| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 364 | 53 | 55 | 16 |
Data Science and Engineering facilitates discussions on Artificial intelligence, Information retrieval, Data mining, Theoretical computer science and Set (abstract data type). The journal focuses on Artificial intelligence but the discussions also offer insight into other areas such as Crowdsourcing, Machine learning and Natural language processing. While the journal focused on Information retrieval, it was also able to explore topics like Semantics and Social network.
The journal explores themes in Data mining like Big data and links them with other fields of study like Structure (mathematical logic). In it, Vertex (geometry), Graph and Graph (abstract data type) are investigated in conjunction with one another to address concerns in Theoretical computer science research. Data Science and Engineering connects the study in Graph (abstract data type) with the closely related area of Exploit.
It integrates many fields, including Set (abstract data type) and related. Cluster analysis study tackled is connected to the field of Embedding.
The journal articles mainly tackle studies in Big data, Data mining, Graph (abstract data type), Information retrieval and Artificial intelligence. The Big data research tackled in the most cited papers is interrelated with Data science which concerns subjects like State (computer science). While Data mining is the focus of the journal papers, it also provides insights into the studies of Data compression and Cluster analysis.
The journal investigates areas of study like Data mining, Information retrieval, Set (abstract data type), Artificial intelligence and Distributed computing. Aside from Data mining, the journal also covered works in the field of Structure (mathematical logic). Search engine indexing studies in the realm of Information retrieval interact with fields like Internet access.
It dives deep in exploring the relationship between the study of Artificial intelligence and Machine learning. The research on Distributed computing featured in Data Science and Engineering combines topics in other fields like Variety (cybernetics), Big data and Identification (information). Topics in Theoretical computer science were tackled in line with various other fields like Point of interest and Pruning (decision trees).
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Data Science and Engineering (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Data Science and Engineering (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 3.45% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 25.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 21.43% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.86% of all publications and 35.71% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
In recent years, the demand for experts in the field of data science and engineering has been exponentially increasing. The skills and tools used in this field can be applied to various industries. Notably, teaching is one niche where these skills can be adapted. Specifically, there is rising interest in employing data science and engineering teachers in private schools. For instance, in Wyoming, private schools have been exploring the incorporation of data science and engineering into their curriculum. To achieve this, the schools are seeking individuals with suitable qualifications and a passion for teaching. If you are interested in becoming a private school teacher in Wyoming equipped with your data science and engineering skills, you need to be aware of the requirements and the process. The requirements often entail a combination of relevant educational qualifications, practical experience in the field, and of course, a teaching certificate. Space for teachers able to engage in advanced topics such as Artificial Intelligence, Data Mining, and Information Retrieval is particularly vast. Visit the following link to get detailed information about the requirements: private school teacher requirements Wyoming. By exploring this opportunity, you get to tap into a unique career path that merges your expertise with the art of imparting knowledge. Navigating through your career as a private school teacher in data science and engineering can be a rewarding journey. Not only will it lead to personal and professional growth, but you would also play an instrumental role in shaping the future of young and curious minds.
Unknown
(2022)Zhabiz Gharibshah;Xingquan Zhu;Arthur Hainline;Michael Conway
(2020)Rubina Sarki;Khandakar Ahmed;Hua Wang;Yanchun Zhang
(2021)Yun Peng;Byron Choi;Jianliang Xu
(2021)Shiwen Wu;Yuanxing Zhang;Chengliang Gao;Kaigui Bian
(2020)Xiang Li;Yan Zhao;Xiaofang Zhou;Kai Zheng
(2020)Fabio Azzalini;Songle Jin;Marco Renzi;Letizia Tanca
(2021)Jianye Yang;Wu Yao;Wenjie Zhang
(2021)Jihong Chen;Wei Chen;Jinjing Huang;Jinhua Fang
(2020)For those interested in advancing their education in computer science while balancing professional commitments, opting for online PhD programs for working professionals offers flexibility and the opportunity to contribute to cutting-edge research. These programs are structured to accommodate busy schedules without compromising on academic rigor.
If you’re seeking to boost your qualifications quickly, pursuing a one year masters degree in computer science or related fields can fast-track your career growth. These accelerated programs provide focused learning with immediate applicability in tech industries.
Many students and professionals are also exploring fast degrees online to capitalize on the growing demand for tech skills. These programs emphasize practical knowledge and are designed to help graduates enter the workforce promptly, often leading to well-paying positions.
Choosing the right specialty is crucial. Computer science consistently appears among the top 10 best majors for the future, reflecting strong job prospects and growth potential. Aligning your studies with emerging fields can open doors to innovative career pathways.
