It’s that time of year again. Students are flooding back onto campuses, resuming their courses of study or embarking on a new path.
Research runs similar lines – following up and taking new directions. And sometimes it leaves me wishing I had learned more _______ as a student. There’s hope for those who are still students – and in the digital age with things like free online courses and CodeAcademy, there might even be hope for the rest of us. Here are a few on my list:
- Statistics – really, why isn’t this required for every science degree, from BS to PhD? We tend take p-values as a statement of truthiness of research results, but how well do most of us understand them?
- More calculus – If you get into any sort of quantitative biology, derivatives and integrals are going to haunt you.
- Linear algebra – Goes along with ‘more calculus’ and the realization that computers operate in matrices.
- Computing – Along with previous 2, if you’re doing quantitative analysis, chances are you’re going to find yourself entrenched in R and/or MATLAB. You might also find that you’re running, editing, or writing code, so speaking the language (e.g. Python, which seems to be taking over bio applications) is be a huge benefit.
Ten years ago, as I was finishing up my BS, I never imagined how much some of these things would be infiltrating my life. From my view, these things certainly are not going to become less useful in the future – of my research or the field.
What’s on your list?
Ever on & on 
A formal biophysics course, a course in the fundamentals of my branch of struct bio, a programming course and a methods course. Stats were always a bitch for me, but yes, I wish I’d taken more stats.
Things like electron microscopy, molecular biology, immunohistochemistry, maybe. I learned fiddly bench skills, but mine were mostly related to electrophysiology.
Statistic and programming definitely. A better bioinformatics course maybe, but I also much more microbiology.
Stats. Oh, how I wish I had taken stats.
That’s pretty much my list. I actually signed up to take Intro Compsci pass/fail, but as you can imagine that ended with me not going much and dropping. I don’t know why I stopped after taking math after I finished the required courses. I think I just wanted a bit of a break to do stuff in my major and work in labs.
Actually I kind of wish I would have considered doubling in applied math more seriously. I didn’t realize how quantitative my work would end up being.
I didn’t realize how quantitative my work would end up being.
I’m with you, Bashir. Oh how I long for the days when all my quantification could be done using Excel… 😛
Picking up from some Twitter chatter, I have to say, a course on spotting data manipulation sadly might be in order…
I second everything you have. For undergrad, I would also add a research methods course. My psychology major required it, but one for biology would have been really helpful!
If as far as a person goes in science is a bachelor degree, why doesn’t even that teach the basics?
As a journalist, I’d say people really need to learn better research techniques. I’d like to say most people who are studying science learn this, but when I was finishing up my college degree, I overheard a biology undergrad who was all, “why should I read a research paper? The info is on Wikipedia.”
I had an entire course on how to research, analyze and vet source material and it was extremely valuable. When it comes to research, everyone should be a skeptic.
For myself… more stats. Why was this not a requirement?! Its frustrating and I think stats would have been more useful than the two years of Calc for understanding the basic fundamentals of science and research.
Kallen,
First apologies for the delay in replying!
Second, agree on the research methods class. A course required for my undergrad degree was called “Intro to laboratory methods”, but it was really about the stuff you do before a project and in writing it up, so a major focus on finding original source material. Being in a chem department, we had to learn how to use Chemical Abstracts (yes, the old print edition) and the e-version SciFinder scholar.
Stats for sure!!! I have been laboring over how to handle a set of data for two months now. I’ve finally come to the conclusion that I can’t do anything statistical until I have more data, which I have to coordinate with a collaborator on the other side of the country!!!
Every stats book I’ve read says you should determine how you’re going to handle/analyze the data before you collect it. I don’t think anyone does that and sometimes its just not practical, but I’m starting to see the wisdom in it and I will definitely think about it more NEXT time!
Also, read a paper today in which simple calculations (percentages!) were WRONG… which makes me question their stats, especially since they didn’t look quite right. So, I second the course on how to spot data manipulation as well. Ugh! Science!
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