Do get the 'Python for Data Analysis' book as it's a good and well written - but it's about Pandas (which you can then use for Data Analysis).

Hands-on Machine Learning with Scikit-Learn and TensorFlow [1] is more ML focused, but highly recommended. Out of the three books (Python for Data Analysis and Python Data Science Handbook) I learned the most from this one by far.

[1] https://github.com/ageron/handson-ml

If you're interested in python, my wife says good things about "Python for Data Analysis." Supposedly quite readable and practical.

http://shop.oreilly.com/product/0636920023784.do

Very cool. I'm currently going through O'Reilly's "Python for Data analysis", and can speak to it's quality as well.

I'd add the disclaimer that while Python for Data Analysis is a great resource for learning pandas, which itself is invaluable for data science in Python, the book doesn't cover machine learning or statistical inference in any great detail. That's not a criticism, it's just (mostly) beyond the scope of the book.

So, this article appears to be plagiarized directly (as in, word for word) from Wes McKinney's Python for Data Analysis (which is an excellent, excellent resource):

http://books.google.com/books?id=JtJAkfzds4wC&lpg=PA46&ots=p...

Seeing as the 'author' of this post was the submitter, I'd love to hear an explanation of what happened here.

I use this along with Chris Albons similar repo of recipes (http://shop.oreilly.com/product/0636920023784.do).

It is a great compliment to Wear McKinney's "Python for Data Analysis" it is more like a recipe book than the internals as Wes' book is. Also, JVP includes more than just Pandas and NumPy goodies.

Highly Recommend, and fork to create your own curated handbook.

For people who are not satisfied with knowing just Numpy and want to delve into its applications, I would recommend Wes McKinney's book "Python for data analysis":

http://shop.oreilly.com/product/0636920023784.do

I have the book and its been great reading so far. Ch 4 gives a nice introduction to Numpy (about 30 pages). Concise but also useful for immediate real-world usage.

I don't think I would call CAPM, the Fama-French 3-factor model, VaR, Black-Scholes and GARCH "the best of the best".

I would probably call the selection of topics "finance basics that no one in industry uses any more".

It may be suitable as a beginner's guide if the quality of the exposition is good enough, but my suspicion is that you would be better off getting a copy of "Python for Data Analysis" and a decent quant finance textbook.

Wes: Thanks!

Anyone reading this who wants to get started with Pandas: The early release of "Python for Data Analysis" (http://shop.oreilly.com/product/0636920023784.do) is already very helpful.

It depends on what kind of analysis you're doing. If you're doing pure exploratory stuff on large datasets, you'll mostly be using NumPy as a tool to help with Pandas. See McKinney's Python for Data Analysis (I believe it's from O'Reilly books) if you'd like a fairly good intro.

If you're more like me on using numerical methods, then I'd recommend first going through the math (I can be more specific, depending on what kind of work you'd like to do/are interested in) using Julia/MATLAB/Octave and then jump into NumPy stuff.

In my experience, I've found that using NumPy/SciPy numerical libraries effectively requires some knowledge of the underlying numerical algorithms since the default arguments sometimes require tweaking.

Here's my list:

Structure & Interpretation of Computer Programs (Abelman, Sussman - Lisp oriented, classic)
Algorithms in a Nutshell (OReilly)
Clever Algorithms (Jason Brownlee. Uses Ruby for examples)
Data Mining Analysis - Fundamental Algorithms (Mo. Zaki - ebook)
Python for Data Analysis (Wes McKinney)
Mining of Massive Datasets (Rajaraman, Leskovec, Ullman - ebook)

Enjoy.

Machine learning is a pretty big field. The Coursera course is very good. It uses Octave not Python, but what you learn will be easy to transfer. It is mostly focused on neural networks. If you don't already know linear algebra you should probably learn that first.

These are three very good O'Reilly books that all use Python:

- Programming Collective Intelligence: A broad and shallow survey of automated machine learning techniques.

- Data Analysis with Open Source Tools: Also a survey. More focused on manual data exploration.

- Python for Data Analysis: A pandas tutorial (and more). Very helpful to learn the ML tools in the python ecosystem.

Fitting all that into two months sounds challenging.

I have no idea if your company is interesting, but the questions are.

-What important truth do very few people agree with you on?

That the first duty of a society is to ensure that everyone is housed, healthy, fed and educated.

-What is a great company that no one has started yet?

I'm not much of a consumer. The only things that come to mind are more like non-profits and foundations dedicated to things like cheap and widely available pharmaceuticals, and other social improvement.

-What are some of your favorite websites?

HN, NYT, fuckinghomepage.com, python.org, stackoverflow, google news, google images, google translate.

-What are some of your favorite books?

Effective Computation in Physics, Python for Data Analysis, The Ringworld Series, Gone With the Wind, Adventures of Frog and Toad, Unix Power Tools, Catch 22, Working by Studs Terkel, A Canticle for Liebowitz, Travels With Charlie, Cannery Row, East of Eden, The Beautiful and the Damned, Time Enough for Love.

-What would you do if you knew that you only had 24 hours left on Earth?

Go outside.

-What do you really believe in?

I marvel at the unfathomable size and age of the universe, and the effectively zero chance that I could ever exist. I believe that when I look out at the universe, in at myself, and around at my world and fellow beings, that it's an instance of the universe observing and knowing itself. I also believe that we humans are wasting our world and our civilization, that this could be a true paradise of love, respect and potential, and I'm skeptically optimistic that that could come to pass.

I just ordered it 2 days ago directly from O'Reilly website (they currently have "buy 2 get 1 free" promotion, just use code OPC10, http://shop.oreilly.com/product/0636920025108.do). Really excited to read it when it comes. In case you're curious the other 2 books I ordered were "Python for Data Analysis" and "JavaScript Web Applications" which also look very promising.

Edit: Sorry, not a referral link.

I like R, it's more fun to write in than, say, Matlab. But it is unbelievably slow and has very little support for web-based projects. I'm reading "Python for Data Analysis" right now, and I'm pretty sure the book is converting me. Pandas' DataFrame includes R's data.frame's functionality as a strict subset, and that's enough for me to switch. I still might need to use rpy2 for awhile for certain packages though.

I'm a huge fan of pandas (http://pandas.pydata.org/) for data analysis. It offers a lot of the basic functionality of R, but everything is in python. The original author of pandas, Wes McKinney, even wrote a book about it: Python for Data Analysis (http://shop.oreilly.com/product/0636920023784.do).

One caveat I would mention about data analysis would be that statistics is not just number crunching. It is really a bit of an art to making sure you are looking at the right sample of data in the right way, and ensure you are accounting for all potential biases. Surprisingly, I have noticed as I've gotten more experience doing data analysis, it takes me longer to do and I make less confident assertions. But on the other hand, I now very rarely make assertions which were incorrect, which is extremely important. I believe that incorrect data analysis is significantly worse than no data analysis.

So, the advice I would give to people getting started is whenever you come to a conclusion by analyzing a particular piece of data, ask your "if I look at the data differently, can I come to the opposite conclusion?". You would be surprised how often the answer to this question is yes, and that is a good indicator that you a) need more data or b) cannot make a significant conclusion. This can be especially difficult when you are already sure you know the answer to a question even before you do the data analysis, but you really have to be disciplined about it.

Training or writing books about the software you wrote. For example Wes McKinney is the creator of the Pandas library (https://pandas.pydata.org/) and wrote the "Python for Data Analysis" book.

While the https://tailwindcss.com/ CSS library is open source, the authors make money by selling access to a component library https://tailwindui.com/

As a CS student, I'd really make sure your stats knowledge is solid. Perhaps take a class that covers stochastic finance (Black-Scholes, etc.) if available.

I learned the hard way that it is quite difficult to break into finance as a non-student, so do everything you can now to land that first gig. Good luck!

Some starting resources:

-Ernie Chan's books and blog (https://epchan.blogspot.com/)

-QuantStart has great starter material and a new book, although I haven't read it (https://www.quantstart.com/)

-"Inside the Black Box" (Narang) I've seen referenced a good bit but felt as though it leaned toward order execution and rather boring

-"Dark Pools" (Scott Patterson) a great story about the rise of algorithmic trading

-"Flash Boys' (Michael Lewis) offers a nice follow up (HFT), but considered a bit sensationalist

EDIT: If you're planning on using Python (a solid bet)...

-Python for Data Analysis (Wes McKinney) - Great, quick book for Pandas by former AQR (and now Two Sigma?) guy.

-Yves Hilpisch books: "Python for Finance" is introductory while "Derivative Analytics in Python" is quite math heavy.

Sorry for the late response, I completely forgot about this post!

Looks like you're on the right track though, "Think Stats" and Udacity's stats class were the main things I was going to recommend. I'd also recommend checking out IPython's web notebook for inline charts and general awesomeness, and the Pandas library for an R-style data frame built on top of NumPy. The best resources for learning about IPython are probably screencasts, and the author of Pandas has a book out named "Python for Data Analysis" that covers IPython, NumPy, Pandas and some matplotlib.

Location: CT/MA/RI

Remote: Yes, or onsite

Willing to relocate: yes

Technologies: Python, C, C++, rust, Linux (Ubuntu, Raspbian), Scheme (LISP), git, macOS

Resume: https://drive.google.com/file/d/1XVKev1z8WF3IpKJCzV4i3XSlBpE...

Email: on resume, RichardMBibeault@gmail.com

================

Recent physics graduate, math minor (bachelors of science), seeking employment in data science or software engineering. Highly ambitious, enjoys self directed learning and difficult problems.

I quite enjoy the learning process, and especially working on complex problems, breaking them down into their component pieces
, and figuring out a best approach for each piece.

Currently reading Python for Data Analysis, and Effective Modern C++.

Recently read a good chunk of TCP/IP administration 3rd edition, Land of Lisp, and Data Science from Scratch.

Since you are an Excel power user who also knows SQL, I would highly recommend that you check out the O'Reilly book "Python for Data Analysis." Pretty much the first chapter just has you very quickly run through examples of using a library of Python called PANDAS. Python is an intuitive enough coding language that you can focus on using it first, understanding it later, since large segments of it will make sense to you off the bat.

I could go on and on, but the short order is this: Pandas has an object called a DataFrame which maps 1 to 1 with a sheet from an Excel file. You can pull in two sheets of data from Excel, do merges, joins, iterations, you name it. I am a professional who also "has a job to do", and after two 1 hour sessions in the evening with Pandas, I immediately began using it at work to manage the huge quantity of spreadsheets my team is generating, combined with output from various databases. Anyone who knows SQL will love Pandas, because it allows you to very simply and easily conduct SQL style set logic on data without having any of it in a database.

Hey all,

I wrote a very detailed review of the book, Python for Data Analysis (2nd edition) by Wes McKinney.

Here is a high-level summary:

PDA is written very much like a reference manual, methodically covering one feature or operation before moving on to the next. The current version of the official documentation is a much more thorough reference guide if you are looking to learn pandas in a similar type of manner.

There is very little actual data analysis and almost no teaching of common techniques or theory that are crucial to making sense of data.

The vast majority of examples use randomly generated or contrived data that bear little resemblance to what data actually look like in the real world.

For the most part, the operations are learned in isolation, independent from other parts of the pandas library. This is not how data analysis happens in the real-world, where many commands from different sections of the library will be combined together to get a desired result.

Although the commands will work for the current pandas version 0.21, it is clear that the book was not updated past version 0.18, which was released in March of 2016. This is apparent because the resample method gained the on parameter in version 0.19 which was absent in PDA. The powerful and popular function merge_asof was also added in version 0.19 and is not mentioned once in the book.

There were numerous instances where it was clear that the book was not updated to show more modern code. For instance, the take method is almost never used any more and has been completely replaced by the .iloc indexer. There were also many instances were code snippets could be significantly transformed by using completely different syntax, which would result in much better performance and readability.

One of the most confusing things for newcomers to pandas are the multiple ways to select data with the indexers[], .loc, and .iloc. There is not enough detailed explanations for the reader to walk away with a thorough understanding of each.

I am not sure there is a generic answer to your inquiry. If you want to understand python itself you would be best off checking out the source code, building it yourself and then try to change some language features for fun.

Other than that I'd recommend that you find a domain you are particularily interested in and get some books on the specific aspects of that, for example Python for Data Analysis. http://shop.oreilly.com/product/0636920023784.do

This allows you to focus on what to do with python and the eco system of frameworks and so on.

If you want to learn how to use Python effectively, I believe you should decide what domain to apply it in. Python is no end in itself, as is the case with most other tools...

Location: CT/MA/RI

Remote: Yes, or onsite

Willing to relocate: maybe

Technologies: Python, C, Debian-based Linux, git

Resume: https://drive.google.com/file/d/13f_QTtwTMI0UsdxiCprqoThnq2v...

Email: on resume, RichardMBibeault@gmail.com

================

Recent physics graduate, math minor (bachelors of science), seeking employment in data science or software engineering.
Highly ambitious, enjoys self directed learning and difficult problems.

Currently reading TCP/IP administration 3rd edition, Data Science from Scratch, and Python for Data Analysis.

Can anyone recommend a good python book for someone learning Data Analysis/Wrangling? I've listed a few options below.

Python for Data Analysis - http://shop.oreilly.com/product/0636920023784.do - I'm working on this one now.

Data Wrangling with Python - http://shop.oreilly.com/product/0636920032861.do

Fluent Python - http://shop.oreilly.com/product/0636920032519.do

Learning Predictive Analytics with Python - http://shop.oreilly.com/product/9781783983261.do

Data Visualization with Python and JavaScript - http://shop.oreilly.com/product/0636920037057.do

Thanks for any comments!

The library support for data science with python has improved a lot in the last few months... There are lots of shiny new options for web development, but I think python is gaining momentum rather than losing it for data analysis and scientific programming.

I expect that user base will increase even faster after Wes McKinney's "Python for Data Analysis" book is published.

I'd go with Python and specifically the scientific + numerical libraries. There are books like "Python for Data Analysis" from O'Reilly.

Five years ago I would have said "R", but Python enthusiasts have been replicating what R does in Python at a frantic pace, and R will never really replicate what Python brings to the table.