How to be a 10x data scientist
If you are a Data Scientist looking to make it to the next level, then there are many opportunities to up your game and your efficiency to stand out from the others. Some of these recommendations that you can follow are straightforward, and others are rarely followed, but they will all pay back in dividends of time and effectiveness for your career.
By Daoud Clarke, Co-founder of DataPastry.
I’m going to tell you what it takes to be a 10x data scientist. What is a 10x data scientist? Someone who runs ten times as many experiments as the average data scientist.
Why experiments? Data scientists do other things, too: data munging, analysis, and writing implementations of machine learning algorithms for production. But experiments are what defines a data scientist. That’s where the science is, and it is what distinguishes them from a data analyst or a machine learning engineer.
So to be a great data scientist, you have to be great at doing experiments.
Why 10 times more experiments? You can never guarantee you’ll get ten times better results by being cleverer or faster. But you can run more experiments. And the more experiments you run, the more likely you are to get better results, the faster you’ll iterate and the faster you’ll learn.
Why do you want to be a 10x data scientist? I don’t know. Maybe because it sounds cool. Maybe because it’s fun. Maybe because you’ll have more time to eat pastries. That’s up to you.
I’m going to assume that you can run experiments correctly. You’re a data scientist, right? Nevertheless, there’s one thing I’ve seen many data scientists get wrong. It’s this:
1. Measure your uncertainty
What’s the point in improving 5% over the baseline if you don’t know whether the result is statistically significant? Data scientists know (or should know) statistics, but they are often too lazy to apply it to their own work.
There is no shortage of options for this. My favourite method is one I learned in my physics degree: estimate the uncertainty as the standard error in the mean. Of course, this means that the value you report has to be the mean of something, whether it’s the mean F1 score on five folds in cross-validation, or whether it’s the mean precision at 10 over rankings for 1,000 different queries.
You don’t need to do statistical significance tests between all your results. But you need to have a handle on how uncertain your results are. The standard error in the mean gives you that — if your results are separated by more than three times the standard error, chances are the difference is significant.
You probably also want to consider what effect size you’re looking for. If a 0.1% improvement isn’t useful to you, then there’s no point in running experiments that can detect this sort of change.
2. Big data is not cool
Big data is slow. You don’t want to be slow. So use small data. Most of the time, you don’t need big data. If you think you need it, spend a bit of time rethinking to make sure you really do.
You want your dataset to be big enough such that the uncertainty in your result is small enough to distinguish between differences that you care about. You don’t want it to be any bigger: that’s just a waste of time.
You don’t have to use all the data you have available. Depending on your experiment, you may be able to estimate how much data you need. Otherwise, look at how the metric you care about varies with training set size. If it levels off fairly quickly, then you’ll know you can get away with discarding a lot of data. Do more experiments to figure out how much data you need to make the uncertainty low enough for the insights you’re looking for.
The number one cause of slow experiments is using too much data. Just don’t do it.
3. Don’t use big data tools
If you have small data, you don’t need big data tools. Don’t use Spark, it will be horribly slow, and the results will be poor compared to something like Pandas and Scikit-learn. Use that instead.
4. Use a good IDE
Use a decent integrated development environment like PyCharm — actually, just use PyCharm, as nothing really compares. Learn how to use it properly.
These are the things that I find most useful:
- Autocompletion, especially in combination with typed code.
- Viewing parameters and documentation for a function or class.
- Quickly search the whole codebase for a file, class, or function.
- Refactor to extract a variable, function or method, and inline variables.
I can’t bear watching people struggle with a text editor for this kind of thing. Please stop.
Jupyter notebooks are OK for exploratory work, but if you want to be a 10x data scientist, you need to use an IDE for experiments.
5. Cache intermediate steps
An experiment can include preprocessing the data, extracting features, feature selection, and so on. Each of these steps takes time to run. The chances are, once you’ve found a good set of features, you will keep them more or less fixed while you experiment with models. If the preprocessing step takes a long time, it makes sense to cache the intermediate steps so that you perform these costly computations just once. This can make a huge difference in how long it takes to run experiments.
I will typically do this with one or more preprocessing scripts that generate files to be used by later stages. Make sure that you keep track of how these files relate to the source data so that you can track your experiment results back to the original data, either through file naming conventions or a tool designed for the job such as Pachyderm.
6. Optimise your code
If your experiment is still slow when you’ve reduced your dataset size, then you may benefit from optimising your code. Balance running experiments with optimising your code while experiments are running.
You should know the basics of how to optimise code. Here’s the basics: use a profiler. The profiler will tell you which bits are slow. Change those bits until they aren’t slow any more. Then run the profiler and find other bits that are slow. Repeat.
Run the profiler on a small sample so that you can quickly find out which bits are slow. (You need to optimise the optimising too.)
7. Keep track of your results
If you lose the results of your experiments, then it’s a waste. So keep careful track. Use a tool designed for the job like MLFlow, Sacred, or my own pet project, PyPastry. If you’re copying results around, then you’re wasting time and likely to make errors. Don’t.
If you do all the above things, running an experiment will likely take less than five minutes, ideally less than two. That’s long enough to think about what the next experiment will be.
This means you can potentially run hundreds of experiments in a day. When you’re running that many experiments, you need a good way to keep track.
7a. Eat lots of pastries
This one isn’t actually good advice. Your brain needs up to 400 calories worth of glucose per day, but eating pastries may not be the healthiest option to achieve this. But it would be tasty.
Instead, you could consider contacting DataPastry, the data science consultancy I run with my cofounder Iskander. If you’d like any advice or need help with a data science project, we’d love to hear from you , and we don’t bite (except for pastries).
If you do all the above things, then I’m pretty sure you will run at least ten times as many experiments as the data scientist sitting next to you (unless you’re sitting next to me). Most data scientists don’t do any of them. So if you do them all, you’ll probably run fifty times more experiments. Does this make you fifty times more valuable? I don’t know, but it can’t hurt. And you’ll have more time to eat pastries.
Original. Reposted with permission.
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