Five years into a statistics program and I’m still shocked at my decision to go down this path. Switching fields going into my PhD was daunting and I’ve spoken before about how was convinced to turn Full Stats Stud during my visit to the programs recruitment day. Since then I’ve helped out with each batch of prospective students as they too are welcomed to Happy Valley and introduced to what earning a PhD in Statistics here could mean for them. I want to share these experiences and a little bit of what’s going on behind the curtain for our readers. If you are be going through a similar process, here’s one look at what a recruitment day looks like and what to expect. We’d also love to hear from others about their experiences as this process varies GREATLY between departments and universities!
In PSU Statistics, we invite domestic students that already have offers to our PhD program to come to campus for a day of information and fun. These students already have been accepted and we hope to convince them that they’ll find a great environment for spending the next 4-6+ years. Not all recruitment events happen after offers go out, and that could make for a more stressful visit. Even visiting a department you do have an offer for can be extremely intimidating. I know I am STILL intimidated sometimes! But one of the most important tips I have is to remember that you have worked hard and earned a spot! They should be working hard to impress you and be showcasing what their program has to offer.
It’s that time again friends. We are bringing you another installment in the Amazing Besties National Park Road Trip series. This was one of the most epic friend adventures either of us have ever had, so if you like best friend hijinks these posts are for you. If you’re into pretty photos of natural wonders, you have come to the right place! 10 states. 9 National Parks and 1 National Monument. One summer of fun!
Want to catch up? Check out the rest of the series here.
HELLO FRIENDS! It has been a long, tough year since Rachel and I have posted here on Sweet Tea, Science. We’ve tried to keep up with people via Twitter (Mer’s, Rach’s, and the STS accounts) and Instagram (again we allhaveone!) but we started feeling that blogging itch once more, so we’re back. We wanted to start with updates on our academic and personal lives, because this blog is about the science journeys of two actual living people. We’ve had some highs and lows. Some heart-breaking tragedies and some magical love-filled unions.
This time last year I was enjoying the perks of summer in Colorado while exploring the in’s and out’s of working in an industry setting. I’ve had many summer adventures/internships/travels, but any work I’ve done has been 100% within the realm of academia. However, via a connection made through my advisor at the big statistics conference (Joint Statistical Meeting or JSM), I landed an internship at an environmental consulting agency. The further along I get in my studies the more certain I am I’d like to explore career options outside of academia; so this was an amazing opportunity.
I worked with Neptune & Co., a small but growing environmental consulting company focusing on environmental decision making though quality assurance, data science, and risk assessment. As an intern, I helped the other statisticians working on a project modelling the future (millions of years future!) risks and impacts of nuclear waste storage around the US. I loved being able to learn about an important issue from experts in various fields while applying what I’ve been learning over the past few years in my PhD studies.
We focused on the biotic impact portion of the models and worked to use what precious few data are available to create some distributions for variable such as: plant root shape,root depth, burrow depths, etc. All of these factors can potentially bring up buried contaminants if the burrows or roots venture too deep. It’s important to represent these as distributions (e.g. a Normal distribution LINK) rather than a point estimate (e.g. a mean or median) because it allows for more representation of uncertainty in the model.