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Scipy Lecture Notes
- pw6hv 6y agoOn Firefox, the left table of content pane overlaps with the text thus I cannot see the leftmost part of the paragraphs...
- complex_pi 6y agoCan you report on your screen resolution?
- jsinai 6y agoSame issue with Safari. Ignoring that small issue, this is a well-crafted and mature resource, one I wish I had access to 5 years ago! Good job to the authors.
- dguest 6y agoIf you make the window narrower the content pane disappears, so it's possible to work around this.
- pw6hv 6y agoI think it's fixed now, it was not working correctly before but now the site looks great!
- ABeeSea 6y agoThe sidebar covers the content when zooming. Terrible design for accessibility.
- SiempreViernes 6y agoWorks fine for me
- rajamaka 6y agoSame here, but without zooming.
- adenozine 6y agoWhat an incredible resource! It's always great to see well-crafted python resources. It's so easy to get started in python and you can get pretty far without knowing the best ways to do things, so I'm glad there's things like this for newbies. Maybe in the future, the statistics portion could be expanded. While I'm grateful for all this information, it is rather odd to leave out Bayesian stuff. As an aside, HN comments with nothing to say except CSS comments is so shameful. Imagine collecting all this information and giving away this catalogue for free and having someone nitpick some silly sidebar zoom functionality. It's honestly despicable how often it happens. I hope the author knows how much this resource helps people out.
- beojan 6y ago> As an aside, HN comments with nothing to say except CSS comments is so shameful. I see two top-level comments of this sort, and they're both at "I can't read it" severity.
- asicsp 6y agoHere's more awesome resources: * Pandas: https://pandas.pydata.org/docs/getting_started/index.html https://pandas.pydata.org/docs/getting_started/index.html * DSP: https://greenteapress.com/thinkdsp/html/index.html https://greenteapress.com/thinkdsp/html/index.html * Numpy: https://www.labri.fr/perso/nrougier/from-python-to-numpy/ https://www.labri.fr/perso/nrougier/from-python-to-numpy/ * Data Carpentry: https://datacarpentry.org/lessons/ https://datacarpentry.org/lessons/ * Data science path: https://github.com/ossu/data-science https://github.com/ossu/data-science
- 3do 6y agothank you!
- blewboarwastake 6y agoThank you for sharing. Never heard of Data Carpentry, looks awesome.
- jbencook 6y agoThis is great! Has anyone read From Python to NumPy?
- screature2 6y agoAlso, I love the cookbooks that Chris Albon put together https://chrisalbon.com/ https://chrisalbon.com/ I make especially heavy use of the data wrangling recipes everytime I can't figure out how to do something in pandas.
- mattficke 6y agoThis is a great list, thank you. Julia Evans has a Pandas cookbook that's a good complement to the official docs: https://github.com/jvns/pandas-cookbook https://github.com/jvns/pandas-cookbook
- zappo2938 6y agoSo .... where do I learn statistics in the first place? Let me rephrase the question. What is the most efficient way to learn the minimum viable amount of statistics?
- cinntaile 6y agoAn introductory statistics course. I'm sure there are a few of those available online, both as a paid course and as free online university lectures.
- yellowstuff 6y agoThink Stats is a very good book aimed at Python programmers who want a broad overview of practical statistical techniques: https://greenteapress.com/thinkstats/html/index.html https://greenteapress.com/thinkstats/html/index.html
- zappo2938 6y agoThank you for the recommendation. I build admin dashboards using stock (double entendre?) charting libraries and recently have been using my own d3.js visualizations with dynamic content. At this point, I might as well start to delve into data science and have been investing time developing math skills with calculus and linear algebra. I would like to also take some time and learn basic statistics concepts. I want a level up a little bit but don't see the point getting a Ph.D. in machine learning. I only need the basics to start from.
- joshvm 6y agoYou probably want the second edition: https://greenteapress.com/wp/think-stats-2e/ https://greenteapress.com/wp/think-stats-2e/ The first edition PDF 404s for me.
- st1x7 6y ago> What is the most efficient way to learn the minimum viable amount of statistics? You need to add 3 constraints to the question 1. What is your starting point and current knowledge of mathematics and statistics? 2. Minimum viable for what? What do you need the statistics knowledge for? 3. How much effort can you afford to put into this over what period of time? Then the answer ranges from "here are a couple of good youtube videos" to "here is how to design your own degree in statistics using freely available material".
- the_mango 6y agoAm I the only one who read - Spicy Lecture Notes ?
- tsjq 6y agoMe Too !
- jagged-chisel 6y agothis is the first time I have ever dyslexified SciPy into Spicy and I fear I will never read it correctly again.
- dragonshed 6y agoDefinitely not the only one. Capitalization matters. For me, the mental transposition is less likely with 'SciPy' than with 'Scipy'
- freakynit 6y agoWow!!!! This is gold. Super useful. Thank you for this :)
- iagovar 6y agoIm my journey through data analytics, what helped me most is to fight with real datasets. Lectures are fine, but you don't really grasp the little details needed to do a proper job until you have messy datasets, very large datasets, have to deal with text in a non-english language, etc. That's the most useful stuff in my opinion. Courses and lectures include sample data that don't really put you in the position to having no option than optimize your workflow because your box can't deal with it in a reasonable time. Or when you go crazy because you can't perform some analysis because something somewhere is wrong and your debugger can't help you, and you just want to punch someone in the face. That's how I discovered that cleaning and preparing data is about 90% of the job, avoid CSV for non-numeric data and use SQLite instead, when possible, the god-send of Knime, etc.
- giu 6y agoBy real datasets you mean company-specific ones? Or do you happen to have some examples that are openly available which helped you a lot? I definitely concur with your first point, since I made the same experience, specifically when working with company-specific datasets. From my experience one also underestimates how much time cleaning up the data takes; there are quite a few steps you need to go through before you can really start to analyze a dataset.
- iagovar 6y agoI happen to scrape a lot of large websites (mostly forums currently) and that's messy enough to force you into learning tricks. I didn't stumble upon into any (tabular, at least) dataset that wasn't very curated. Keep in mind that I studied sociology, so stuff that is a given for most HN people isn't for me. I had to learn a lot of CSS (for selectors), regex (still hate it), what's OLAP and how to take advantage of it (DuckDB) and a lot of stuff I'm not even aware now. But I remember taking courses in my Uni, and later on, with R and Python. It was interesting, but no matter how deep into the rabbit hole of weird models I learnt, it felt... IDK, shallow? Imagine yourself pulling data out of a company ERP, with human filled data. It won't be a walk in the park, just make some logit models and call it a day. You'll spend a lot of time trying to understand what's going on. And then you perform the models or make a dashboard.
- johndoe42377 6y agoThis, by the way, should not be called "science". Science is a methodology of establishing aspects of truth (via reproducible experiments). What it should be called accurately is "modeling". Mostly oversimplified and plainly wrong (like the Bayesian sect or any kind of predictive modeling - look how all covid models and simulations missed everything). So, it is data modeling, not data science. And it is important to realize and understand the difference.
- enriquto 6y agoThe section "how does python compare to other solutions" is a bit lackluster, and heavily biased at the same time. It would be more useful if this section was written by proponents of each of the other "solutions".
- reallydontask 6y agoI agree with you 100%, maybe offer to write it up for them. It's hard to write from a different point of view from that which you hold, or at least that's what I find
- Bostonian 6y agoDoes anyone have a book they would recommend over this resource for learning Scipy? Or is this the best place to start?
- mattip 6y agoThis is the best place to become familiar with the tools and to set the stage for your journey. Then find a problem you want to solve, and find more domain specific resources. Some people learn best from tutorials, some from video, some from courses, some from just banging their heads against a wall till they figure it out.
- deleted 6y ago[deleted]
- maztaim 6y agoI skip-read this as SPICY lecture notes...
- sireat 6y agoThis is an awesome resource but the general Python section could use some work. I am assuming that target audience are scientists with a modicum of programming knowledge. The list and especially dictionary section is a bit bare. In the optimization section have a discussion on when to use lists, dictionaries, tuples and sets. (for example the difference between "needle" in my_list vs "needle" in my_set) When to use something from collections and when to use ndarray. (the short answer being - it depends)
- complex_pi 6y agoCo-editor of the lecture notes here, if someone has a question.
- rajesht 6y agoIf you read it spicy, you are not alone. Human brain optimizes by reading first and last letters to the wodrs