Reddit reviews Data Science from Scratch: First Principles with Python
We found 18 Reddit comments about Data Science from Scratch: First Principles with Python. Here are the top ones, ranked by their Reddit score.
Oreilly Associates Inc
Yeah, the course follows most of the book's content, though there are some chapters that the course doesn't cover. But it's a nice supplement regardless.
I don't really know of any follow up material off the top of my head. I'd recommend learning about version control (like git) and can recommend the free books Version Control by Example and Pro Git. Other than that, I've noticed that Data Science from Scratch is doing very well on Amazon, so you might want to check that out.
I usually recommend this book for this sort of problem: https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X
In it you'll get your feet wet with respect to basic python and be exposed to how you would implement some core algorithms from scratch. Once you know that it should be relatively straightforward to move to the higher level libraries.
It's important to note that there aren't really "equivalent functions" mapping R to python. This is because R and python optimize for different things. R is a declarative analysis language- you tell it what you want it to do, not how to do it. Python is a full featured programming language also used for software development, so it supports many different paradigms (OO, functional, etc.). There are component libraries such as sklearn that implement declarative apis that will let you say things like "fit a model with these characteristics" or pandas that lets you say things like "what is the average of value in all of these columns". But in general python itself doesn't really work that way. You build things bottoms up.
The best chapters are the ones where he covers the ML method from scratch (like ANN). The ones that start with scikit-learn are OK, but you are really learning the scikit-learn API. The code layout is not nearly as good as O'Reilly books. His coding style leaves something to be desired (OO and mutations everywhere). As an alternative, I'd recommend the O'Reilly book: "Data Science From Scratch" by Joel Grus
https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X/ref=sr_1_1?s=books&ie=UTF8&qid=1467497329&sr=1-1&keywords=data+science+from+scratch
which covers every techniques from 'scratch.' His coding style is much better. Disadvantage is that all the routines are written in pure Python (slow).
Jose Portilla on Udemy has some good python based courses (and also frequents this subreddit). There's regularly sales or some sort of coupon code available to get any of the courses for $10-$15, so it's very reasonable.
For books:
https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/ref=asap_bc?ie=UTF8 ... it's not out yet, but due any day. You can also get preview access on sites like Safari Online (which would also have all the books below).
https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X/ref=sr_1_1
For general python:
https://www.amazon.com/Python-Crash-Course-Hands-Project-Based/dp/1593276036/ref=sr_1_1
https://www.amazon.com/Automate-Boring-Stuff-Python-Programming/dp/1593275994/ref=sr_1_1
No Starch Press, OReilly, APress and Manning generally have pretty good quality publications. I'd usually skip anything from Packt, unless it's specifically received good reviews.
Thanks! I can't wait to slay this beast. I timed my defense such that I could go let it all out at a Power Trip show a few days later, haha.
Python seems pretty intuitive to me in my initial tinkering; I also come from a Matlab/R background. I'll definitely check out pandas and scikit-learn! Do you have any suggestions for resources to efficiently learn Python? I'm working on Data Science From Scratch right now.
I have a friend who recently graduated from my same program and is now working as a data scientist at a financial startup in CA. He said the exact same things. I can't wait to make more than just slave wages....
One more question: Any recommendations for an R Studio-like IDE for Python in OSX?
If your calculus needs brushing up then I am guessing that you will probably benefit from putting some effort into linear algebra too. Just a guess though.
The Sage Hanbook of Quantitative Methods in Psychology is aimed at advanced graduate students and working researchers. The Oxford Handbook of Quantitative Methods in Psychology, Volume 1 and Volume 2 is even more comprehensive with Volume 1 covering some more philosophical topics not covered in the Sage Handbook.
An introduction to programming and computer science like this one (there are many others) is probably a good idea. You can also jump straight into a basic introduction to data science like Data Science from Scratch: First Principles with Python. The author can be amusing. Consider the quote in the preface:
> "There is a healthy debate raging over the best language for learning data science. Many people believe it’s the statistical programming language R. (We call those people wrong.) A few people suggest Java or Scala. However, in my opinion, Python is the obvious choice."
I would highly recommend Aurélien Géron's book. The first half is an introduction to standard machine learning techniques, which I would recommend reading through if you have little familiarity. The second half is dedicated to neural networks and takes you from the basics up to very results from very recent (2017) literature. It has examples building networks both from scratch and with TensorFlow.
If you want to dive deeper, the book Deep Learning is a little more theoretical, but lacks a lot of low level detail.
Joel Grus's "Data Science From Scratch" is another good reference.
Then learning Python would be very useful for you. I have used the book called Automate the Boring stuff (Free).
For data science and machine learning I use: Data Science from Scratch and Hands on Machine Learning with Scikit-learn and Tensorflow.
For AI I have used Artificial Intelligence: A Modern Approach (3rd ed.).
I don't know of any MOOCs that use Keras, so your best bet might be going through their documentation.
If you are looking for a Data Science MOOC that uses Python, University of Michigan has one here.
Also, another great resource is Joel Grus's book called Data Science from Scratch.
Hello, I am an undergrad student. I am taking a Data Science course this semester. It's the first time the course has ever been run so it's a bit disorganized but I am very excited about this field and I have learned a lot on my own.I have read 3 Data Science books that are all fantastic and are suited to very different types of classes. I'd like to share my experience and book recommendations with you.
Target - 200 level Business/Marketing or Science departments without a programming/math focus.
Textbook - Data Science for Business https://www.amazon.com/gp/product/1449361323/ref=ya_st_dp_summary
My Comments - This book provides a good overview of Data Science concepts with a focus on business related analysis. There is very little math or programming instruction which makes this ideal for students who would benefit from an understanding of Data Science but do not have math/cs experience.
Pre-Reqs - None.
Target - 200 level Math/Cs or Physics/Engineering departments.
Textbook -Data Mining: Practical Machine Learning Tools and Techniques https://www.amazon.com/gp/aw/d/0123748569/ref=pd_aw_sim_14_3?ie=UTF8&dpID=6122EOEQhOL&dpSrc=sims&preST=_AC_UL100_SR100%2C100_&refRID=YPZ70F6SKHCE7BBFTN3H
My comments: This book is more in depth than my first recommendation. It focuses on math and computer science approaches with machine learning applications. There are many opportunities for projects from this book. The biggest strength is the instruction on the open source workbench Weka. As an instructor you can easily demonstrate data cleaning, analysis, visualization, machine learning, decision trees, and linear regression. The GUI makes it easy for students to jump right into playing with data in a meaningful way. They won't struggle with knowledge gaps in coding and statistics. Weka isn't used in the industry as far as I can tell, it also fails on large data sets. However, for an Intro to Data Science without many pre-reqs this would be my choice.
Pre-Req - Basic Statistics, Computer Science 1 or Computer Applications.
Target - 300/400 level Math/Cs majors
Textbook - Data Science from Scratch: First Principles with Python
http://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X
My comments: I am infatuated with this book. It delights me. I love math, and am quickly becoming enamored by computer science as well. This is the book I wish we used for my class. It quickly moves through some math and Python review into a thorough but captivating treatment of all things data science. If your goal is to prepare students for careers in Data Science this book is my top pick.
Pre-Reqs - Computer Science 1 and 2 (hopefully using Python as the language), Linear Algebra, Statistics (basic will do, advanced preferred), and Calculus.
Additional suggestions:
Look into using Tableau for visualization. It's free for students, easy to get started with, and a popular tool. I like to use it for casual analysis and pictures for my presentations.
Kaggle is a wonderful resource and you may even be able to have your class participate in projects on this website.
Quantified Self is another great resource. http://quantifiedself.com
One of my assignments that's a semester long project was to collect data I've created and analyze it. I'm using Sleep as Android to track my sleep patterns all semester and will be giving a presentation on the analysis. The Quantified Self website has active forums and a plethora of good ideas on personal data analytics. It's been a really fun and fantastic learning experience so far.
As far as flow? Introduce visualization from the start before wrangling and analysis. Show or share videos of exciting Data Science presentations. Once your students have their curiosity sparked and have played around in Tableau or Weka then start in on the practicalities of really working with the data. To be honest, your example data sets are going to be pretty clean, small, and easy to work with. Wrangling won't really be necessary unless you are teaching advanced Data Science/Big Data techniques. You should focus more on Data Mining. The books I recommended are very easy to cover in a semester, I would suggest that you model your course outline according to the book. Good luck!
Data engineering is definitely more in line with my background and experience, though data science is fascinating to me and something I'd like to learn both for my own edification and to provide better support to colleagues in any future data-oriented roles.
Thanks for the blog recommendation, I'll take a look. I did just pick up Data Science from Scratch to get me started and I'll go from there.
Thank you very much for the thoughtful feedback and recommendations!
Thanks for the response.
Yea I have 2 quarters and summer school before transfer to university. Currently taking calculus I and next quarter calculus II.
I started that coursera course but realized I should probably go to school and learn math there since my parents are paying for it.
I just finished the statistics course offered at my junior college and am reading [Data Science From Scratch](http://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X "").
I'm a sophomore in college wanting to get in to the data science field after I graduate. I'm currently learning Python in a class of mine and I'm looking to do some learning on my own. I've found two books, Data Science from Scratch: First Principles with Python and Data Science from Scratch: Practical Guide with Python My roommate has a copy of the first book and I've looked through it some. I'm wondering if anyone has experience with either of these, or any other resources that would be helpful for me.
Thanks for your help!
First off, thank you for your service!
I hate to say it but you've got quite a lot of ground to make up. It's hard for me to gauge whether you have the coding skills needed. I get the impression that it's mostly sys admin stuff, which is good but not really sufficient (correct me if I'm wrong). You may want to teach yourself python if you don't use it yet.
The Coursera class on machine learning is something you should look into, since it will introduce you to a large body of knowledge that is critical for DS and probably all new to you.
I also encourage reading a book on data science, which would give you a good overview of the field as a whole and let you assess where the gaps are in your knowledge. I published one recently, which has great coverage of topics but has gotten mixed reviews so far. Here's another one which has better reviews, and is by a guy I know and respect.
The books I already mentioned in this thread will cover that. That said, I am generally anti-test and pro-estimation.
If you're already a proficient programmer. Try "Data Science from Scratch". I've found it to be one of the better books on the mechanics that underpin a lot of the work.
What resources would you recommend for newbies? I'm currently reading Data Science from Scratch(Grus) and Python for Data Analysis(McKinney). Anything else I should check out?
Love the blog!
Looks good, and seems to have good reviews. May well order that.
However, I am more after online learning style courses that include online tutorials and videos. More my style of learning.