández del Río
Keyword(s):  
Data Science ◽  
Science Models ◽  
Behavioral Prediction
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