NCLEX-RN Question Bank: Next Gen (Case Studies)
Description
Data are omnipresent these days and that what is scarce are the talent … needed to make sense of these data. It is an interesting time to be a data scientist. Because an intellectual pursuit for an educated citizen in the digital age is important, even if one does not necessarily aspire to become a data scientist. But the job requires a person, who is open-minded, easy to communicate enough to analyze, interpret, and present data in meaningful, understandable ways across domains – a wide span involves science, business, education, government agencies, and industries of all variety.
We’re helping you explore the different ways data science is used in healthcare! We have tailored a bootcamp for your ideal career path on your health data scientist journey, with further focus on specializations in adult-gerontology primary care, family health, pediatric primary care, psychiatric/mental health, and women’s health in the future.
The course is designed to provide rigorous healthcare training and essential nursing skills needed to manage and analyze health science data to address important questions in population medicine. This bank covers NCLEX-RN next-gen case studies collected online by a self-developed artificial intelligence algorithm.
Don’t worry if you have no experience in nursing or medical knowledge, you will learn everything you could pass the exam with a real question bank (if applicable).
Who this course is for:
- Best for students who need a deeper look at the NCLEX® examination subject areas and more study resources
Instructor
- 4.2 Instructor Rating
- 16 Reviews
- 2,036 Students
- 8 Courses
My name is Chunlei Tang. Currently, I am a research associate at Harvard. Prior to Harvard, I worked as a revenue officer in Shanghai, China, for over ten years, responsible for corporate tax-related management. I also serve as the global Treasurer Co-Chair for ACM-W (Association for Computing Machinery's Council on Women).
My background is in computer science, and I graduated from Shanghai Jiao Tong University with a bachelor's degree. I then got a master's degree in software engineering and a Ph.D. in Computer and Software Theory at Fudan University.
My research occupies a unique place in data science because (1) I have experience in both business operations and revenue management, and (2) I conduct my research across fields (e.g., stock markets and healthcare) instead of staying in computer academics. Compared with my peers, I can easily hear the needs and requirements of stakeholders in other domains.
Also, I spent some time on basic research in data science. In my mind, data is resources, assets, and capital. A complete picture consists of three elements: "data, technology, and application," which all work toward augmenting people's decision-making by following data as it crosses borders. I wrote two books independently in 2015 and 2020, respectively. I am the author of The Data Industry: The Business and Economics of Information and Big Data (Wiley; 2016) and Data Capital: How Data is Reinventing Capital for Globalization (Springer; 2021). I am excited to see that Springer's book Data Capital is selling very well, reaching nearly 5,000 digital copies now.
I'm very interested in converting data-driving forces into productivity that can serve society. I'm also good at data product designing and social media data mining.