International Journal of Data Science and Analytics

Top Research Topics at Journal of data science?

The journal focuses on Statistics, Artificial intelligence, Econometrics, Management information systems and Data mining. It focused on Statistics research but expanded to cover Estimation. Issues in Artificial intelligence were discussed, taking into consideration concepts from other disciplines like Machine learning and Pattern recognition.

The journal explores topics in Management information systems which can be helpful for research in disciplines like Data science and Big data. Journal of data science connects the study in Data mining with the closely related area of Cluster analysis.

• Statistics (25.20%)
• Artificial intelligence (15.04%)
• Econometrics (13.44%)

What are the most cited papers published in the journal?

• Singular Spectrum Analysis: Methodology and Comparison (385 citations)
• The Weibull-G Family of Probability Distributions (217 citations)
• The Exponentiated Generalized Class of Distributions (159 citations)

Research areas of the most cited articles at Journal of data science:

The most cited articles are organized to reinforce research efforts on Statistics, Artificial intelligence, Management information systems, Data mining and Econometrics. The published articles connects research in Statistics with the related topics of Applied mathematics. While work presented in the published papers provide substantial information on Econometrics, it also covers topics in Regression analysis, Value at risk and Regression.

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Journal of data science (based on the number of publications) are:

• Anthony Wanjoya (19 papers) published 2 papers at the last edition, 5 less than at the previous edition,
• Gauss M. Cordeiro (16 papers) published 16 papers at the last edition,
• Samuel Mwalili (11 papers) absent at the last edition,
• Dino Pedreschi (10 papers) published 7 papers at the last edition,
• Gordon G. Bechtel (10 papers) published 10 papers at the last edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of data science (based on the number of publications) are:

• University of Pisa (14 papers) published 9 papers at the last edition,
• Istituto di Scienza e Tecnologie dell'Informazione (10 papers) published 7 papers at the last edition,
• University of Porto (7 papers) published 2 papers at the last edition,
• Birla Institute of Technology and Science (6 papers) published 1 paper at the last edition, 2 less than at the previous edition,
• Carnegie Mellon University (6 papers) published 1 paper at the last edition.

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.

Publication chance based on affiliation

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, 86.14% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.53% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.25% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 10.31% of all publications and 63.92% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities In Data Science

This extensive overview of research in the field of data science, including its various branches like statistics, artificial intelligence, econometrics and data mining, positions you well for a rewarding career in this thriving industry. Opportunities range from data analysts and scientists to machine learning engineers and much more. Those with a knack for mathematics might also find a rewarding career as a middle school math teacher, a role that can indirectly contribute to this expanding field, by producing the next generation of aspiring data scientists. For more information on this profession within the state of Texas, feel free to check out this comprehensive guide on how to be a middle school math teacher in Texas.

Considering the rich interdisciplinary nature of data science, there are exciting opportunities for research in this domain across various industries. Data science applications span across healthcare, finance, retail, energy, and many more sectors, all of which are constantly looking for professionals who can extract insights from data and contribute to informed decision-making. This wide range of opportunities reinforces the versatile nature of a career in data science and highlights how impactful your skill-set can be to organizations worldwide.