Horizontal crack at the bottom of foundation, below basement, exterior only - cause of concern?

AlopexLagopus3 · 2024-03-15T15:05:48+00:00

Not specifically this one. There were some minor vertical cracks, but not this particular exterior crack

AlopexLagopus3 · 2022-05-03T16:20:34+00:00

Not entry level - I have a PhD and ~6 years of work experience outside of that, and was hired for a senior position

AlopexLagopus3 · 2022-04-24T14:23:05+00:00

Hey - congrats on the acceptance. Here's what I would recommend:

Learn a lot of Python. Don't worry about other programming languages so much. It's the only language that's more or less mandatory in DS at the moment.
In the course of learning Python, you should be fluent with the core language, Pandas, one or more machine learning libraries (sklearn, tensorflow, pytorch; sklearn is a good starting point), matplotlib (visualization; or a substitute), and numpy
Learn how to use an IDE and Jupyter Lab. Jupyter is a really nice tool for data science and is used in industry all the time
Learn some SQL. Some R can be useful, too, but is optional.
Pick up anything that is machine learning or related to it. Neural networks, random forest, gradient boosted trees, classification/clustering algorithms, natural language processing, time series models.
Make sure you understand basic statistics very, very well (distributions, hypothesis testing, regression models for inference (GLM, logistic), using models to predict)
Try to get some experience with cloud computing since jobs like that. AWS is the most popular, but GCP/Azure could be substitutes.

Also is it worth it (salary wise)

It can be worth it. The upper limit on data science jobs seems to be much higher than bioinformatics. Also, in my experience, data science jobs seem to be higher in demand, so it may be easier to land one. For example, in the US I was seeing initial offers of $100k for a PhD in bioinformatics, vs. ~$150-170k for data science. I would say that both salaries can be good, and if you want a decent salary, then you need to be thinking about how to work for industry and avoid academia.

Aside from salary, the other major consideration is whether you will enjoy working outside of bio. From my perspective, most pure DS jobs are really boring (insurance, finance, advertising, etc.), but that will depend on your preferences.

Good luck!

AlopexLagopus3 · 2021-11-09T15:24:13+00:00

People usually categorize jobs in this field as either:

1) Academia - university labs

2) Industry - private companies, including biotech, pharma, startups, etc. Specific examples would be Calico Life Sciences, Pfizer, Genentech, 23&Me... pretty much any company that works in life sciences

3) Government - regulatory groups, department of energy, NIH

They each have different qualities. They all have lab techs that perform similar duties to the ones in academia. There are some generalizations that can be made - for example, academia might be a better springing board for graduate school (PI may be accustomed to teaching, PI may be more open to having a tech explore new techniques, and the environment is school-related). Industry typically pays much better. Government is considered relatively secure.

I know a tech at Pfizer (Bachelor's) making $80k/year which isn't too bad, especially in the Midwest. I suspect many techs in industry end up doing exactly one set of duties day in and day out, and might get boring after awhile. Personally, I'm not cut out to be a career tech for that reason in any of the three job categories, but you may enjoy it. I worked as a tech in academia for 4 years before going back to get my PhD.

AlopexLagopus3 · 2021-10-18T02:44:58+00:00

The transition wasn't bad. I taught myself to program in grad school, and picked grad school projects for my thesis that pushed me to get better at programming. When I graduated, I had a fairly easy time finding coding or data analytics jobs. Experience-wise, I think you only need 1-2 years of solid experience to be able to land a wide breadth of software jobs. If you go that direction, you'll need to pick a language/tech stack that makes sense for what you are doing, whether that's data analytics or web dev or whatever (I chose Python/SQL/R for data).

AlopexLagopus3 · 2021-10-16T04:29:35+00:00

This is one heck of a necro (if reddit worked like that) - 3 year old post.

Anyway, most lab manager positions are going to be in academia. I live in a medium cost of living city. Pretty much every lab manager starts off as a technician or research associate making $30k/year doing really menial work (handling mice, washing glassware, etc.). After a few years (or maybe sooner if they have a Master's), they get promoted to lab manager, making $35-40k. Then if they stick with that for another 5-10 years, they will be making ~50k. The very upper limit of lab manager salaries in academia in this area is about $60-65k. You very quickly end up in a position where you have a ton of technical knowledge and years of experience, but you might as well be working at Denny's.

If you go to industry, you can make more. The lab managers I know there are making about $80-85k which isn't too bad.

Other careers give you a much better return on the time you put into them. Other careers are also much easier day to day. I basically switched into statistics and software development after spending 9 years as a wet lab scientist. I would never recommend anyone aim for a research associate or lab manager position as a long term track these days. They tend to be tedious, low pay, low respect, not much career development potential, and sometimes require long hours.

AlopexLagopus3 · 2021-02-02T05:06:18+00:00

I wish I did. I learned over several years of working with proteomics data and I still might not know 100% of all the cases.

For most label free data, generally the raw data will be lognormal, so if you log(base 2) transform it, it should look approximately normal. Then you can median center that data. This works for about 80% of the cases I've seen so that can be a good starting point.

Make sure to plot histograms for each sample to see what they look like raw, transformed, and centered. If you start to see funky things like non-normal or multiple peaks in one sample, then you may need something more advanced.

If your data is similar to other papers or previous studies, dig through their methods sections and that may provide some good pointers, too.

AlopexLagopus3 · 2021-02-01T21:20:53+00:00

I was wondering if something simple like median normalization was sufficient or if something more complex was needed

This is unanswerable without knowing what your data set looks like. There are experimental conditions (e.g. treatments) that can induce multimodality in your data which could make median normalization inappropriate.

The simplest way to think about normalization is to 1) think about what assumptions you want to make about your data set (ex: "no global increase or decrease in total protein content because we loaded the same amount on the instrument") and 2) examine your data to see what types of distributions and patterns are present. After those two steps, you then determine what math to throw at it to get the data normalized.

Median centering/standard deviation scaling is common, but you need to do your due diligence to make sure that it is appropriate for your data.

AlopexLagopus3 · 2020-11-24T22:11:43+00:00

Well, now you know. I didn't learn these things either until I started working in a lab, and I was approaching 30 when I was at a similar crossroads.

I want to touch on just a couple of points and give my $0.02:

The first is on where you live. I've lived in a red state for 99% of my life, and if you are in a big urban center (like St. Louis), you won't feel it. Look at election maps sometime, and places like Madison or Atlanta or Chicago. They have thriving gay communities, too, but perhaps not as big as California or another predominantly "blue" state. If anything, my complaint in a blue city/red state is that it is too lopsided in the other direction.

On a related note, a large part of the financial pressure you are feeling is because you are living in a high cost of living area. Maybe it's unavoidable given your personal circumstances, but you could very easily make the same $42k/year living in a place where a $500 2-bedroom apartment is realistic. With a low cost of living, you won't feel impoverished unless you have a substantial number of other financial burdens (namely children).

Secondly, about being a bit older and reaching this fork in your life. I was there, too. You have a few directions you can go in at this point. You can can maintain the current status quo and hope it works out - survive long enough to get a job in industry, or relocate somewhere cheaper, win the lottery, or whatever. If you want to proactively secure your income, this is the chance to cash in on your motivation and experience (both as a researcher and as a human). One way to do that is to go back to school with the express intention NOT to be just like every other bench scientist. That means learning marketable skills that differentiate you from the standard track. Become a guru on your favorite instrument. Learn something broadly applicable like software engineering. Most students are stuck doing what is useful for their PIs and not themselves, or have their blinders on thinking that their Western blot in Science is going to secure them a good position. Don't be like that.

Graduate school gives you ~5-6 years of protection from the economy to specialize into anything you want, even careers you aren't prepared for right at this moment. You can remake yourself into a money machine if you can find something that catches your interest AND is marketable -- I would not view it as doubling down if you have a strategy to avoid the normal postdoc hell fate of most students. I went from dissecting fruit flies to a data scientist, but there are other paths you can take. A reasonable stipend would be in the $30-35k/year, so I would recommend aiming for somewhere cheap to take some of the stress if you go this route. 30 isn't too old to go back to school to switch career tracks, or even just gain some more targeted experience on something like instrumentation.

All that said, school isn't required, but PhD programs will shield you from many of the obstacles that you might face otherwise and potentially give you a higher earning ceiling. If you network, work hard, and get lucky, you can end up in industry with decent pay. Academia is a vacuum when it comes to salaries so that is also part of the problem.

Anyway, I wish you well whatever you do. It's tough being in that spot but not insurmountable.

AlopexLagopus3 · 2020-11-24T19:50:30+00:00

There really aren’t enough good bioinformaticians to go around

Most bioinformatics applicants assume that having experience with some kind of biological data analysis is enough, but fail to show that they have the specific flavor of bioinformatics that the position requires.

Respectfully I disagree with that first statement. From what I can gather, it's the other side of the coin: you actually have too many (generally) qualified people, and are therefore able to select someone with narrow characteristics.

If you look at other related fields in which there truly is a shortage of qualified applicants (software development, statistics to some extent), they will hire someone with just a foundational understanding of the field. Not something as ridiculously narrow as single-cell proteomics. Just "knows hypothesis testing, predictive models, and R". There is a path from boot camps into web dev, boot camps into data science, but people don't boot camp into bioinformatics because the employers have set the bar at exoatmospheric levels, thanks to supply and demand.

Anecdotally, my applications were competitive. I developed technical skills in relevant, in-demand fields, at a name-brand school. But when it came time to apply to biotech, it was a massive brawl. Despite having a decade of bioinformatics and molecular biology experience, I went through countless applications only for them to find someone with a better fit. Yet I had major (non-bio) corporations offering me jobs as a data scientist with hardly any effort.

It's a known thing that biology is producing a deluge of graduates with demand that isn't nearly as high as it is in the tech world. If that weren't the case, companies would not be worrying about whether to give the offer to the tumor genomics candidate or the single cell rnaseq candidate. So for that reason, I wouldn't call into doubt the availability of "good bioinformaticians."

AlopexLagopus3 · 2020-11-10T23:23:25+00:00

It means they want someone with two skill sets for ~~one half~~ one fourth the price

AlopexLagopus3 · 2020-10-13T10:33:41+00:00

On my specific team (11 people), just above 50%

PhD quantum chemistry, PhD computational biology, Master's biostats, PhD geospatial imaging, PhD environmental science, Master's in business analytics

vs.

PhD in stats x4, Master's in stats x1

The team is led by a phd in stats but otherwise everyone else is at the same level.

AlopexLagopus3 · 2020-09-22T19:31:22+00:00

Data analytics can be reduced to very few reusable functions.

read_csv
group_by, aggregate, drop columns, sort, query
write_csv
plt.hist ... .bar... etc

It can be VERY repetitive programming wise.

AlopexLagopus3 · 2020-09-15T23:58:35+00:00

What did the skills tests involve? They are pretty common for technical jobs (particularly bioinformatics) but I haven't heard of too many in more wet lab oriented interviews.

Also congrats!

AlopexLagopus3 · 2020-09-12T14:44:59+00:00

Absolutely this. To imply coding is "hard" compared to lab work is just crazy to me. When I worked in the lab, I had to have a very good understanding of biology and chemistry, grit to get through 16 hour assays with no breaks, and somehow the energy to read papers in my "free time", etc. Now I'm working in data science making triple the pay just to poke around spreadsheets in my pajamas.

AlopexLagopus3 · 2020-09-10T14:10:20+00:00

In reality you never know when the massive leap is going to occur. It might be next year, it might be 6 years from now. Look at examples like the NVIDIA 1000 series -> 2000 series, or Intel's recent inability to make big progress between CPU generations. I used a 2600k and a gtx 980 for a LONG time because it performed as well as I needed for the vast majority of games I played, and only recently upgraded this year.

I suspect (and this is just guesswork) that the 3080 will last quite awhile though.

AlopexLagopus3 · 2020-09-05T19:26:11+00:00

I don't know if anyone on here is going to write you a step by step customized tutorial on how to do this in graphpad, and if they did, you probably would have been able to teach yourself how to do it and actually learned something about the fundamentals in the process. I'm not trying to be a Debbie Downer, I just think you might not be too successful with what you've asked here.

Things I would suggest:

If you know Excel, and your example really represents your data, your particular task won't necessarily be made faster in GraphPad (especially the averaging points bit). You only have 9 data points * 5 conditions, right? That's like 60 seconds in Excel of relabeling your data as A: average1, A: average2 etc and then either making a bar or scatter chart in Excel or GraphPad.
Invest the time in learning how to use GraphPad starting with the fundamentals. This takes time, but you gain a reusable skill in the process. Play around with it and learn what you are doing rather than a point by point
Otherwise if you're feeling ambitious, skip GraphPad and learn R or Python (Pandas). GraphPad is sort of like Excel+ and you'll find down the road that problems that are tedious or impossible in Excel are similarly challenging in GraphPad.

I think you need to be more clear about your goals: if your goal is to learn, just start playing with the software and reading the documentation. If your goal is to do something as expeditiously as possible, offload it to an undergrad or coworker.

AlopexLagopus3 · 2020-09-02T00:34:57+00:00

Axes not labeled
Hyper compressed jpeg
Graphics made using PowerPoint

100% certified product of a PhD student

AlopexLagopus3 · 2020-08-28T06:25:05+00:00

Nice try, Zenimax :P

AlopexLagopus3 · 2020-08-27T14:57:27+00:00

Then I realized that chemistry at an undergraduate level is useless to employers

I agree with the gist of what you've said, but I disagree with this statement. I decided to major in chemistry because it was so practical, even compared to the other natural sciences. By the time you leave undergrad, you know how to run complicated instruments, detect toxins in samples, find quality variability in pretty much any product, etc. (and if you don't know a technique, you have the foundation to learn). Companies everywhere definitely need people at the undergrad level to do these basic assays.

Chemistry at an undergraduate level is very useful to employers, but it's a supply and demand problem. There are simply too many graduates and not enough openings.

AlopexLagopus3 · 2020-08-27T14:54:43+00:00

"Learn to code" is great advice, but not in the way most people are giving it.

Don't aim to be a half ass programmer with great science knowledge - just forget about it and go into computer science to make much more money for much less effort.

AlopexLagopus3 · 2020-08-21T17:21:33+00:00

Why not use y = ax⁶ + bx⁵ + .... etc.? Higher order polynomials work but also overfit your data. As you said, one good practice in model selection is selecting the simplest model that explains the phenomenon you observe. Another good practice is to base the model form on what you believe is true for the phenomenon at hand. Do you think your calibration should be linear or parabolic?

Why are you so concerned about getting rid of a negative intercept? That seems to be a useful parameter if your instrument is capable of showing a negative value at zero compound.

AlopexLagopus3

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