US Public Health Alphabet Soup Explained: What is the NIH?

The National Institutes of Health is a federal agency that funds health research such as clinical trials

Wondering what we mean by the National Institutes of Health (NIH)? In my blog post, I explain the NIH’s role in appropriating funding for research, and demystify its role in implementing federal priorities.

US Public Health Alphabet Soup Explained: What is the FDA?

The food and drug administration in each country serves as an agency to regulate medications.

Can you name categories other than “food” and “drugs” that are regulated by the FDA in the US? Read this blog post to learn what they are, and what the FDA does in the US.

GPower Case Example Shows How to Calculate and Document Sample Size

This case example shows a use case where we estimated sample size in GPower under different conditions.

GPower case example shows a use-case where we needed to select an outcome measure for our study, then do a power calculation for sample size required under different outcome effect size scenarios. My blog post shows what I did, and how I documented/curated the results.

Querying the GHDx Database: Demonstration and Review of Application

Many data scientists interested in health are looking to query the Global Burden of Disease database, also known as the GHDx

Querying the GHDx database is challenging because of its difficult user interface, but mastering it will allow you to access country-level health data for comparisons! See my demonstration!

Variable Names in SAS and R Have Different Restrictions and Rules

You need to come up with names of variables in SAS and in R, but they need to be compatible with both languages if you are running a data warehouse.

Variable names in SAS and R are subject to different “rules and regulations”, and these can be leveraged to your advantage, as I describe in this blog post.

Referring to Variables in Processing Data is Different in SAS Compared to R

When doing data processing, especially extract-transform-load (ETL) into a data warehouse, you might need to refer to the variables in your code, and it's done differently in SAS vs. R.

Referring to variables in processing is different conceptually when thinking about SAS compared to R. I explain the differences in my blog post.

Interview Preparation for Data Science Positions: Tips and Tricks

You can actually prepare for interviewing for data science positions by doing certain activities, like looking up common questions, and practicing answers.

Interview preparation for data science jobs can involve taking several simple, actionable steps to make yourself feel confident and ready to answer questions with ease. Read my blog post for my tips and tricks!

Researching Data Science Companies: How to Evaluate Your Future Employer

You should research companies offering data science job positions before scheduling an interview, because you do not want to be surprised during the hiring process.

Researching data science companies who might be your future employers, but you don’t know where to start? Read my blog post to learn my simple approach.

Answer Your Data Science Questions by Attending my Livestream Discussions on YouTube!

If you have questions about your journey toward a data science career, sometimes you can get them answered with online resources, but sometimes you do not get the information.

Answer your data science questions by attending my livestreams on YouTube and interacting in chat! Learn more about this in my blog post.

Counting Rows in SAS and R Use Totally Different Strategies

If you are a data scientist working with large datasets, you need to learn the commands to count both columns and rows in the dataset, whether you are using SAS or R.

Counting rows in SAS and R is approached differently, because the two programs process data in different ways. Read my blog post where I describe both ways.

Verified by MonsterInsights