Data School

As other commenters have pointed out, accuracy is not a great metric to use here. A confusion matrix [1] is needed to fully capture the results. I'm surprised that one was not prominently displayed in this article. They are standard practice for evaluating binary classifiers. [1] https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology…

A couple recommendations piggybacking off of yours: A First Course in Probability has a lot of problems (with solutions) and worked examples, but it’s light on intuition and pedagogy. It’s not an easy book to learn from, on its own. I highly recommend listening to Joe Blitzstein’s STAT 110 lectures and reviewing the wealth of problems/notes. The greater mastery of probability theory that you have, the easier studying ML and stats is. https://projects.iq.harvard.edu/stat110/home Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/ There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/

Another great guide in a similar vein: https://www.dataschool.io/python-quick-reference/

Thanks! I also just did a quick google and found the below resources if anyone else is interested http://www.dataschool.io/best-python-pandas-resources/ https://www.quora.com/How-do-I-learn-data-analysis-with-Python-via-pro… https://www.quora.com/What-is-the-best-way-to-learn-algorithmic-tradin…

For another option, similar to the Udacity course, I recommend this: http://www.dataschool.io/machine-learning-with-scikit-learn/

Coursera Machine Learning is one of the best places to start, but there are countless resources. There's a huge, wonderful open list of links at Github[1]. I definitely recommend you take a look at it There are also another great resources online, like those I list below: 1.) In-depth introduction to machine learning in 15 hours of expert videos[2] 2.) Deep Learning Tutorial (@ ufldl.Stanford.edu/tutorial/, can't post the link because I'm out of mana, I mean, not enough reputation yet) [1]: https://github.com/josephmisiti/awesome-machine-learning [2]: https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/

Hi all! I'm the owner of the GitHub repo linked above. It's called "DAT8" because it was the 8th session of General Assembly's part-time Data Science course in Washington, DC. Here's a bit of history, if you're interested: - General Assembly launched a standardized Data Science course curriculum in mid-2013. The course used lots of slides, and taught both Python and R. - Over time, the curriculum evolved in different directions as instructors around the world made their own modifications, both in terms of what was taught and how it was taught. By early 2015, I think all instructors had moved to teaching Python only. - I taught the course five times starting in mid-2014. Over the course of those five sessions, I rewrote nearly all of the lessons and converted most of them to IPython/Jupyter notebooks. I would estimate that 95% of the material in the DAT8 repo is my original lessons. - Earlier this year (2016), General Assembly rewrote and relaunched a standardized Data Science course curriculum. Therefore, if you take this course in the future, I would expect that you will be learning from that new curriculum. For what it's worth, I wrote a 4000-word essay [1] about data science education after teaching the course the first time. I'm happy to answer any questions! [1] http://www.dataschool.io/teaching-data-science/

Do this course. Do the examples. http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/

Check out CoCalc[1], formerly Sagemath Cloud. It is a project with a lot of history (think of SageMath as python-based open-source Mathematica alternative). It supports Jupyter and Sage notebooks. CoCalc also supports Julia, LaTeX, R. I'm using Google Colab[2]. It's the direct equivalent, hosted by Google. Not quite open source, but it's free (and does it matter if a cloud service is open source? If you want control that open source gives you, you run things locally anyway). Disclaimer: I work there, and use it at work daily. For a Colab vs CoCalc comparison, see[3]. In my brief experience, CoCalc UI is currently snappier than Colab; but I haven't written any large notebooks in it yet. There are other Python cloud services[4] which I don't have any experience with. [1]https://cocalc.com/ [2]https://colab.research.google.com/ [3]https://www.forbes.com/sites/gregoryferenstein/2019/08/18/a-review-of-… [4]https://www.dataschool.io/cloud-services-for-jupyter-notebook/

I can attest to this in the case of the Data Science course. My wife, a non-CS PhD, was interested in changing her career track. We were referred to General Assembly (the SF location) by one of my coworkers and the curriculum looked comprehensive enough, so she signed up. About a month into the course, to our despair, we discovered that there was almost zero conceptual training imparted (I am a data scientist by profession and can speak to this). The instructors seemed to be working there as a stopgap measure while looking for other jobs, and the students were there for standard credential-seeking. The course material meanwhile was focused on answering "How" but not "Why", which is far more important when approaching data science problems. To fix the situation, we quickly pieced together a curriculum based on Tibshirani & Hastie's machine learning video lectures[0] and picked a few Kaggle challenges for practice. My wife went through this material over a month, and came out with enough of a mental framework to be able to tackle book-length material such as ISLR[1]. Finally, there was almost zero career support provided apart from generic advice around how networking is important and how one should try to get outside their comfort zone. Though all's well that ends well (my wife is working as a data scientist now at a seed-stage Valley startup), in retrospect we often talk about how we shouldn't have wasted money on this course. She didn't learn anything that she couldn't…