This is a list of frequently asked questions. Have a question not seen here? Please email us at data@fredhutch.org
We support in-person and online attendance (through Teams) of the courses.
We have a number of learners who learn online, and switch between online and in-person. We do have a limited number of in-person seats, so we do try to account for that in course registration. Due to the popularity of the badging program, we also limit registration so our instructors can live sane lives.
Our preference is that you try to attend the courses synchronously, as a lot of good questions are brought up by other learners that can help in your learning, and we find that learning together socially can accelerate your learning process.
When registration is finalized for a course, we will be sending out course invites via Outlook.
If the course is full, you will be redirected to a waiting list. If a slot becomes available, we will contact you.
We will contact you when the next time the course is given and give you preferential registration for the course.
If you have enrolled for the previous intro courses (Intro Python/R), you will have preferential registration for the following courses.
Please email us at data@fredhutch.org so we can understand your level of knowledge. Based on that discussion, we will allow enrollment for the course.
Courses are multiple sessions focused on Data Science with both in-class and homework exercises. They have a badge associated with them.
Workshops are single sessions meant to focus on smaller topics. Some workshops are part of a sequence (such as Scalable Computing or Reproducible Research, but you can take the workshops separately.
For new Data Science learners, this can seem like a difficult decision. Here's our advice:
The first thing you should ask is: what language is your group using? If they focus on R, then start there. If they focus on Python, then focus on Python.
If you are learning on your own, they both are strong languages. If you are focusing on data analysis and statistical analysis, R can be easier to understand. For machine learning, Python is stronger in terms of machine learning support. There is the ability to work with both languages.
The R Community is very welcoming of beginners, but the Python Community is definitely catching up.
Ultimately, what matters is your own tenacity and curiosity. Once you have the fundamentals of one language mastered, then you can pick up the other one.
We do not recommend taking both Python and R courses at the same time, as they are just different enough that it will frustrate your learning.
We have started the Credly badging program as a way to show your mastery of data science concepts. Once you receive a badge, you can share in in various places, such as LinkedIn.
It is not an accredited program (Fred Hutch is not an accredited university), but many learners find it useful because it keeps them accountable for learning.
Requirements for each course vary, but it is usually completing an number of assignments.
We currently offer badges for the following courses:
We highly recommend that you sign up for our newsletter! We post about upcoming courses, workshops, and related events such as Data Deep Dives, Co-Working, and Learning Communities.
You're not alone! As a rule of thumb: