DSBDT

Aims & Scope

Data Science and Big Data Technology is a peer-reviewed academic journal dedicated to the exploration and dissemination of research in the fields of data science and big data technologies. It aims to provide a platform for researchers, practitioners, and industry experts to share their findings, methodologies, and innovations related to data analysis, data management, and the application of big data techniques across various domains. The journal covers a wide range of topics, including data mining, machine learning, statistical analysis, data visualization, and the development of big data frameworks and tools. It seeks to address the challenges and opportunities presented by the growing volume and complexity of data in today's digital landscape. By publishing high-quality research articles, case studies, and reviews, the journal contributes to the advancement of knowledge and practices in data science, fostering collaboration and innovation in this rapidly evolving field.

The topics related to this journal include but are not limited to:

1. Data Science

2. Big Data

3. Machine Learning

4. Data Mining

5. Data Analytics

6. Artificial Intelligence (AI)

7. Predictive Modeling

8. Data Visualization

9. Statistical Analysis

10. Cloud Computing

11. Data Warehousing

12. NoSQL Databases

13. Data Governance

14. Data Engineering

15. Natural Language Processing (NLP)

16. Data Lakes

17. Business Intelligence (BI)

18. Data Quality

19. Deep Learning

20. Streaming Data