Data Science from a Perspective of Computer Science

2019 ◽  
pp. 209-219
Author(s):  
Sirje Virkus ◽  
Emmanouel Garoufallou
Keyword(s):  
Computer Science ◽  
Data Science

scholarly journals Addressing Hate Speech with Data Science: An Overview from Computer Science Perspective

2021 ◽  
pp. 317-336
Author(s):  
Ivan Srba ◽  
Gabriele Lenzini ◽  
Matus Pikuliak ◽  
Samuel Pecar
Keyword(s):  
Computer Science ◽  
Data Science ◽  
Hate Speech

scholarly journals Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1798 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Yung Cheol Byun
Keyword(s):  
Social Media ◽  
Computer Science ◽  
Data Science ◽  
Learning System ◽  
Local Context ◽  
Great Success ◽  
Learning Context ◽  
Wide Range ◽  
E Learning ◽  
Learning Software

Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future.

Data science overtakes computer science?

ACM Inroads ◽  
2012 ◽  
Vol 3 (3) ◽  
pp. 18-19 ◽  
Author(s):  
Deepak Kumar
Keyword(s):  
Computer Science ◽  
Data Science

scholarly journals Calabi-Yau Spaces in the String Landscape

2020 ◽  
Author(s):  
Yang-Hui He
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Data Science ◽  
Theoretical Physics ◽  
Superstring Theory ◽  
Data Set ◽  
Pure Mathematics ◽  
String Landscape ◽  
Natural Solution ◽  
Vacuum Solutions

Calabi-Yau spaces, or Kähler spaces admitting zero Ricci curvature, have played a pivotal role in theoretical physics and pure mathematics for the last half century. In physics, they constituted the first and natural solution to compactification of superstring theory to our 4-dimensional universe, primarily due to one of their equivalent definitions being the admittance of covariantly constant spinors. Since the mid-1980s, physicists and mathematicians have joined forces in creating explicit examples of Calabi-Yau spaces, compiling databases of formidable size, including the complete intersecion (CICY) data set, the weighted hypersurfaces data set, the elliptic-fibration data set, the Kreuzer-Skarke toric hypersurface data set, generalized CICYs, etc., totaling at least on the order of 1010 manifolds. These all contribute to the vast string landscape, the multitude of possible vacuum solutions to string compactification. More recently, this collaboration has been enriched by computer science and data science, the former in bench-marking the complexity of the algorithms in computing geometric quantities, and the latter in applying techniques such as machine learning in extracting unexpected information. These endeavours, inspired by the physics of the string landscape, have rendered the investigation of Calabi-Yau spaces one of the most exciting and interdisciplinary fields.

scholarly journals Data Dimensionality Reduction Techniques : Review

2020 ◽  
Vol 4 (4) ◽  
pp. 62-65
Author(s):  
Bhargavi K
Keyword(s):  
Computer Science ◽  
Scientific Method ◽  
Data Science ◽  
Process Data ◽  
Reduction Techniques ◽  
Data Dimensionality Reduction ◽  
Organization Analysis ◽  
User Data ◽  
Dimensionality Reduction Techniques ◽  
A Company

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data. Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.

scholarly journals What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyong Zhang ◽  
Keyword(s):  
United States ◽  
Text Mining ◽  
Computer Science ◽  
Topic Modeling ◽  
Data Science ◽  
The United States ◽  
Operational Definition ◽  
Bottom Up ◽  
Science Curricula

Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These topics reiterate that data science is at the intersection of statistics, computer science, and substantive fields.

scholarly journals Cooking Up Knowledge From Big Data Using Data Science

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrea Rau
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Programming Language ◽  
Data Science ◽  
Weather Forecasting ◽  
Original Question ◽  
Data Scientist ◽  
Using Data ◽  
The Way

Data collected in very large quantities are called big data, and big data has changed the way we think about and answer questions in many different fields, like weather forecasting and biology. With all this information available, we need computers to help us store, process, analyze, and understand it. Data science combines tools from fields like statistics, mathematics, and computer science to find interesting patterns in big data. Data scientists write step-by-step instructions called algorithms to teach computers how to learn from data. To help computers understand these instructions, algorithms must be translated from the original question asked by a data scientist into a programming language—and the results must be translated back, so that humans can understand them. That means that data scientists are data detectives, programmers, and translators all in one!

scholarly journals TOPIC SEGMENTATION METHODS COMPARISON ON COMPUTER SCIENCE TEXTS

2021 ◽  
pp. 59-66
Author(s):  
Volodymyr Sokol ◽  
Vitalii Krykun ◽  
Mariia Bilova ◽  
Ivan Perepelytsya ◽  
Volodymyr Pustovarov ◽  
...  
Keyword(s):  
Information Systems ◽  
Computer Science ◽  
Management System ◽  
Data Science ◽  
Continuous Training ◽  
Data Set ◽  
Topic Segmentation ◽  
Science Texts ◽  
Segmentation Methods ◽  
Segmentation Problem

The demand for the creation of information systems that simplifies and accelerates work has greatly increased in the context of the rapidinformatization of society and all its branches. It provokes the emergence of more and more companies involved in the development of softwareproducts and information systems in general. In order to ensure the systematization, processing and use of this knowledge, knowledge managementsystems are used. One of the main tasks of IT companies is continuous training of personnel. This requires export of the content from the company'sknowledge management system to the learning management system. The main goal of the research is to choose an algorithm that allows solving theproblem of marking up the text of articles close to those used in knowledge management systems of IT companies. To achieve this goal, it is necessaryto compare various topic segmentation methods on a dataset with a computer science texts. Inspec is one such dataset used for keyword extraction andin this research it has been adapted to the structure of the datasets used for the topic segmentation problem. The TextTiling and TextSeg methods wereused for comparison on some well-known data science metrics and specific metrics that relate to the topic segmentation problem. A new generalizedmetric was also introduced to compare the results for the topic segmentation problem. All software implementations of the algorithms were written inPython programming language and represent a set of interrelated functions. Results were obtained showing the advantages of the Text Seg method incomparison with TextTiling when compared using classical data science metrics and special metrics developed for the topic segmentation task. Fromall the metrics, including the introduced one it can be concluded that the TextSeg algorithm performs better than the TextTiling algorithm on theadapted Inspec test data set.

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