ISCSITR - International Journal of Data Science (ISCSITR-IJDS) is a peer-reviewed, open-access journal committed to advancing research and innovation in the field of data science. It provides a platform for researchers, academicians, and industry professionals to publish and share original research, case studies, and technical reports on various aspects of data science.
Data science plays a crucial role in transforming raw data into meaningful insights, enabling informed decision-making and driving innovation across industries. ISCSITR-IJDS aims to support this transformation by encouraging research that explores new methodologies, algorithms, and applications in data science. The journal seeks to bridge the gap between theory and practice, facilitating the exchange of knowledge and fostering collaboration among data science professionals.
The journal covers a wide range of topics, including but not limited to data mining, machine learning, big data analytics, statistical modeling, and data visualization. It also explores real-world applications of data science in fields such as healthcare, finance, marketing, and environmental science.
ISCSITR-IJDS follows a rigorous double-blind peer-review process to ensure that all published research meets the highest standards of quality and integrity. Our editorial board consists of experienced researchers and industry professionals who provide valuable insights and guidance throughout the review process.
By encouraging the exchange of ideas and promoting high-quality research, ISCSITR-IJDS aims to contribute to the advancement of data science and help organizations and individuals make data-driven decisions with greater accuracy and efficiency.
???? For more information or to submit your manuscript, contact us at: iscsitr@gmail.com
