<story><title>Ask HN: Which MOOCs in Math/CS are worth still worth taking in 2022?</title><text>MOOCs doesn&amp;#x27;t necessarily mean courses on platforms like edX or Coursera. They can include university courses with public materials like videos, notes, assignments, etc.</text></story><parent_chain></parent_chain><comment><author>The_Amp_Walrus</author><text>Nand 2 Tetris: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.nand2tetris.org&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.nand2tetris.org&amp;#x2F;&lt;/a&gt;&lt;p&gt;In which you, more or less, build a computer from scratch. The course takes you through 12 projects, about 1 week each, where you incrementally build:&lt;p&gt;a CPU&lt;p&gt;a RAM chip&lt;p&gt;a full von Neumann computer&lt;p&gt;an assembly language&lt;p&gt;a virtual machine&lt;p&gt;a high-level language&lt;p&gt;an operating system&lt;p&gt;... using NAND gates. All of this is done on your computer using tools provided by the course. Once you&amp;#x27;ve done these projects you will understand the building blocks of a computer from the RAM and CPU, to assembly up to the compiler that executes your programming language of choice. It&amp;#x27;s a powerful course that will unlock a whole new perspective on computer programming for you. I believe that bang-for-buck it&amp;#x27;s probably the best online course for someone who is a self-taught programmer. It&amp;#x27;s practical, fun and mostly oriented around building things.&lt;p&gt;(from my blog @ &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;mattsegal.dev&amp;#x2F;nand-to-tetris.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;mattsegal.dev&amp;#x2F;nand-to-tetris.html&lt;/a&gt;)</text></comment>
<story><title>Ask HN: Which MOOCs in Math/CS are worth still worth taking in 2022?</title><text>MOOCs doesn&amp;#x27;t necessarily mean courses on platforms like edX or Coursera. They can include university courses with public materials like videos, notes, assignments, etc.</text></story><parent_chain></parent_chain><comment><author>minhmeoke</author><text>I liked the EdX Statistical Learning course by Trevor Hastie and Robert Tibshirani, it&amp;#x27;s a great introduction to statistical modeling and data science (assuming you already have a solid math and statistics background):
&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.edx.org&amp;#x2F;course&amp;#x2F;statistical-learning&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.edx.org&amp;#x2F;course&amp;#x2F;statistical-learning&lt;/a&gt;&lt;p&gt;It is not too math heavy, and the focus is on basic, interpretable approaches and concepts like:&lt;p&gt;- linear and polynomial regression&lt;p&gt;- logistic regression and linear discriminant analysis&lt;p&gt;- cross-validation and the bootstrap&lt;p&gt;- model selection and regularization methods (ridge and lasso)&lt;p&gt;- tree-based methods, random forests and boosting&lt;p&gt;- support-vector machines&lt;p&gt;- neural networks and deep learning&lt;p&gt;- survival models; multiple testing&lt;p&gt;- some unsupervised learning methods like principal components and clustering (k-means and hierarchical).&lt;p&gt;The instructors are really articulate and passionate about teaching well. As a bonus, there are guest speakers about every second week including Jerome Friedman and Geoff Hinton.</text></comment>