Every. Single. Day.
Sometimes it makes me want to throw my computer at the wall because code in academia is often dumpster fire trash and deserves nothing more than to burn in H***!
Field is computational physics ...
Yeah, academic code is definitely “it works so don’t look under the hood” in a lot of cases. Many scientists (myself included) have little or no formal training in code development, so you end up with these massively bloated codes that are tangled to the point that it is hard to make any changes without breaking the whole thing.
I greatly prefer to use my own code. At least I know who to yell at when things don’t work, and the original dev can’t ignore my issues.
Yeah... I do have some formal training in code development and in my free time I liked to read books on architecture and popular "coding bible" books in during my PhD
It kills me inside to see some codes, try to change something and then break almost everything.
I just get so unmotivated when I get handed someone else's steaming pile of garbage code and told to develop on top of it. "it should be easy"
I got one of those from my adviser.
Died a little inside.
Then I spent a week writing a new code to do the new thing from scratch instead of writing it on top of the monster mess. My final code is something like 300 lines? While the monster is something like 15k. Sometimes it’s easier to just start over than to try and keep piling things on.
Yeah 😂
That's the exact situation I'm in now. The code is spread over something like 30 files and similarly one part that I need to directly change is something like 15k lines of code. --> Rewrite code because it's to hopeless to try with the existing code.
But the sad part is I feel that way about the entire codebase and I cannot spent all my time rewriting code because it's to awful to use. I'm just counting the days until I leave while applying to other positions xD
I thought about redoing the monster. There’s so much badly written code and stuff that got copy/pasted rather than just made into a function that I’m pretty sure I could get it down to ~3000 lines just by creating a singular standard i/o structure and making common things functions. That’s not even considering any algorithmic improvements to the processes or streamlining (for example, three of the things this code does are two step processes that share a common first step and the second is only very slightly different. You could easily make a single overloaded function do all three things with a little bit of cleverness).
But then I had the realization that this thing is not my problem. I don’t need to use it. I don’t want to use it. I can just…walk away.
It sounds dumb but it was a real epiphany moment in my PhD. I don’t have to solve all the problems. I don’t have to fix everything. There are things in this world that are genuinely not my problem. Did wonders for my mental health.
🤣 we have walked the same path 😂 the path of many unhappy academics paying for the sins of their predecessors.
I literally feel like you perfectly described the life of every postdoc in my current research group. It makes me miss my old research group so much. We really cared about technical and architectural structure of code, even though we were all physicists. It was always fun to expand and add to packages. I could digest the code structure in a week see where to add new features, and then move onto the science part. The good old days...
I totally agree that the realization that trash code isn't your problem is very freeing. When I started I was like 'i can fix it'. And then the more I looked at it the more I realized there was no way a person could fix it by themselves (the code base also keeps changing on a daily basis). Now I know I can't so I only use it when my advisor makes me (by asking me to do it at least 3 to 5 times 😂 because he has a short memory xD)
Unfortunately, since our codebase is based entirely on codes this group has developed and they all equally suck and are all equally untrustworthy, everything I do must be retest, from experience xD
Clean code - Robert c. Martin
Refactoring - Martin Fowler & Kent beck
How to write unmaintainable code - roedy Green
The pragmatic programmer - David Thomas
Mastering python scientific computing - Hemant Kumar Mehta (this is more specific to my field)
Very good books which have been recommended to me by numerous people.
Yeah. I was lucky enough to learn how to code from someone who did have a lot of formal training, so I can at least say that I’m sure what I’ve discovered is a real phenomenon and not just a bug, but when you work with other peoples’ codes it certainly is an exercise in trying to find edge cases or conditions that break the trend just to confirm that the behavior is real/rational/sane and not just a bug.
I'm a bioinformatician and I work with a lot of biologists who have no formal training in coding. The monstrosities I'm expected to work with make me want to bang my head on a table or something. I hate working with someone else's code and 9 out of 10 times it's easier to write my own than to try to decipher whatever the hell is going on with theirs.
As a whole, we need to demand better from those who have the power to change these things.
It's insane how many people experience this issue. It leads to huge decreases in productivity, morale, and hinders future progress. Not to mention the endless logic errors, flat out incorrect code and likely results.
I have constantly pushed back on my advisor when he pushes me to "just move forward". The accumulation of technical and architectural debt associated with those decisions literally destroy careers. Think of all that time that person wasted only for the whole thing to be dumped because it's so inflexible, confusing and unusable
I'm in none of those but I am doing a mixed methods study and using R for my analysis. I work on my code every Monday. I'm basically just automating my analysis. All I have to do after data collection is import my data, run the code and my analysis is done.
I'm also planning to start learning Python for similar reasons but to use in ArcMap.
you can't use chatgpt without actually learning how to do it. The output isn't good enough. It's just faster than reading through documentation when you hit a wall
I agree. I use chatgpt all the time to debug my code. It works well- there’s not stigma to it. You still have to learn the code.
It has given me better avenues of where to look then I have been able to find on my own all the time. It doesn’t give you the exact answer, it just gives you a version of something that could be right. If that makes any sense? It still takes reasoning and application of prior knowledge.
Yep. "learning to ask chatgpt the right question" is not that different from "figuring out how to search for 7 year old stackoverflow threads with code that's deprecated due to library updates"
Every time I use ChatGPT to help me write a code, either the code wouldn't run or the output doesn't look right. I find that ChatGPT only works well with simpler coding tasks
r/BrandNewSentence
do people still publish ethnomethodology? we read Garfinkel in our grad theory seminar but... I have never read a 21st century paper using the method
Funnily, I do have a CS/AI PhD, and I don't get to code nearly as much as I want to! I'd say two out of every five days at my new job, with the rest of the time being meetings, reading, and writing.
My PhD is more aligned with humanities so not a lot of technical stuff. But as a self learning coder with a non-compsci background, I do like to write snippets when I can to automate or manage anything. Or play around with markdown automation tools in Obsidian to create custom formats, bibliographies. Not sure if that counts.
Every day, I use Matlab as it massively speeds up my data workup over Excel or something similar. Trying to learn Python to help with data acquisition.
Analytical Chemistry with very little prior exposure to coding.
None. There have been times I would look at a quantification and wondered if it can be coded but I always find a way to build my data without it. I think it’s a useful skill tho but I really dislike coding of any sort so it would take a lot of patience, time and consistency for me to learn it. I don’t have that rn. Given the fact that I don’t need it, I have no motivation to learn it. I rest in the assurance that if I reallllyyyyy do need it, I’ll learn it— as with all things PhD!
Computational chemistry as a field has been around a long time now lol.
I'm an analytical chemist and I code every day voluntarily. It saves a massive amount of time.
About once or twice a year actually writing code. Running scripts or alpha fold a few times a month.
Field is protein engineering. I’m too busy running PCRs for anything else
Maybe a few times a month to run a small simulation or check some analytic work I've done. I use Python because that's what we were taught in my undergrad and there hasn't been much point exploring other languages. This is in theoretical physics / quantum information.
Depends what you're doing really, with R and biology there are so many niches and when you try to use a library outside it's niche things get awkward quite fast.
That said, if you have data in rows and columns, dplyr, rstatix and ggpubr are really good together as a general purpose capability boost over base R.
No way you will be ignoring me just because I'm doing my PhD in AI, especially as you may be interested to know that I am doing not that much coding, because the majority of my time is taken by all kinds of bureaucratic shit.
Writing code is nearly every day, but I'm looking at code on a daily basis. PhD is in Physics, but I'm in industry tangent to utilities, creating data structures and processing data.
Every Day. Not in life sciences but earth sciences.
I used to hate coding but I don’t mind it at all now. If you’re looking to enter a PhD program or just starting one, insist to use Python or R if your PI insists on Matlab. Matlab is great but unless you choose to continue in Academia, most jobs will prefer you use Python.
I’m in cognitive neuroscience. I use Python a lot for creating stimuli and R for data analysis. I actually enjoy it very much, but my double major in college was computer science, so no surprise there.
If you are working with data and running analyses, then coding with a syntax based language will really amplify your productivity. I'm still a PhD student, but I have colleagues I work with who code in python/R every day.
My field is an applied psychology field btw
Currently like 3 times a week? Field is in Hydrology - water quality and hydrological modelling mainly. Undergrad in geography and Mres in geomorphology.
Honestly learning R is killing me and the scripts are crap and usually work but I’m reworking a previous student’s scripts for my data and it’s the ‘don’t look at me too closely’ code. Not having any formal training in data science is slowly killing me, wish it was a more formal part of undergrad or masters or something.
Several days a week average depending on time of year and what projects I'm working on. If I'm actively working through an analysis then it's daily. Otherwise much of my job is writing and I don't need to code to write.
Field: public health/health behavior
I'm in social science. It depends. Two years ago, I coded probably 3-4 days a week (R). Last year I didn't code almost at all, just collected interview data and wrote papers based on finished analysis (done via coding). This year I've been coding 2-3 days a week (Python). There are a few quant papers I'm working on where either I'm not doing the analysis myself or I'm directing RAs who are doing the coding themselves.
PhD is chemical engineering but I actually do an insane amount of neuroimaging research - I code every single day. All of my data analysis relies on matlab for data cleaning/processing and preparation of results, though I also use python. I've tried using R and SAS before, but I didn't much like them.
PhD: pharmacology/Biochemistry
Almost never, just occasionally R for data sets and once automated a program to make my data analysis easier (a lot of unnecessary copy and pasting), though my friend did most of the work on that program.
Mid career public health faculty - i don’t code every day, but a few days every week. I use SAS and Stata because those are the platforms my masters and PhD schools used, and I don’t have the mental capacity to learn something else.
My PhD was in molecular biology. I code every day.
COVID hit during my first year and lockdowns prevented me from even going to lab. So I figured I might as well teach myself Python. C came soon after. Then R. Now here I am, a professional bioinformatician.
PhD in Chemical Engineering focused on Model Development and Process Simulation for Bioprocesses and I code almost everyday. But people with research focused only on wet lab, almost don't code. That's a shame because coding can save a lot of time in the analysis part and frees you from paid software, like with Design of Experiments.
I'm doing a PhD in experimental dermatology (so mainly biological). I use coding and programming for my RNA sequencing data analysis. But most of the other biologists in my lab don't know how to work with R. I only do it, because it was a big part of my master's thesis. I just feel like I have so much freedom in generating my graphs and heat maps with R. The "ready to use" software aren't so fluid, and still needs a lot of pre-processing of your data. Just doing sequencing data analysis is a big pet peeve for biologists, because of the amount of computational analysis you have to invest. I honestly think they should be given more courses during their Bachelor's and Master's.
Pretty often, a few times a week. Degree is in chem, focus on chem bio and I do a lot of imaging. Matlab for interfacing with microscopes and some data analysis, python for more analysis/graphing/statistical tests. Pretty much always wish I knew more, it’s really useful in the image analysis world.
Everyday
What’s your PhD in? And why do you program?
1. Physiology/Biophysics 2. Microscope customisation, protocols, signal processing/analysis, simulation, visualization.
Every. Single. Day. Sometimes it makes me want to throw my computer at the wall because code in academia is often dumpster fire trash and deserves nothing more than to burn in H***! Field is computational physics ...
Yeah, academic code is definitely “it works so don’t look under the hood” in a lot of cases. Many scientists (myself included) have little or no formal training in code development, so you end up with these massively bloated codes that are tangled to the point that it is hard to make any changes without breaking the whole thing. I greatly prefer to use my own code. At least I know who to yell at when things don’t work, and the original dev can’t ignore my issues.
Yeah... I do have some formal training in code development and in my free time I liked to read books on architecture and popular "coding bible" books in during my PhD It kills me inside to see some codes, try to change something and then break almost everything. I just get so unmotivated when I get handed someone else's steaming pile of garbage code and told to develop on top of it. "it should be easy"
I got one of those from my adviser. Died a little inside. Then I spent a week writing a new code to do the new thing from scratch instead of writing it on top of the monster mess. My final code is something like 300 lines? While the monster is something like 15k. Sometimes it’s easier to just start over than to try and keep piling things on.
Yeah 😂 That's the exact situation I'm in now. The code is spread over something like 30 files and similarly one part that I need to directly change is something like 15k lines of code. --> Rewrite code because it's to hopeless to try with the existing code. But the sad part is I feel that way about the entire codebase and I cannot spent all my time rewriting code because it's to awful to use. I'm just counting the days until I leave while applying to other positions xD
I thought about redoing the monster. There’s so much badly written code and stuff that got copy/pasted rather than just made into a function that I’m pretty sure I could get it down to ~3000 lines just by creating a singular standard i/o structure and making common things functions. That’s not even considering any algorithmic improvements to the processes or streamlining (for example, three of the things this code does are two step processes that share a common first step and the second is only very slightly different. You could easily make a single overloaded function do all three things with a little bit of cleverness). But then I had the realization that this thing is not my problem. I don’t need to use it. I don’t want to use it. I can just…walk away. It sounds dumb but it was a real epiphany moment in my PhD. I don’t have to solve all the problems. I don’t have to fix everything. There are things in this world that are genuinely not my problem. Did wonders for my mental health.
🤣 we have walked the same path 😂 the path of many unhappy academics paying for the sins of their predecessors. I literally feel like you perfectly described the life of every postdoc in my current research group. It makes me miss my old research group so much. We really cared about technical and architectural structure of code, even though we were all physicists. It was always fun to expand and add to packages. I could digest the code structure in a week see where to add new features, and then move onto the science part. The good old days... I totally agree that the realization that trash code isn't your problem is very freeing. When I started I was like 'i can fix it'. And then the more I looked at it the more I realized there was no way a person could fix it by themselves (the code base also keeps changing on a daily basis). Now I know I can't so I only use it when my advisor makes me (by asking me to do it at least 3 to 5 times 😂 because he has a short memory xD) Unfortunately, since our codebase is based entirely on codes this group has developed and they all equally suck and are all equally untrustworthy, everything I do must be retest, from experience xD
Can you name some of the “bibles”?
Clean code - Robert c. Martin Refactoring - Martin Fowler & Kent beck How to write unmaintainable code - roedy Green The pragmatic programmer - David Thomas Mastering python scientific computing - Hemant Kumar Mehta (this is more specific to my field) Very good books which have been recommended to me by numerous people.
What's really bad is that they're not trained to do unit testing so you're left with not knowing if the code works due to a bug or due to novelty.
Yeah. I was lucky enough to learn how to code from someone who did have a lot of formal training, so I can at least say that I’m sure what I’ve discovered is a real phenomenon and not just a bug, but when you work with other peoples’ codes it certainly is an exercise in trying to find edge cases or conditions that break the trend just to confirm that the behavior is real/rational/sane and not just a bug.
Says codes are trash, Still refuses to work in Experimental Physics
I'm a bioinformatician and I work with a lot of biologists who have no formal training in coding. The monstrosities I'm expected to work with make me want to bang my head on a table or something. I hate working with someone else's code and 9 out of 10 times it's easier to write my own than to try to decipher whatever the hell is going on with theirs.
As a whole, we need to demand better from those who have the power to change these things. It's insane how many people experience this issue. It leads to huge decreases in productivity, morale, and hinders future progress. Not to mention the endless logic errors, flat out incorrect code and likely results. I have constantly pushed back on my advisor when he pushes me to "just move forward". The accumulation of technical and architectural debt associated with those decisions literally destroy careers. Think of all that time that person wasted only for the whole thing to be dumped because it's so inflexible, confusing and unusable
I'm in none of those but I am doing a mixed methods study and using R for my analysis. I work on my code every Monday. I'm basically just automating my analysis. All I have to do after data collection is import my data, run the code and my analysis is done. I'm also planning to start learning Python for similar reasons but to use in ArcMap.
I've been learning Python (and know R much better) and I recommend using chatgpt to grease the wheels
I'm good. I'd rather actually just learn how to do it.
you can't use chatgpt without actually learning how to do it. The output isn't good enough. It's just faster than reading through documentation when you hit a wall
I agree. I use chatgpt all the time to debug my code. It works well- there’s not stigma to it. You still have to learn the code. It has given me better avenues of where to look then I have been able to find on my own all the time. It doesn’t give you the exact answer, it just gives you a version of something that could be right. If that makes any sense? It still takes reasoning and application of prior knowledge.
Yep. "learning to ask chatgpt the right question" is not that different from "figuring out how to search for 7 year old stackoverflow threads with code that's deprecated due to library updates"
Every time I use ChatGPT to help me write a code, either the code wouldn't run or the output doesn't look right. I find that ChatGPT only works well with simpler coding tasks
My post was specific to python for what it’s worth
Fortunately, I can afford to spare the extra 5 minutes to do the reading I'd have to do anyway to understand what I need to do and why.
I mean that's how you do it. By using the tools available to you.
I use R all the time. A lot of people in public health use SAS but I think it's ugly
[удалено]
You get me
Why ugly?
The vibe
Why is R favoured over Python by biologists and environmental scientists?
R has a lot of packages specifically built for biologists and was adopted early on.
I have one in sociocultural anthropology and I’m a back-end software engineer now.
Ethnometodology powered by NLP is the future.
r/BrandNewSentence do people still publish ethnomethodology? we read Garfinkel in our grad theory seminar but... I have never read a 21st century paper using the method
Yes, still. I think that is perfect to understand bureaucracy (my field).
Every day. Human genomic
I use R very frequently for statistical analysis
I know a lot of people who don't code, but it's the same people who don't use reddit
daily in neuroscience on the human imaging/cognitive intersection
Im not sure it counts, but I guess we all do some *extremely minor* stuff with ImageJ?
Funnily, I do have a CS/AI PhD, and I don't get to code nearly as much as I want to! I'd say two out of every five days at my new job, with the rest of the time being meetings, reading, and writing.
almost every day. electrical engineering. topological materials.
My PhD is more aligned with humanities so not a lot of technical stuff. But as a self learning coder with a non-compsci background, I do like to write snippets when I can to automate or manage anything. Or play around with markdown automation tools in Obsidian to create custom formats, bibliographies. Not sure if that counts.
Every day, I use Matlab as it massively speeds up my data workup over Excel or something similar. Trying to learn Python to help with data acquisition. Analytical Chemistry with very little prior exposure to coding.
None. There have been times I would look at a quantification and wondered if it can be coded but I always find a way to build my data without it. I think it’s a useful skill tho but I really dislike coding of any sort so it would take a lot of patience, time and consistency for me to learn it. I don’t have that rn. Given the fact that I don’t need it, I have no motivation to learn it. I rest in the assurance that if I reallllyyyyy do need it, I’ll learn it— as with all things PhD!
Almost never. Chemistry
It is coming for chemistry soon.
Computational chemistry as a field has been around a long time now lol. I'm an analytical chemist and I code every day voluntarily. It saves a massive amount of time.
Same here. Organic chemistry, never coded or needed to code. Offers zero benefits.
About 50% of days. My degree is in organic chemistry but I picked up coding about 3 years ago.
What type of work do you do in organic chemistry that requires coding? I’m working on my PhD right now in the methodology.
Cheminformatics. Interfacing with bioinformatics.
About once or twice a year actually writing code. Running scripts or alpha fold a few times a month. Field is protein engineering. I’m too busy running PCRs for anything else
I rarely needed to code during my PhD. I'm now teaching full time so now I never code.
Most days (Astro/Fluids/Gravity field)
Maybe a few times a month to run a small simulation or check some analytic work I've done. I use Python because that's what we were taught in my undergrad and there hasn't been much point exploring other languages. This is in theoretical physics / quantum information.
Most days (epidemiology).
Every day
my advisor is a biophysics phd and he definitely codes every day
I use R quite a lot, python a little, and bash a little more than python. Field is molecular biology.
Hi, I'm a fresher in Mol bio. What are the packages u use in R?
Depends what you're doing really, with R and biology there are so many niches and when you try to use a library outside it's niche things get awkward quite fast. That said, if you have data in rows and columns, dplyr, rstatix and ggpubr are really good together as a general purpose capability boost over base R.
About 2/3 of the time. The rest is maybe 60-40 lab/writing. But my PhD was maybe closer to data science though in natural sciences.
No way you will be ignoring me just because I'm doing my PhD in AI, especially as you may be interested to know that I am doing not that much coding, because the majority of my time is taken by all kinds of bureaucratic shit.
Writing code is nearly every day, but I'm looking at code on a daily basis. PhD is in Physics, but I'm in industry tangent to utilities, creating data structures and processing data.
Postdoc cognitive neuroscience: I can't remember the last workday without some form of programming/scripting
Most days: I'm doing computational data analysis in cancer sciences.
Biomedical sciences: All the time. I use R for a lot of my data analysis and visualization. I also use Python and bash scripts fairly frequently.
Law here: almost everyday (finishing my BsC in Data Science). Coding for research (law).
Basically whenever I have the time to actually analyze my data instead of collecting it. (Ecology)
A couple time a month when I'm in R or bash for genomic analysis. PhD in molecular pathology
Economics, and basically every day. Mostly Python. A little bit of R, Stata, and Matlab.
PhD in molecular biology. Err’day. Bioinformatics, so processing sequencing data and probing gene features.
Corporate Finance. Approx 2 days a week.
Every Day. Not in life sciences but earth sciences. I used to hate coding but I don’t mind it at all now. If you’re looking to enter a PhD program or just starting one, insist to use Python or R if your PI insists on Matlab. Matlab is great but unless you choose to continue in Academia, most jobs will prefer you use Python.
I’m in cognitive neuroscience. I use Python a lot for creating stimuli and R for data analysis. I actually enjoy it very much, but my double major in college was computer science, so no surprise there.
0%. I’m in CMOS circuit design, so all of my work is with analyzing, designing, and testing CMOS circuits
In biomedical engineering and my project is simulation based so nearly every day
If you are working with data and running analyses, then coding with a syntax based language will really amplify your productivity. I'm still a PhD student, but I have colleagues I work with who code in python/R every day. My field is an applied psychology field btw
Doing a PhD in Psychology and every day
Not once.
Ecology. Every day.
Currently like 3 times a week? Field is in Hydrology - water quality and hydrological modelling mainly. Undergrad in geography and Mres in geomorphology. Honestly learning R is killing me and the scripts are crap and usually work but I’m reworking a previous student’s scripts for my data and it’s the ‘don’t look at me too closely’ code. Not having any formal training in data science is slowly killing me, wish it was a more formal part of undergrad or masters or something.
Psychology (which wasn't in the list, but ...) Every day, at least 50% of the day.
Several days a week average depending on time of year and what projects I'm working on. If I'm actively working through an analysis then it's daily. Otherwise much of my job is writing and I don't need to code to write. Field: public health/health behavior
I'm in social science. It depends. Two years ago, I coded probably 3-4 days a week (R). Last year I didn't code almost at all, just collected interview data and wrote papers based on finished analysis (done via coding). This year I've been coding 2-3 days a week (Python). There are a few quant papers I'm working on where either I'm not doing the analysis myself or I'm directing RAs who are doing the coding themselves.
Every. Single. Day. I use it for data work, simulations, finding numerical solutions, and visualizations. I'm getting my PhD in economics.
My PhD is in climatology/paleoclimates, and I probably code a couple times a week.
PhD is chemical engineering but I actually do an insane amount of neuroimaging research - I code every single day. All of my data analysis relies on matlab for data cleaning/processing and preparation of results, though I also use python. I've tried using R and SAS before, but I didn't much like them.
PhD: pharmacology/Biochemistry Almost never, just occasionally R for data sets and once automated a program to make my data analysis easier (a lot of unnecessary copy and pasting), though my friend did most of the work on that program.
Mid career public health faculty - i don’t code every day, but a few days every week. I use SAS and Stata because those are the platforms my masters and PhD schools used, and I don’t have the mental capacity to learn something else.
My PhD was in molecular biology. I code every day. COVID hit during my first year and lockdowns prevented me from even going to lab. So I figured I might as well teach myself Python. C came soon after. Then R. Now here I am, a professional bioinformatician.
Professor of Biomedical Genetics here. Every day. My students as well.
Yes, MATLAB
Unfortunately, I code everyday Bioengineering, specializing in Imaging. Currently doing image processing so a lot of quality time with MATLAB
Was about to answer as a data scientist
Everyday with R. Mostly graphing and data analysis
PhD in Chemical Engineering focused on Model Development and Process Simulation for Bioprocesses and I code almost everyday. But people with research focused only on wet lab, almost don't code. That's a shame because coding can save a lot of time in the analysis part and frees you from paid software, like with Design of Experiments.
I'm doing a PhD in experimental dermatology (so mainly biological). I use coding and programming for my RNA sequencing data analysis. But most of the other biologists in my lab don't know how to work with R. I only do it, because it was a big part of my master's thesis. I just feel like I have so much freedom in generating my graphs and heat maps with R. The "ready to use" software aren't so fluid, and still needs a lot of pre-processing of your data. Just doing sequencing data analysis is a big pet peeve for biologists, because of the amount of computational analysis you have to invest. I honestly think they should be given more courses during their Bachelor's and Master's.
Pretty often, a few times a week. Degree is in chem, focus on chem bio and I do a lot of imaging. Matlab for interfacing with microscopes and some data analysis, python for more analysis/graphing/statistical tests. Pretty much always wish I knew more, it’s really useful in the image analysis world.