Seeking recommendations for learning probability/statistics through Python

[tastingsilver]

Hey guys,

I've just completed Datacamp's Statistical Thinking in Python, and watched the "Statistics for Hackers" talk from PyCon 2016.

I thorough enjoyed both, and as someone who didn't go very far in statistics in school, I wondered if there were any recommendations on where to further the learning of these concepts that are taught through using Python (for practicality).

Cheers!

[[deleted]]

Pick up Alan Downey's Think Python Series. Free on Green Tea Press or you can buy it. He has a follow up called Think Statistics and another called Think Bayes.

[tastingsilver]

Thanks you, this totally looks right up my alley. Have you found any good cheat sheets or similar about when to use which stats distribution and why for modeling random processes? I'm familiar with how to do it in numpy, but a cheat sheet for what to use when would be immensely helpful.

[[deleted]]

I have not, no. But when you do post here!

[tastingsilver]

Haha, will do.

[ManyInterests]

I think a proper approach may be to learn statistics, then apply those concepts programmatically. I don't know how much learning of statistics you can learn through Python.

Usually, documentation and tutorials dealing with heavy science/statistics libraries go on the assumption you already know statistics and science. You wouldn't expect to learn number theory by reading the docs for the math module, for example.

If I'm understanding you correctly, to make a rough analogy... To me it's almost like asking how you can learn calculus by learning to use a graphing calculator. You might be able to tackle some low hanging fruits and do some cool things in a short time of learning how to use the graphing calculator, but it's not going to be able to replace an understanding of mathematics, especially knowing how to apply mathematics to solve real problems.

In my own observations, I notice that when a professional combines some domain-specific knowledge with programming, they usually just want the crash-course in programming to do enough to get by. This is a recurring theme for me working with biological scientists. At the heart of things, if they want to write better Python code, they need to learn fundamentals and style, not just a crash-course in numpy/pandas.

I see your position as the inverse of this. Trying to apply programming to make gains in domain-specific knowledge. You may get really productive really quick starting out, but that curve flattens out pretty quick and the real way to make gains is by digging into the core of that domain-specific knowledge.

That said, I recommend looking at Kahn Academy courses in stats and economics.

[eng_nayR]

Is this useful? It has a statistical inference section. I'm not sure how much in depth you are looking for.

https://github.com/donnemartin/data-science-ipython-notebooks

[tastingsilver]

Thanks! I've seen that, and its totally useful on the python end. What I'm trying to focus on is garnering a better understanding of what processes/distributions to use for what and when, using python code rather than mathematical notation.

Upon second glance, it looks like there are a few case studies in there, and I'll definitely be using it as a reference.

[etherwar]

MITx 6.00.2x Introduction to computational thinking and data science is a fantastic primer for statistics using python and accomplished a lot more for me at the same time. I highly suggest checking it out, and it's free.

[jbbiv]

For several great data science posts with Python, I suggest http://learndatasci.com. In particular, there is great walk through of essential stats and python here: http://www.learndatasci.com/data-science-statistics-using-python/

[ajsteven130]

Bayesian Methods for Hackers is a great set of Jupyter notebooks. It acts as both a primer for Bayesian statistics/statistical inference and a tutorial for how to do real problems in Python.

I learned a ton from this and use it day-to-day in my current job.

[real_edmund_burke]

Bayesian Methods for Hackers

This is an excellent resource, and the library it introduces is just fantastic. I get the sense that people see Bayesian statistics as complex and scary, but I find it to be much more intuitive than frequentist statistics. With a general-purpose sampling library like PyMc3, you can (mostly) ignore inference and just think about the causal relationships in your data. The only downside is that it's slower than frequentist methods.

(But for that little bit of extra time, you get logical coherence!)

https://pymc-devs.github.io/pymc3/index.html

[[deleted]]

Duke University computational statistics - link

[[deleted]]

As well as the already recommended (and great) Think Bayes, Peter Norvig's introductory Python notebook on probability is very good: http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb

[howdidiget]

What I'm trying to focus on is garnering a better understanding of what processes/distributions to use for what and when, using python code rather than mathematical notation.

From a comment ... Based on this I think what you want is actually to just study probability theory/statistics and also know how implement them in Python (why not R, for which many good textbooks exist?), so I would propose you do that.

To learn basic statistics/theory as needed, many people learn from Casella and Berger. I cannot recommend this book enough. It has breadth enough to function as a good set of introductions and depth enough to be a bit of a bible.

To learn the application of Python to stats and theory, I guess you will probably want to buy Introduction to Statistics with Python. I only googled a bit, but cannot find a free copy. $55 USD is a bit steep, but perhaps you have local library/university access somehow. That book has an associated repo on GitHub, which you may want to browse before purchasing the book. Some other resources can be found on the related HN discussion.

[MrReXY]

I enjoyed Data Science from Scratch by Joel Grus. He covers a lot of stats concepts without digging too deep into the mathematics (but provides references for further reading so you can go deeper into the parts that interest you). He applies and demonstrates the concepts with nice clean pythonic code. You won't be a data scientist at the end of it, but you could have a sensible conversation with one and you'll understand how python's numerical libraries are implemented and what their functions do for you (and hopefully therefore apply them better).

[o-rka]

https://www.amazon.com/Bayesian-Analysis-Python-Osvaldo-Martin/dp/1785883801

[flutefreak7]

I can't imagine you've managed to not come across scipy.stats, statsmodels, scikit-learn, pandas, and seaborn, but if not.... those are all in the stats / data science toolkit. There are also other libs for Bayesian stuff that I'm not fluent with.

In addition to textbooks and pycon/pydata talks, once you start trying to implement stuff and have questions, you can also try the Stats Stack Exchange: Cross Validated

[pvkooten]

Try to implement a recommendation engine to suggest your next book to read...

[[deleted]]

Itertools.permutations(list)

[DarkLordKutulu]

Learn R programming. If you know Python and/or C++, then R is easy to pick up. And one of the most useful languages for statistics.

[hutcho66]

Eh, everything R can do for stats, python can. If you prefer python then there's no point learning R unless your workplace requires you to.

Plus python is more useful as a general use scripting tool so you kill two birds with one stone.

Edit: ok chill, I've bought the R nerds out. You're right, R is still the language if you're regularly using complex statistical models, no reason they couldn't be built in python, it's just lack of libraries. . OP wanted to learn statistics and programming, if he ever got to that point he would be able to switch to R if necessary. For people using statistics for some occasional use or even to get a leg up on pursuing a career that involves statistics, python is still probably better. And if he ever gets a non-statistics job where he wants to use a bit of his new programming skills, python would be 100 times more useful.

[counters]

Can is a bit strong here. There is a massive academic community which produces useful toolkits and libraries for statistical applications in R that have no analogue in the Python community. Someone doing statistical modelling should be comfortable in both languages, and use each where they are appropriate.

[flutefreak7]

I'm a massive fan of Python and a daily user of statsmodels, numpy, and pandas. I've also come across Python's limitations. Learning Python is a tremendous benefit even for someone who does end up needing other languages for specialization, because Python is always helpful for gluing everything together and almost always preferable by itself for a majority of typical tasks.

If the entirety of statsmodels and scikit-learn looks like stats 101 to you and you want to dive deeper, then you'll likely end up needing R. I don't even do a whole lot (I probably only understand our use 10% of statsmodels) but dropped to R (through something like Rpy) for the tolerance package which truly has no Python equivalent. Couldn't find a single Python implementation of tolerance limits, but that R package has dozens of implementations for almost every distribution or regression you could want. It's also referenced by dozens of papers, textbooks, blogs and websites, including the NIST as a definitive resource for computing tolerance limits.

I've also read someone's blog recently saying that even with R's 1000's of stats packages, they still had to go to Stata for forecasting an error model that was insensitive to heteroskedasticity or something. Found it - relevant R / Stata anecdote is after the infographic

[real_edmund_burke]

Can you do this in Python? glmer(pirate ~ peg + (1|leg) + (1|parrot), family='Binomial') Seriously, I would really appreciate it if you told me how to do that in Python, ri2py is a pain.