31–40 of 44 posts
The original post for this was what inspired me to create this: http://afaq.dreamhosters.com/free-cs
I feel like lists of resources are OK, but with something like data science, which has its own branches and specialities, it would be good to have some kind of stack ranking of topics and information beyond just 'start here'. That way, a reader gets more and more conversant with the different ideas being thrown around. Also, I don't have a list of these handy, but I've found long annotated notebooks/blog posts of wor…
Thanks for putting these resources together, this is amazing
that's kinda awkward, but something is wrong with my monitor when I'm on that website.
I'm biased as a non-technical analyst with an academic history, but I'm concerned that the curriculum doesn't meet the most basic needs. Quoting the section 'An Academic Shortfall': "Academic credentials are important but not necessary for high-quality data science. The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the be…
This is an applied curriculum with a focus on specific technologies that enable an analysis-bent people to leverage their brains with technology. That's why data work is so beautiful -- it's space to demonstrate unquantifiables like curiosity, diligence, creativity, and grit.
The quality of your projects is likely a good metric for your aptitude for data work, which is why I strongly advise working on a personal project.
I'd love to get more pull requests with more materials that teach analysis!
And I also think you're taking the right approach building/hacking/doing rather than going to an institution. This stuff is so new that I find myself spending 5-10% of my time just trying to stay up on the latest tech.
One thing I would add though, data science is really 3 things usually: business knowledge, hacking and lastly stats/machine learning. The stats piece is shockingly easy as more and more modules/packages/libraries make it possible to create/train a model in 2 lines of code. (Applying the right model to your data set is difficult.)
The other shocking thing is that really, 80% of my time is probably spent hacking, and most of that is just spent on getting data.
It would be nice to have a clear list of assumed capabilities (for example, I am familiar with basic programming and have built a few websites, but I haven't taken a math class since senior year of high school, and don't think I remember enough about Calculus to do anything that assumes knowledge of it). Just a simple list of what level math, stats, and programming fluency this starts from would be great.
I feel like lists of resources are OK, but with something like data science, which has its own branches and specialities, it would be good to have some kind of stack ranking of topics and information beyond just 'start here'. That way, a reader gets more and more conversant with the different ideas being thrown around. Also, I don't have a list of these handy, but I've found long annotated notebooks/blog posts of wor…
The list is for beginners.Ofcourse data science is a huge domain and you can't actually make a roadmap to be a master in this field but I am sure this list will help people to get basic understanding of what to read and how to workout things.
The original post for this was what inspired me to create this: http://afaq.dreamhosters.com/free-cs
http://blog.zipfianacademy.com/post/46864003608/a-practical-...