Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

The volumes of genetic, biodiversity, and environmental data that can be obtained from individual studies are growing at an impressive rate, as are the public repositories sharing these data. Such ...

The design of your study, the research questions you’ve posed, and types of data you’ve collected (e.g., quantitative, qualitative) are important considerations in determining the data analysis and ...

One drawback of working for so long in the data industry is that I often misjudge what people think about when they think about data. Particularly, I've observed a common misunderstanding about ...

In today’s market, product, process, and service quality is a necessity for a competitive edge in any business. Consumers and customers expect (and demand) high-quality products and services from ...

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out. Novice data scientists sometimes have ...

In the realm of data analysis, the advent of artificial intelligence has been a game-changer. One such AI tool that has revolutionized the field is ChatGPT. This article will delve into how to utilize ...

In this technology cast study, we’ll examine MeritData, Inc., a leading big data analysis technology and service provider in China. The company’s product is called Tempo, a big data platform that has ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...