A Review of data science technology and Applications
Data science, Statistics, unstructured data, Machine learning, Cloud computing, Operations.
Data Science refers to an emerging area of work concerned with the collection, preparation, analysis, visualization, management, and preservation of large collections of information. Although the name Data Science seems to connect most strongly with areas such as databases and computer science, many different kinds of skills including non-mathematical skills are also needed here. Data Science is much more than simply analysing data. There are many people who enjoy analysing data who could happily spend all day looking at histograms and averages, but for those who prefer other activities, data science offers a range of roles and requires a range of skills. The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms an appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics, This review paper provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. Data science is growing up fast. Over the past five years companies have invested billions to get the most-talented data scientists to set up shop, amass zettabytes of material, and run it through their deduction machines to find signals in the unfathomable volume of noise. It’s working to a point. Data has begun to change our relationship to fields as varied as language translation, retail, health care, and basketball. But despite the success stories, many companies aren’t getting the value they could from data science. Even well-run operations that generate strong analysis fail to capitalize on their insights. Efforts fall short in the last mile, when it comes time to explain the stuff to decision makers. Basically we are focusing in this paper application of data science and upcoming technology which can be implementing in industrial to improve the quality of the product, Statistics, Automation, Machine learning, Cloud computing, Maintenance, Big Data etc.
"A Review of data science technology and Applications", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 5, page no.203 - 213, May-2020, Available :https://ijsdr.org/papers/IJSDR2005035.pdf
Volume 5
Issue 5,
May-2020
Pages : 203 - 213
Paper Reg. ID: IJSDR_191706
Published Paper Id: IJSDR2005035
Downloads: 000347260
Research Area: Engineering
Country: PUNE, MAHARASHTRA, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
