Research on Data Science, Data Analytics and Big Data

Nadikattu, Rahul Reddy

doi:10.2139/ssrn.3622844

Cited by 15 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Nakamura (2020) regarded big data analytics as data science. In practice, Data Science provides broader insights and focuses on what questions should be asked, while big data analysis emphasizes nding answers to the questions asked (Nadikattu, 2020). Big Data Analysis is merely one of the stages of a Data Science Lifecycle, and data analysis systems must provide effective mechanisms to design and complete analysis tasks (Elshawi, 2018).…”

Section: Big Data Analyticsmentioning

confidence: 99%

WITHDRAWN: Data Science: Trends, Perspectives, and Prospects

Borjigin

Zhang

2021

Preprint

View full text Add to dashboard Cite

Data Science is one of today’s most rapidly growing academic fields and has significant implications for all conventional scientific studies. However, most of the relevant studies so far have been limited to one or several facets of Data Science from a specific application domain perspective and fail to discuss its theoretical framework. Data Science is a novel science in that its research goals, perspectives, and body of knowledge is distinct from other sciences. The core theories of Data Science are the DIKW pyramid, data-intensive scientific discovery, data science lifecycle, data wrangling or munging, big data analytics, data management and governance, data products development, and big data visualization. Six main trends characterize the recent theoretical studies on Data Science: growing significance of DataOps, the rise of citizen data scientists, enabling augmented data science, diversity of domain-specific data science, and implementing data stories as data products. The further development of Data Science should prioritize four ways to turning challenges into opportunities: accelerating theoretical studies of data science, the trade-off between explainability and performance, achieving data ethics, privacy and trust, and aligning academic curricula to industrial needs.

show abstract

Section: Big Data Analyticsmentioning

confidence: 99%

WITHDRAWN: Data Science: Trends, Perspectives, and Prospects

Borjigin

Zhang

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The utilization of advanced ML algorithms enables these professionals not just only to process large sets of data but analyze it effectively along with uncovering hidden patterns, trends, or correlations that would otherwise have gone unnoticed previously. By harnessing this raw potential known as insights; they can utilize this knowledge base for a multitude of purposes such as serving customers according to their preferences and needs or identifying market opportunities in real-time while optimizing overall marketing strategies accordingly for maximum effectiveness going forward [11].…”

Section: -Data Analytics and Insightsmentioning

confidence: 99%

Impact of Artificial Intelligence on Digital Marketing

Kar

2023

IJSREM

View full text Add to dashboard Cite

Keywords: Artificial Intelligence (AI), Machine learning (ML), Digital Marketing (DM) Research question: How does AI affect digital marketing and help digital marketers to improve their performance? Purpose: The aim of this study is to determine the Impact of AI in digital marketing by analyzing and determining how digital marketers utilize AI to improve performance. Method: This is a quantitative study by conducted survey questions and analyzed using chart. Conclusion: AI has the potential to revolutionize digital marketing by automating various processes and enabling marketers to better understand and meet customer needs. As digital marketers strive to enhance their strategies and engage with customers effectively, they recognize the value of AI in achieving their goals.

show abstract

“…The results of the study showed that the best way to apply the decision is to build 10 random decision trees. Therefore, in this study, a total of 10 randomized decision trees were built from the analyzed data because the decision trees were built in a randomized manner, and a total of 5 trials were conducted to verify the stability of the decision trees [ 23 , 24 ].…”

Section: Experimental Validationmentioning

confidence: 99%

Financial Data Analysis and Application Based on Big Data Mining Technology

Cheng

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

We provide a brief overview of the connotation and characteristics of data mining technology in the era of big data, analyze the feasibility of data mining technology in business management from the economic and technical perspectives, and propose specific application suggestions according to the content and requirements of business management. This paper describes in detail the principles and steps of using the weighted plain Bayesian algorithm and the decision tree algorithm to analyze students’ performance; firstly, we need to obtain the plain Bayesian analysis model of college students’ learning literacy in physical education and the C4.5 graduation literacy analysis model, and then use certain rules to combine the weighted plain Bayesian algorithm and the decision tree algorithm to obtain the WNB-C4.5 college students’ learning literacy analysis model. In addition, in the prediction of financial risks, the classification scheme can be used in the judgment of violation of regulations, but the most used classification scheme is the decision tree. Experiments show that the effectiveness of this scheme in data mining for financial companies is increased by 2% compared to the benchmark method.

show abstract

Research on Data Science, Data Analytics and Big Data

Cited by 15 publications

References 0 publications

WITHDRAWN: Data Science: Trends, Perspectives, and Prospects

WITHDRAWN: Data Science: Trends, Perspectives, and Prospects

Impact of Artificial Intelligence on Digital Marketing

Financial Data Analysis and Application Based on Big Data Mining Technology

Contact Info

Product

Resources

About