The Biostats degree was a lot more theoretical, requiring knowledge of calculus and linear algebra. We had to do proofs. We weren't shown how to Interest in biostatistics but not much math background - Reddit[E] why is biostats more difficult than other STEM courses? : r/statisticsHow is biostatistics different from "plain" statistics and why - RedditWhich graduate program is more difficult: a MPH in Biostatistics or a More results from www.reddit.com
A real biostatistics course usually contains some elements of calculus, and this is what causes problems for many students. Think about it. If a significant number of students in today's society struggle with basic algebraic problems, they're also going to struggle with the calculations in biostatistics.
It really depends on how math-orientated you are. Biostats can be difficult even for those who aren't math-oriented at all, or it can be (to a certain extent) not so hard for people who are somewhat math-orientated as Epi people tend to be.
Abstract
As a discipline that deals with many aspects of data, statistics is a critical pillar in the rapidly evolving landscape of data science.
The increasingly vital role of data, especially big data, in many applications, presents the field of statistics with unparalleled challenges and exciting opportunities.
Statistics plays a pivotal role in data sci.
Closing Remarks
Statistics as an ever-growing discipline has always been rooted in and advanced by real-world problems.
Statisticians have played vital roles in the agricultural revolution, the industrial revolution, the big data era, and now in the broad digital age.
Statistics cannot live successfully outside data science, and data science is incomplete without .
How should research in statistics and Biostatistics respond to the major challenges?
We believe that research in statistics and biostatistics should respond to the major challenges of our time by keeping a disciplinary identity, promoting valuable statistical principles, working with other quantitative scientists and domain scientists, and pushing boundaries of data-enabled learning and discovery.
Ten Research Areas
1.
Quantitative Precision-X
