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Scipy Lecture Notes

320 点作者 vyuh超过 4 年前

15 条评论

asicsp超过 4 年前
Here&#x27;s more awesome resources:<p>* Pandas: <a href="https:&#x2F;&#x2F;pandas.pydata.org&#x2F;docs&#x2F;getting_started&#x2F;index.html" rel="nofollow">https:&#x2F;&#x2F;pandas.pydata.org&#x2F;docs&#x2F;getting_started&#x2F;index.html</a><p>* DSP: <a href="https:&#x2F;&#x2F;greenteapress.com&#x2F;thinkdsp&#x2F;html&#x2F;index.html" rel="nofollow">https:&#x2F;&#x2F;greenteapress.com&#x2F;thinkdsp&#x2F;html&#x2F;index.html</a><p>* Numpy: <a href="https:&#x2F;&#x2F;www.labri.fr&#x2F;perso&#x2F;nrougier&#x2F;from-python-to-numpy&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.labri.fr&#x2F;perso&#x2F;nrougier&#x2F;from-python-to-numpy&#x2F;</a><p>* Data Carpentry: <a href="https:&#x2F;&#x2F;datacarpentry.org&#x2F;lessons&#x2F;" rel="nofollow">https:&#x2F;&#x2F;datacarpentry.org&#x2F;lessons&#x2F;</a><p>* Data science path: <a href="https:&#x2F;&#x2F;github.com&#x2F;ossu&#x2F;data-science" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;ossu&#x2F;data-science</a>
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iagovar超过 4 年前
Im my journey through data analytics, what helped me most is to fight with real datasets. Lectures are fine, but you don&#x27;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.<p>That&#x27;s the most useful stuff in my opinion. Courses and lectures include sample data that don&#x27;t really put you in the position to having no option than optimize your workflow because your box can&#x27;t deal with it in a reasonable time.<p>Or when you go crazy because you can&#x27;t perform some analysis because something somewhere is wrong and your debugger can&#x27;t help you, and you just want to punch someone in the face.<p>That&#x27;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.
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adenozine超过 4 年前
What an incredible resource!<p>It&#x27;s always great to see well-crafted python resources. It&#x27;s so easy to get started in python and you can get pretty far without knowing the best ways to do things, so I&#x27;m glad there&#x27;s things like this for newbies.<p>Maybe in the future, the statistics portion could be expanded. While I&#x27;m grateful for all this information, it is rather odd to leave out Bayesian stuff.<p>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&#x27;s honestly despicable how often it happens. I hope the author knows how much this resource helps people out.
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pw6hv超过 4 年前
On Firefox, the left table of content pane overlaps with the text thus I cannot see the leftmost part of the paragraphs...
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sireat超过 4 年前
This is an awesome resource but the general Python section could use some work.<p>I am assuming that target audience are scientists with a modicum of programming knowledge.<p>The list and especially dictionary section is a bit bare.<p>In the optimization section have a discussion on when to use lists, dictionaries, tuples and sets. (for example the difference between &quot;needle&quot; in my_list vs &quot;needle&quot; in my_set)<p>When to use something from collections and when to use ndarray. (the short answer being - it depends)
zappo2938超过 4 年前
So .... 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?
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enriquto超过 4 年前
The section &quot;how does python compare to other solutions&quot; 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 &quot;solutions&quot;.
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rajesht超过 4 年前
If you read it spicy, you are not alone. Human brain optimizes by reading first and last letters to the wodrs
complex_pi超过 4 年前
Co-editor of the lecture notes here, if someone has a question.
Bostonian超过 4 年前
Does anyone have a book they would recommend over this resource for learning Scipy? Or is this the best place to start?
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ABeeSea超过 4 年前
The sidebar covers the content when zooming. Terrible design for accessibility.
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freakynit超过 4 年前
Wow!!!! This is gold. Super useful. Thank you for this :)
maztaim超过 4 年前
I skip-read this as SPICY lecture notes...
johndoe42377超过 4 年前
This, by the way, should not be called &quot;science&quot;. Science is a methodology of establishing aspects of truth (via reproducible experiments).<p>What it should be called accurately is &quot;modeling&quot;. Mostly oversimplified and plainly wrong (like the Bayesian sect or any kind of predictive modeling - look how all covid models and simulations missed everything).<p>So, it is data modeling, not data science. And it is important to realize and understand the difference.
the_mango超过 4 年前
Am I the only one who read - Spicy Lecture Notes ?
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