| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 587 | 13 | 21 | 10 |
Annals of Data Science facilitates discussions on Applied mathematics, Statistics, Estimator, Artificial intelligence and Order statistic. Annals of Data Science addresses concerns in Applied mathematics which are intertwined with other disciplines, such as Distribution (mathematics), Weibull distribution, Exponential distribution, Exponential function and Distribution (number theory). The journal connects research in Weibull distribution with the related topic of Inverse.
Statistics studies presented in the journal focus on topics such as Confidence interval, Bayesian probability, Censoring (statistics), Bayes' theorem and Regression analysis. The Estimator works featured in it incorporate elements from Mean squared error, Entropy (information theory), Monte Carlo method and Maximum likelihood. Annals of Data Science features Mean squared error research that overlaps with concepts in Bayes estimator.
In it, Machine learning, Data mining and Pattern recognition are investigated in conjunction with one another to address concerns in Artificial intelligence research. It links adjacent topics like Data mining with Cluster analysis. It facilitates discussions on Order statistic that incorporate concepts from other fields like Quantile function, Probability density function, Moment-generating function and Quantile.
The most cited papers mostly deal with topics like Data science, Big data, Artificial intelligence, Data mining and Analytics. Aside from discussions in Artificial intelligence, the journal articles also deal with the subject of Machine learning which intersects with Generalization, Computational complexity theory and Hyperplane disciplines. The journal articles focus on Data mining but the discussions also offer insight into other areas such as False alarm, Hierarchical clustering, Correlation clustering, Cluster analysis and Conceptual clustering.
The discussions in the journal mainly cover the fields of Applied mathematics, Statistics, Estimator, Artificial intelligence and Weibull distribution. Applied mathematics research presented in Annals of Data Science encompasses a variety of subjects, including Distribution (mathematics), Power function, Distribution (number theory) and Generalization. The work on Statistics tackled in it brings together disciplines like Group method of data handling and Selection (genetic algorithm).
The journal explores topics in Estimator which can be helpful for research in disciplines like Bivariate analysis, Probability density function, Moment-generating function and Conditional probability distribution. Annals of Data Science addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Genetic algorithm, Machine learning and Pattern recognition. The research on Pattern recognition tackled can also make contributions to studies in the areas of Time complexity, Embedding and Cluster analysis.
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 Annals of Data Science (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 Annals of Data Science (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, 7.69% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 23.33% were posted by at least one author from the top 10 institutions publishing in the journal. Another 11.67% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 28.33% of all publications and 36.67% 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.
Considering an exciting career in Data Science? Many paths lead into the industry. With the variety of research topics covered in journals like the Annals of Data Science, it is clear that the field offers diverse opportunities for individuals with different interests and strengths. Career options in data science can range from being a Data Analyst, Statistical Consultant, Machine Learning Engineer, to name a few. You can choose to work across different sectors like healthcare, finance, e-commerce, and many more. Speaking of specifics, data science professionals who specialize in certain research areas such as 'Applied Mathematics', 'Artificial Intelligence' or 'Machine Learning' are in high demand in today's data-driven world. Do not restrict yourself to only these paths. There are numerous other intersecting fields that you might be interested to explore. Creating a career in data science involves not just gaining theoretical knowledge, but also practical experience and constant learning. For instance, in your journey as an aspiring data scientist, reading scientific articles, attending conferences and webinars, and contributing to research works can prove to be of immense help. But what about something more specific? Suppose you're passionate about Art and also keen on teaching, coupling it with your data science skills. There is a possibility for such a unique combination! Read about how to become an art teacher in California. It divulges how data can be used to improve and streamline the art teaching process. Whether you're a seasoned professional or a fresh graduate, opportunities are plentiful in the data science industry. Explore and find a career that not only fits your professional goals but also lets you enjoy the work you do. The only constant in the world of data science is change. You need to upgrade your skills continuously to stay relevant in this ever-evolving industry.
Qi Wang;Yue Ma;Kun Zhao;Yingjie Tian
(2020)Unknown
(2022)Mohiuddin Ahmed;A. K. M. Najmul Islam
(2020)Heba M. Emara;Mohamed Elwekeil;Taha E. Taha;Adel S. El-Fishawy
(2021)Feng Liu;Yong Shi;Yong Shi
(2020)For those interested in pursuing Computer Science in the USA, exploring online degree options can be a practical and flexible way to advance your education. Many students opt for programs offered by the best online accredited universities, ensuring they receive quality education recognized by employers.
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