(Part 3) Best databases & big data books according to redditors
We found 1,990 Reddit comments discussing the best databases & big data books. We ranked the 541 resulting products by number of redditors who mentioned them. Here are the products ranked 41-60. You can also go back to the previous section.
Yes, generally accepted, oh, and Universal Turing Machine to be more specific. Most people wouldn't consider a watch to be a computer, though it is technically a Turing machine. The difference is that a UTM can simulate any other TM.
Source: Page 15 of this book gives a definition of a computer as consisting of five parts: input, output, memory, datapath, and control. If you think about it, those are parts of a UTM. The control is the states of the UTM, the input and output are on the tape, as is the memory and the datapath. This may seem like cheating, but the same disk in your computer stores programs and data (both input and output).
http://www.amazon.com/Computer-Organization-Design-Third-Edition/dp/1558606041/ref=cm_lmf_tit_7
http://en.wikipedia.org/wiki/Universal_Turing_machine
There, now a theoretical computer science doctorate told you.
I'm pretty sure this book talked about how easy it was to scrape facebook before they locked down their API.
https://www.amazon.com/Mining-Social-Web-Facebook-LinkedIn/dp/1449367615/
A lot of people probably did this. I remember a talk given in my city, the guy had a few thousand people signup to his app and got millions of entries to his graph database
https://maxdemarzi.com/2013/01/28/facebook-graph-search-with-cypher-and-neo4j/
Popular game devs probably got oodles of data. Must have been awesome having a social graph of the US
Mitchandre has an excellent point. This program is called an REU or Research Experience for Undergrads, and most professors will really love having an eager undergraduate for the summer to learn more. I am a computational chemist pursuing my PhD at Georgia Tech with Dr. Sherrill, and I know that we are looking for REU students every summer. I have previously worked with Dr. Robert Harrison at the University of Tennessee/Oak Ridge National Labs and he also has REU students most summers.
Here is the REU program: http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5517&from=fund
If you are just interested in learning some computational chemistry for yourself, a book that I have found to be extremely useful and basic enough to understand at any level is Introduction to Computational Chemistry by Frank Jensen: http://www.amazon.com/Introduction-Computational-Chemistry-Frank-Jensen/dp/0470011874/ref=sr_1_1?ie=UTF8&s=books&qid=1267759772&sr=8-1
If you really wanna try and get your feet wet, try applying for an REU with any professors you can contact, plus its a great way just to see a different part of the country for a summer! Good luck
I'm assuming you are interested in designing schemas for relational databases, and not writing relational databases.
I really like CJ Date's SQL and Relational Theory and Database Design and Relational Theory. Use The Index, Luke is a nice site, too, with generally less formal information than Date's books, but more practical info about day-to-day problems.
If you'd like to dig under the covers, I'd start with Principles of Transaction Processing. Philip Bernstein knows what he is talking about, and is active in database research. Gray and Reuter's Transaction Processing is a classic, but is less approachable.
Excel does not make publication quality graphics. I recommend Matlab or matplotlib (python) whenever I review papers with Excel figures in them.
> How did you learn the best way to organize and present your data in your publications?
Cleveland's book is a good start. Although he is is maybe a little too austere. But in general, better to have too little ink than too much.
I bought this and have just begun reading it. seems good so far although in the first example they're pulling in Flask and some other stuff which, to me, made the code harder to think through.
be aware, the book is really short -- about 100 pages. (I don't have it on hand to confirm and I'm not convinced it's exactly 100 as Amazon says.) I didn't realize that and thought the price (compared to others I was considering) was good -- then it arrived... still, I think it's a good intro.
Essential Reading:
Hoowhee, how did this text get so... wally.
Bots are usually (fairly) simple programs, so Python will make it easy to get at all the common functionality you want (maybe looking for pattern matches in a piece of text, some math/analytics, saving files to your hard drive, converting images...) and in practice you'll mostly only be limited by what you can figure out how to do in your language of choice, regardless of the bot you're writing.
Wherever possible, you should use official APIs (which will often support Python these days), or at least third-party APIs that are built on top of the official ones. The APIs are sort of like a mediator between your bot and the service, or a menu of remotely-accessible functionality -- for twitter it might include things like "get the list of tweet IDs posted by this user ID in the last month" and "get the full text and metadata for this tweet ID." The set of functionality in that API determines what is and isn't possible for your bot to do (and depending on the service, it might actually hide a lot of complexity around sending messages to multiple servers, authenticating the request, etc)
When there's no API (or if the official API doesn't let you do something that you know should be possible) you usually have to switch to scraping. It's error-prone (could break any time) and frowned on by a lot of services (which is why you have to think about rate limiting and bans -- you may well be violating their terms of service, and either way you're using the service in unintended ways that might interfere with its normal functioning). "Unofficial APIs" are often just scrapers under the hood, tidied up into something that looks more like a normal API. I've written a ton of little scrapers in Python -- it really is a great tool for the job.
I suppose the other case is that some services can be built in standardized ways, so you don't need an official API from that particular company, because anyone else's API for that standard should be interoperable. That's common for databases, for example, but probably not the services you're talking about -- the popular web services are usually either proprietary, or a "standard" they invented that no one else actually uses, so you're basically stuck with the official API anyways.
For a lot of examples of integrating with public APIs, you can try Mining the Social Web from O'Reilly. I didn't actually spend a lot of time with that book personally (I wasn't expecting the sort of "cookbook" format with lots of examples and code) but it might cover some of the APIs that you're interested in.
"Killing Floor" -- Lee Child. Starts out his Jack Reacher series, which is absolutely phenominal.
As for what I'm currently reading... Computer Organization and Design, Third Edition: The Hardware/Software Interface. Turns out school doesn't leave me much leisure time.
You should first read "Transaction Processing, Concepts and Techniques" http://www.amazon.com/Transaction-Processing-Concepts-Techniques-Management/dp/1558601902/
There's also Michael Stonebraker's "Red Book"
After that, a lot of resources available on the Web... and source code of course.
https://www.amazon.com/Data-Warehouse-Lifecycle-Toolkit/dp/0470149779
https://www.amazon.com/Data-Warehouse-Toolkit-Definitive-Dimensional-ebook/dp/B00DRZX6XS
Get them to read the classics
This is a fairly useful review that I believe is available via google scholar for free: Wainer, H., & Thissen, D. (1981). Graphical data analysis. Annual review of psychology, 32, 191–241.
Tufte is useful for a historical overview and for inspiration, but he has a particular style that doesn't necessarily match up with the way that you or your audience think.
Hadley Wickham developed ggplot2 and his site is a good place to start browsing for guides to using it.
There's a pretty good o'reilly book on visualization as well, and Stephen Few's book does a really good job of enumerating the various ways you can express trends in data.
I'm freshening up on Python for work, and these are my materials:
Mastering Python Design Patterns https://www.amazon.com/dp/1783989327/ref=cm_sw_r_cp_awd_kiCKwbSP5AQ1M
Learning Python Design Patterns https://www.amazon.com/dp/1783283378/ref=cm_sw_r_cp_awd_BiCKwbGT2FA1Z
Fluent Python https://www.amazon.com/dp/1491946008/ref=cm_sw_r_cp_awd_WiCKwbQ2MK9N
Design Patterns: Elements of Reusable Object-Oriented Software https://www.amazon.com/dp/0201633612/ref=cm_sw_r_cp_awd_fjCKwb5JQA3KG
I recommend them to OP.
Two Python ML resources below. The former mixes math with working code. The latter is new and appears to be more of a guide to applying scikit-learn and/or milk specifically.
EDIT: All of the code from the first one is here: http://www-ist.massey.ac.nz/smarsland/MLBook.html
A graph theory project! I just started today (it was assigned on Friday and this is when I selected my topic). I’m on spring break but next month I have to present a 15-20 minute lecture on graph automorphisms. I don’t necessarily have to, but I want to try and tie it in with some group theory since there is a mix of undergrads who the majority of them have seen some algebra before and probably bored PhD students/algebraists in my class, but I’m not sure where to start. Like, what would the binary operation be, composition of functions? What about the identity and inverse elements, what would those look like? In general, what would the elements of this group look like? What would the group isomorphism be? That means it’s a homomorphism with a bijective function. What would the homomorphism and bijective function look like? These are the questions I’m trying to get answers to.
Last semester I took a first course in Abstract Algebra and I’m currently taking a follow up course in Linear Algebra (I have the same professor for both algebra classes and my graph theory class). I’m curious if I can somehow also bring up some matrix representation theory stuff as that’s what we’re going over in my linear algebra class right now.
This is the textbook I’m using for my graph theory class: Graph Theory (Graduate Texts in Mathematics) https://www.amazon.com/dp/1846289696?ref=yo_pop_ma_swf
Here are the other graph theory books I got from my library and am using as references: Graph Theory (Graduate Texts in Mathematics) https://www.amazon.com/dp/3662536218?ref=yo_pop_ma_swf
Modern Graph Theory (Graduate Texts in Mathematics) https://www.amazon.com/dp/0387984887?ref=yo_pop_ma_swf
And for funsies, here is my linear algebra text: Linear Algebra, 4th Edition https://www.amazon.com/dp/0130084514?ref=yo_pop_ma_swf
But that’s what I’m working on! :)
And I certainly wouldn’t mind some pointers or ideas or things to investigate for this project! Like I said, I just started today (about 45 minutes ago) and am just trying to get some basic questions answered. From my preliminary investigating in my textbook, it seems a good example to work with in regards to a graph automorphism would be the Peterson Graph.
If you're looking for web app help, I would suggest looking into ASP.Net MVC. Unless there's a reason why you need webforms, I would avoid them. Check out MVC and Razor syntax. It's easier to work with and there's a clear separation of concerns with the HTML and C# code.
Check out Professional ASP.Net Design Patterns for guidance on good structuring of an ASP.Net webform/MVC project.
Questo è un classico anche se un po' datato: https://www.amazon.it/Effective-Java-Joshua-Bloch/dp/0321356683
Ti consiglio di abbinarlo a https://www.amazon.it/Java-Action-Lambdas-functional-style-programming/dp/1617291994 per le ultime novità di java 8 (che sono fondamentali).
Sono in inglese e te ne devi fare una ragione... il 99% del materiale di qualità è in inglese.
P.S. Nè html nè css sono "linguaggi"
1 book by far is Andy Kirk’s. Data Visualisation: A Handbook for Data Driven Design https://www.amazon.com/dp/1526468921/ref=cm_sw_r_cp_api_i_rjx3DbDVRPFDN
It’s very broad and accessible yet substantial. That’s the book I recommend to anyone who need to read just one book.
2 is RJ Andrews book Info We Trust: How to Inspire the World with Data https://www.amazon.com/dp/1119483891/ref=cm_sw_r_cp_api_i_gmx3Db0FDG9DC.
This is a wonderful book that I read as an ode to visualization as a medium. It’s more artistic than Andy’s book both in its topic and its execution.
3 book depends on your specific interest. Dashboards/tableau? https://www.amazon.com/big-book-dashboard/s?k=big+book+of+dashboard.
Data art? https://www.amazon.com/dear-data-book/s?k=dear+data+book
Data journalism/ storytelling? Data-Driven Storytelling (AK Peters Visualization Series) https://www.amazon.com/dp/B07CCZPKV3/ref=cm_sw_r_cp_api_i_Msx3DbF1GZMG8
Science of visualization? https://www.amazon.com/Information-Visualization-Perception-Interactive-Technologies/dp/0123814642
Visualization from an academic point of view? https://www.amazon.com/Visualization-Analysis-Design-AK-Peters/dp/1466508914
D3js? https://www.amazon.com/Interactive-Data-Visualization-Web-Introduction/dp/1449339735
Perhaps Bollobas' Modern Graph Theory?
You might look at the O'Reilly book "Mining the Social Web". I've found it very helpful and the author has even responded to my questions about how to get the virtual machine with the ipython notebooks up and running. Has code examples, explanations about the underlying technologies, and intros to things like natural language processing included.
http://www.amazon.com/Mining-Social-Web-Facebook-LinkedIn/dp/1449367615
Start with the source, Kimball:
http://www.amazon.com/Data-Warehouse-Toolkit-Dimensional-ebook/dp/B00DRZX6XS/ref=sr_1_1?ie=UTF8&qid=1375223918&sr=8-1&keywords=data+warehouse+kimball
If you're working in an enterprise environment, then most likely your data will live - at the source, in a transaction-based database (OLTP). For this, I'd recommend Database Design for Mere Mortals - it's a well written book that is more heavily based on the practical application of how your data is architected, designed and stored and less on the theoretical side of things - but it's written in a way I feel most any learned person can understand. For theoretical review, there's always the seminal work of E.F. Codd's A Relational Model For Large Shared Data Banks and also some of his follow up work The Relational Model for Database Management
From the analytical database side of things (Data Warehouses/BI Solutions) and, where hopefully you'll actually be pulling and manipulating your data from there is The Definitive Guide to Dimensional Modeling - this is a more verbose read - and not practical, but more thought experiment provoking and includes the business reasons why dimensional modeling should be used so that Data Science/Data Analytics professionals can get at their data - nevertheless - for most large companies this is the "foundation" by which your data sits on if you're a Data Scientist. I, unfortunately, do not have a good recommendation for the practical application of OLAP databases as I've never found one that generally tickled my fancy.
Just skimming through these and periodically reading through them should at least give you an idea about how your data is stored, which more importantly gives you an idea around how it can be pulled and manipulated by the systems within your company.
As an example, I had a hard time explaining once to a research assistant why I couldn't 100% match two free-text string fields with names in them to one another in a large data set. I tried explaining to him that while there is fuzzy string matching algorithms I can apply to a given data set (Like Jaro-Winkler or Levenshtein), that it wasn't always 100% and was an approximation - I guess he wanted me to further the field of Computer Science by making fuzzy string matching 100% and therefore doing what many CS and Stats gurus haven't been able to do -shrugs-.
Books and when possible building side projects.
List of Java-related books I've found helpful.
Clean Code
Java 8 in Action
Data Structures & Algorithms in Java
Test-Driven Java Development
The last book (Test-Driven Java Development) briefly introduces different testing frameworks for java. It gives you a good start to work off of on your own.
Ninja Edit: I'm also adding two websites that have rather useful examples/diagrams for different design patterns.
DZone
TutorialsPoint
Don't be. Basically everything here is either about conditional probability (naive bayes) or weights*inputs + bias > threshold calculations. The weights inputs stuff is basic neural networks. Support vectors machines, perceptrons, kernel transformations, are all about finding linearly separable classes (in some appropriate dimension).
For more info this will help http://en.m.wikipedia.org/wiki/Perceptron
If you are interested I really have enjoyed reading http://www.amazon.com/gp/aw/d/1420067184/ref=redir_mdp_mobile which explains really well a lot of machine learning stuff and demystifies the math
For some practical examples here are some blog posts I've written:
K means step by step in f#: http://onoffswitch.net/k-means-step-by-step-in-f/
Automatic fogbugz triage with naive bayes: http://onoffswitch.net/fogbugz-priority-prediction-naive-bayes/
I share the links mostly cause its nice to see a worked through, practical example with code. In the end a lot of these algorithms aren't that hard to implement with some matrix math
Anyways, hope this helps!
I haven't personally read it, but the table of contents of this book looks like it has some potential to be a more modern version of what the Mitchell book aimed to provide.
I may be teaching a course in ML next year; I may order a copy to see if it's any good. Has anyone else looked at it?
Powershell in depth is really good after you get done with the month of lunches.
It's an interesting book.
R's powerful
ggplot2 graphics system has a default output
style which follows many of these principles, and it looks good.
But it's not my favourite book in this area.
My favourite would be (both)
Bill Cleveland's books
After seeing references to Cleveland in the
R documentation
(for example, the
loess
and
lattice
packages),
I read both the Cleveland books, and found them extremely interesting.
There's a classic paper by Cleveland and McGill,
"Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods"
(you can download a PDF)
which is also interesting. (And if you find that interesting, you would
most likely enjoy the books mentioned above.)
The Cleveland books are not widely famous like
The Visual Display of Quantitative Information,
but I found them more appealing in a way that's kind of
hard to describe. But, very roughly
visualisations which are efficiently and accurately perceived.
visualisations based on a kind of minimalist aesthetic. Or
maybe like a philosopher trying to find the essence of a
visualisation.
The conclusions of the two approaches are not necessarily
incompatible. They would certainly agree on the
undesirability of most of the ridiculous
stuff
in the MS Excel plot menu. (So if Tufte stops people doing that, then the more people who read him, the better).
But when there's tension between the two approaches then I'd
choose the first (Cleveland).
For example, the
Tufte (minimalist) boxplots
manage to represent the same information as a box plot, but with less ink.
But they feel like they might not be as easy to read.
(See also "W. A. Stock and J. T. Behrens. Box, line, and midgap plots: Effects of display characteristics on the accuracy and bias of estimates of whisker length. Journal of Educational Statistics, 16(1): 1–20, 1991"
(abstract) )
My vote would be D3 over out of the box libraries like highcharts.
Since you're focusing on statistic, I'm guessing you'll want to do some decent custom visualizations to highlight certain aspects of the data. Typically most of the charting libraries work well for traditional vis (bar charts, etc) but fall apart trying to make more advanced visuals.
D3 has a learning curve, but it's not as high as people think. One book in particular really helped me understand how it works, and once you know, you'll never go back.
https://www.amazon.com/Interactive-Data-Visualization-Web-Introduction/dp/1449339735
This is the book I'm following. I was not very familiar with d3.js beforehand, but it serves as a solid introduction to the core concepts. Very easy to read and hands-on approach. The first three chapters (around 60 pages) are introductions to html, css, javascript, with some descriptions of basic syntax and whatnot. Starting chapter 4 it's pure D3.
Building scatter diagrams, bar charts, geodata, representing graphs with interconnected nodes, all sorts of fun stuff.
Frank Jensen's Introduction to Computational Chemistry is, in my opinion, one of the best books out there for computational chemistry. Jensen's book does a great job introducing the concepts and equations in a way that doesn't feel like you need years of background in math and physics to understand. This book lived on my desk while I was in grad school. From what I've seen, most university libraries have a copy of it in their collection.
There are books out there that go into far more detail about the various methods and give detailed mathematical proofs, like Ira Levine's Quantum Chemistry, but those are very dense and, even as someone with a PhD in the field, intimidating.
thenewboston.org is a really good tutorial website. I like the Head Start HTML & CSS book, then the O'Reilly book, PHP,mySQL,JavaScript, and CSS (all in one book)
Here is the link to the book. I am on chapter 4 and so far it is pretty good. They explain everything like you have never touch it before, which is good.
http://www.amazon.com/Learning-MySQL-JavaScript-Step---Step/dp/1449319262/ref=sr_1_2?ie=UTF8&qid=1343577123&sr=8-2&keywords=learning+php%2C+MySql%2C+and+JavaScript
Oh, and here is the one to the Head First HTML & CSS one
http://www.amazon.com/Head-First-HTML-Elisabeth-Robson/dp/0596159900
If you read both of these books, you will be well prepared to tackle web development projects. Then, when you want to learn more about a programming language (Like JavaScript) you can either buy the JavaScript book from O'Reilly, or watch the more advanced TheNewBoston tutorial videos.
Good luck!
This was the first PHP book that I ever read (previous version) and it will point you in the right direction. One thing I will recommend, the book offers most tutorials using MySQLi or PDO. ALWAYS do the PDO version, it is a much more current and secure standard. Don't worry if you don't know what that means, you will :)
The book covers creating records and some image manipulation. It is built around a fictional travel site, but you will be able to learn from that and reapply to that the building a CMS.
I'd recommend doing mathematics, It's much important than learning a language. It helps you grab the logic of solving a problem.
Discrete Mathematics by Rosen is the best book from my experience.
Graph Theory by Bollobas is recommended by many but i prefer Graph Theory by Douglas West
Algorithms by Cormen. No introductions needed this book encompasses most of the problems you'll encounter.
However if you're keen on learning a C/C++/Java i'd recommend the Head First Series from O'Reily .
Goodluck!
Beautiful Visualisation Here
O'Reilly Designing Interfaces Here
I am reading the first edition now and it is really good so it can't hurt. It is a great into, and a good reference even for people who know what they are doing.
I am also reading this for school as well http://www.amazon.com/Practical-Guide-Fedora-Enterprise-Edition/dp/0133477436/ref=pd_sim_14_2?ie=UTF8&dpID=517h5KJOQVL&dpSrc=sims&preST=_AC_UL160_SR129%2C160_&refRID=0T274B6GDEYV9EQ9WGSG but I hear http://www.amazon.com/Practical-Commands-Editors-Programming-Edition/dp/013308504X isn't bad either
Yo, I'm not getting that image, but at a base level I can tell you this -
Here's a good book for Python: http://www.nltk.org/book/
A link to some more: http://nlp.stanford.edu/~manning/courses/DigitalHumanities/DH2011-Manning.pdf
And for R, there's http://www.springer.com/us/book/9783319207018
and
https://www.amazon.com/Analysis-Students-Literature-Quantitative-Humanities-ebook/dp/B00PUM0DAA/ref=sr_1_9?ie=UTF8&qid=1483316118&sr=8-9&keywords=humanities+r
There's also this https://www.amazon.com/Mining-Social-Web-Facebook-LinkedIn/dp/1449367615/ref=asap_bc?ie=UTF8 for web scraping with Python
I know the R context better, and using R, you'd want to do something like this:
And that's that!
Head first is a great book but I found Professional ASP.NET Design Patterns by Scott Millett to be a little more practical for me.
My personal favorite is SQL for Smarties by Celko:
https://www.amazon.com/dp/0128007613/ref=cm_sw_r_cp_apa_1wTRAbG6R88WS
One warning - he doesn't hold back on advanced SQL, so you'll run into a handful of cases where MySQL doesn't support what you're trying to do, or even worse, does the wrong thing. You should be fine in postgres.
I'm reading a book called Beautiful Visualization that I strongly recommend.
http://www.amazon.com/Beautiful-Visualization-Looking-through-Practice/dp/1449379869
Think about any time someone tells a story with stats. Is it misleading? How could it be more objective. What are the trade offs? How could people misinterpret your visual?
What I'm talking about is pretty basic knowledge. I'd suggest googling for it first and reading through that.
If you're using an IDE (seriously, use an IDE), look for debugging in <your IDE>. It should give you what you need. You should be able to stop execution at a given breakpoint, step into and over method calls, and inspect the values of object and expressions. Bonus points for learning how to "Run to the cursor position" instead of dropping breakpoints everywhere and for figuring out conditional breakpoints. It's more complicated, but being able to debug a remote application is also really useful for most web applications.
After that, if you want to get into more systematic debugging, I recommend looking at Debugging: The 9 Indispensable Rules for Finding Even the Most Elusive Software and Hardware Problems
For SQL, again, read the basics and then play around with it in an SQL application. Honestly, for learning purposes, if you have it, MS Access is pretty good. MySQL is probably the most accessible free tool.
Learn how to get data, filter it, order it from a single table. Then how to use grouping and group level filtering with GROUP BY and HAVING. Then learn inner and outer joins.
That's going to put you way in front of most young devs.
If you want to really get into it, I recommend Joe Celko's SQL for Smarties, but that's kind of overkill.
I started iOS programming with : http://www.amazon.com/Beginning-iPhone-Development-Exploring-SDK/dp/1430216263/ref=sr_1_7?ie=UTF8&amp;qid=1372191579&amp;sr=8-7&amp;keywords=iphone+programming
Once I got past the basics, I got myself this : http://www.amazon.com/Core-Developers-Cookbook-Edition-Library/dp/0321884213/ref=sr_1_4?ie=UTF8&amp;qid=1372191615&amp;sr=8-4&amp;keywords=iphone+programming
I would check for newer versions of the books though.
By access another application's data it depends on what you mean. Can you access 3rd party applications' data? No, each application is sandboxed and unable to communicate with each other. If it's a base iOS application though, Apple allows you to make calls to several of the base functionalities for use. You can utilize location services as well as maps in your app.
One suggestion I would make if you're just getting started. I would look to HTML 5 and multiplatform development solutions. More and more, these are becoming the standard rather than native applications. Because of this, I wouldn't start with iOS programming, but javascript instead. Don't waste your time with base javascript, instead choose a library to work with. jQuery is my personal choice.
From there, you have the basic knowledge to start using cross platform tools such as Sencha Touch or Apache Cordova (formerly phone gap). With these tools, you can develop for iOS and Android at the same time. Cordova also allows you to write native code when HTML & Javascript just isn't getting the job done.
By "expensive calculation", I meant DFT. A semiempirical method such as PM6, PM3, AM1, ZINDO, etc. is much, much quicker to run and can often get you a good starting geometry for a DFT calculation. You'll need to use your eyeballs for this part though.
You usually want the best basis set you can afford. In this case, you want it to include d and f angular momentum functions to properly describe the wavefunction at the cobalt. A small basis set like 3-21G will not work. However, 6-311+G(d,p) is too costly and may fail; even though it has higher angular momentum functions, the '+' means a set of diffuse functions has been added. This will result in orbitals that are quite large but "fuzzy", potentially causing false overlap of orbitals between atoms. It's important for many anions, but unnecessary here. Something intermediate like 6-31G(d) might be acceptable for a geometry.
If you want to learn more, I highly recommend this book, this book, and maybe most of all this book, depending on how much modeling you're required to do.
Asp.net Wiki: Design Patterns Looks like a good place to start after a quick [google search] (https://www.google.com/webhp?sourceid=chrome-instant&amp;ie=UTF-8&amp;ion=1#hl=en&amp;sclient=psy-ab&amp;q=ASP.net%20web%20design%20patterns&amp;oq=&amp;aq=&amp;aqi=&amp;aql=&amp;gs_l=&amp;pbx=1&amp;fp=87c6230bd8e448f1&amp;ion=1&amp;bav=on.2,or.r_gc.r_pw.r_cp.r_qf.,cf.osb&amp;biw=731&amp;bih=536).
Design Patterns on Amazon has a bigger list of books. I like the look of Professional Asp.net and it seems well reviewed and more recent (2010).
I have this book and I can tell you that I have not read it.
Definitely. Don't forget atomic modeling, either. As I said, I'm not an architect, but this book I see commonly referenced around data warehouse modeling:
https://www.amazon.com/Data-Warehouse-Toolkit-Definitive-Dimensional-ebook/dp/B00DRZX6XS/ref=sr_1_1?ie=UTF8&amp;qid=1539091831&amp;sr=8-1&amp;keywords=kimball+modeling
It's probably also worth pointing out that data warehouse design can be simplified by obtaining a pre-built data model (either purchased from someone like IBM for specific sectors, or obtained open-source, such as OMOP for healthcare), or data warehouse design and modeling can be outsourced to consultants that do it for you. Those may not be great options for you, but if you have a mission critical need for your business, it might be nice to have access to an experienced architect.
I've read these and enjoyed them:
https://smile.amazon.com/Joe-Celkos-SQL-Smarties-Fifth/dp/0128007613/
PHP Solutions by David Powers was really good imo. I learned PHP in about a week with it. Helped me learn a bit about SQL, HTML, and CSS too.
Mining the Social Web
Not exactly what you're looking for but it's very helpful, imo
Hi,
I am starting out studying Data Science myself (not employed in the field). Here is what has been useful to me:
Data Quest has some free lessons and they are good. (They also have a weekly newsletter that has learning resources/articles).
Practical Statistics for Data Scientists has been very helpful in getting me up to speed on statistics (note that the code here uses R, not Python).
As someone who is a long time but intermittent Java developer myself I'd suggest focusing on Java 8 and in particular how the arrival of lambdas is influence the language and ecosystem. As an experienced OO developer I'd guess most other aspects won't present the same learning curve. Superficially even lambdas can appear as a method to reduce boiler plating, but the implications of this trend toward supporting an increased functional programming style run much much deeper I feel. With that in mind I would suggest either the book Java 8 in Action or Functional Programming in Java.
I just bought Learning Python Design Patterns. I haven't spent a long time with it so far, but my first few skims of it gave me a fairly good impression.
Check out The Accidental DBA from Redgate. It covers the basics from a standpoint of someone who has no background but is now responsible for keeping a SQL server running. Scan through it and see if there is anything you don't know. if so then dive into the "Inside Out" series of books from Microsoft Press.
if you want to get really into the weeds find SQL for Smarties for some in-depth discussion on why SQL (the language) behaves the way it does.
Java 8 in Action is what we use at work.
Edit: Stupid formatting...
Not as quick as OP's tutorial, but the "Interactive Data Visualization For The Web" book is good too.
http://www.amazon.com/Interactive-Data-Visualization-Scott-Murray/dp/1449339735
You can find it here on amazon.com:
http://www.amazon.com/gp/product/1420067184?ie=UTF8&amp;tag=oliviergrisel-20&amp;linkCode=as2&amp;camp=1789&amp;creative=390957&amp;creativeASIN=1420067184
(Please feel free to strip the reference to oliviergrisel-20 in the URL should you want not to tip me through the amazon affiliates program)
Thank you! This is exactly what I was looking for!!!! I didn't think anyone was going to give me a sufficient reply because there are a lot of books (sorry), but this is what I wanted. Where would you place the two books I linked, Principles and Techniques in Combinatorics and Introduction to Combinatorial Mathematics, Liu, in that list or would you consider studying them a redundant exercise? I also did not include this book in the list, but where would you place Problems from the Book and its accompanying Straight from the Book?
I will likely end up replacing the Graph Theory book I have in the list, by Berge, with Modern Graph Theory by Bellobas, since Berge doesn't have exercises, but I will assume it stays in the same order of the sequence.
I apologize for not initially including them. I did not realize that I did not. Also, are there any other topics you would recommend I cover for establishing a solid foundation. I didn't buy Rudin's Complex Analysis because I didn't know if that kind of thing was necessary. I don't even know what other branches of mathematics Complex Analysis relates to. There could be other topics I'm not aware of as well. Please don't hesitate to make more recommendations. I appreciate it.
OP, so that you know, you mention uncle Bob's "Clean Code", but your link redirects to the book "Clean Coder". They're both great, but different.
I've ordered the book Practical Statistics for Data Scientists: 50 Essential Concepts. Looks great to brush up the basics of statistics and machine learning. Since I haven't actually read it yet, take my input with a grain of salt.
This -isn't- the most suitable book...
Computer Organization and Design: The Hardware/software Interface
http://www.amazon.co.uk/Computer-Organization-Design-Interface-Architecture/dp/1558606041
http://books.google.co.uk/books?id=1lD9LZRcIZ8C&amp;dq=the+hardware+software+interface&amp;printsec=frontcover&amp;source=bl&amp;ots=o5CWvdKjLy&amp;sig=nu-R_qM4kpzyJBIKOyDwnMEEOcg&amp;hl=en&amp;ei=eps0S4XeI5jSjAeC77CmDg&amp;sa=X&amp;oi=book_result&amp;ct=result&amp;resnum=10&amp;ved=0CE0Q6AEwCQ#v=onepage&amp;q=&amp;f=false
BUT!
You know that black-box... that bit of magic where the symbolic stuff you see on your screen gets turned into the electrical patterns inside the chips which exist, and do stuff... the book explains all that in minute detail.
It'll kill the "magic black box" feel of computers, but give you an amazing insight into the physical workings of a computer program...
http://developer.apple.com/iphone/
You probably should register in the $99 iPhone Developer Program - Standard Program as soon as you can.
I read these books to get started:
Apple's sample code is usually helpful but can be buggy, e.g. in the Reachability sample.
I personally would say a design patterns book that is less abstract. I like it when I can link it back to what I'm doing.
This is one I read that I thought was pretty good. However with limited exposure to asp.net it will probably be cryptic and possible more advanced than you are ready for (some of the amazon review state this also). But even without, the first couple chapters are summary chapters that don't have much code and go over the "big" design patterns and concepts.
So maybe a later pickup, if you're in .NET you'll be doing ASP.NET eventually, so i'd say the book is relevant for you if not now sometime in the future.
http://www.amazon.com/gp/product/0470292784/ref=oh_details_o00_s00_i00
aah well if you primarily like webpage designing and management and sorts then you can start with HTML/CSS and move up to php,sql and javascript. I've seen many job openings for people efficient with the above.
Now if you want to go to the path of making apps and program then you can choose C++/Java/Python+swift(or swift alone if iOS developer is your focal point) which is very lucrative.
Personally I hated C++ and I am learning Java. I use the course book "Intro to Java Programming" by Daniel Liang which comes with a website that lets you preview examples which really helps. I've heard that python codes are easier to begin with but Java has the market. This article might help you decide what to go with. Also I have found learning html/css is very useful as many blogs and such allows you executive control over design with html/css. I am learning php and sql with the help of this book and this one which is really top notch. They require you to have prior knowledge on HTML and CSS so I'd recommend you to use codecademy to kickstart it all. They have HTML/CSS, JavaScript, python among others. I am really fond of their beautiful UI and learning process which helps a lot. At the end of the course they have where you have to build a project to complete the course which is really cool and exciting imo.
Good luck in your journey and get the 8.1, it's not bad as people make it out to be, it's kind of like OS X but windows people adore the start functionality and reporters who use mac join on the bandwagon. Also windows 7 requires cost to upgrade to 8.1 and possibly to 10 whereas 10 will most probably be free for 8.1 users. I adore the multi tasking functionality on the 8.1 and prefer it to 7.
Practical Statistics for Data Science is on sale right now on Amazon. Only $13!
Transaction Processing by Jim Gray
For cash-in-pocket noobs who like to have a book in hand I'd recommend any of these books by Mark G. Sobell.
A Practical Guide to Linux
Old (1997) but takes you from the basics to intermediate.
A Practical Guide to Linux Commands, Editors, and Shell Programming, 4th. Ed.
Also covers the basics but it's more focused on those subjects included in the title.
A Practical Guide to Ubuntu Linux, 4th. Ed
I have no experience with this book but I'm including it for completeness.
A Practical Guide to Fedora and Red Hat Enterprise Linux, 7th. Ed.
Same. No experience.
I highly recommend this series. They have books on developing for most systems, and there's also a few on game development
Powershell in a Month of Lunches - https://www.amazon.com/Learn-Windows-PowerShell-Month-Lunches/dp/1617291080/ref=sr_1_1?s=books&ie=UTF8&qid=1467232090&sr=1-1&keywords=learn+powershell+in+a+month+of+lunches
Then, when you get the hang of it, Powershell In Depth https://www.amazon.com/PowerShell-Depth-Don-Jones/dp/1617292184/ref=sr_1_1?s=books&ie=UTF8&qid=1467232074&sr=1-1&keywords=powershell+in+depth
Outside of that, check out /r/PowerShell and work through the questions that people ask. No better way to learn than practice.
Aside from Tufte, you might find Cleveland's Visualizing Data worthwhile. I'm reading Stephen Few's Now You See It: Simple Visualization Techniques for Quantitative Analysis now.
Also, try following some related blogs, like Nathan Yau's Flowing Data or Kaiser Fung's Junk Charts. You can get a sense of some appropriate and/or inappropriate ways of visualizing data from these.
Finally, once you get more familiar, get something like Murrell's R Graphics. This will help you understand the basics of the base R graphics capabilities so you can make what you want, exactly how you want. ggplot2 is awesome, too, but understanding the basics is really helpful. Hope that helps.
Just learning assembly will not really tell you about how your machine works. I recommend that you in addition to or instead read a few books on internals:
The hardware/software interface: http://www.amazon.com/Computer-Organization-Design-Interface-Architecture/dp/1558606041
Linkers and Loaders: http://darcs.olsner.se/Linker/linker-book/ (note this is no longer the author's original page which I can't find -- maybe he removed it from there, so you might want to buy the excellent book instead)
Maybe something like this?
Amazon Link
I started with Head First PHP & MySQL when I started years ago. Two books I loved the most though were Beginning PHP and MySQL From Novice to Professional and PHP Solutions Dynamic Web Design Made Easy.
Later this book helped me immensely PHP Objects, Patterns, and Practice.
Ultimately, php.net is your bible and you can learn everything from there alone. Post on forums and ask for help. Try something and stick with it, don't jump around creating dozens of projects but stick with one and expand it.
Also this
That and
A Practical Guide to Fedora and Red Hat Enterprise Linux (7th Edition) https://www.amazon.com/dp/0133477436/ref=cm_sw_r_cp_apa_3Qy8ybA04XHJJ
Eu sunt în general împotriva tutorialelor, cel puţin pe partea de programare. E greu de urmărit şi nu prea are de-a face cu procesul de învăţare ci mai degrabă cu cel de înmagazinare a unor cunoştinţe.
Cel mai bine ar fi la început să începi să citeşti o carte/un articol despre concepte ale programării în general pentru ca apoi să te apuci de sintaxa unui anume limbaj. Plus că îţi trebuie determinare şi multă răbdare.
Odată ce ai prins basicul limbajului, recomandarea mea ar fi să te implici într-un proiect la care poţi contribui cu cod. Codul ăsta va suferi o grămadă de transformări pentru ca la final să nu mai semene deloc cu cel de la început. Ştiu, sună descurajator dar ăsta e procesul natural prin care îţi îmbunătăţeşti skillurile.
Dacă vrei neapărat tutorial video, singura mea recomandarea ar fi PHP with MySQL Essential Training
with Kevin Skoglund
, însă repet: nu mi se pare cea mai fericită metodă de a învăţa programare. Cele mai sfinte lucruri în PHP sunt: cărţile, practica şi http://www.php.net/manual/en/index.php!
Dintre cărţi ţi-aş recomanda:
Astea fiind spuse, îţi urez spor la treabă!
The book Practical Statistics for Data Scientists is worth a look. https://www.amazon.com/Practical-Statistics-Data-Scientists-Essential/dp/1491952962
> For those who prefer video lectures, Skiena generously provides his online. We also really like Tim Roughgarden’s course, available from Stanford’s MOOC platform Lagunita, or on Coursera. Whether you prefer Skiena’s or Roughgarden’s lecture style will be a matter of personal preference.
>
> For practice, our preferred approach is for students to solve problems on Leetcode. These tend to be interesting problems with decent accompanying solutions and discussions. They also help you test progress against questions that are commonly used in technical interviews at the more competitive software companies. We suggest solving around 100 random leetcode problems as part of your studies.
>
> Finally, we strongly recommend How to Solve It as an excellent and unique guide to general problem solving; it’s as applicable to computer science as it is to mathematics.
>
>
>
> [The Algorithm Design Manual](https://teachyourselfcs.com//skiena.jpg) [How to Solve It](https://teachyourselfcs.com//polya.jpg)> I have only one method that I recommend extensively—it’s called think before you write.
>
> — Richard Hamming
>
>
>
> ### Mathematics for Computer Science
>
> In some ways, computer science is an overgrown branch of applied mathematics. While many software engineers try—and to varying degrees succeed—at ignoring this, we encourage you to embrace it with direct study. Doing so successfully will give you an enormous competitive advantage over those who don’t.
>
> The most relevant area of math for CS is broadly called “discrete mathematics”, where “discrete” is the opposite of “continuous” and is loosely a collection of interesting applied math topics outside of calculus. Given the vague definition, it’s not meaningful to try to cover the entire breadth of “discrete mathematics”. A more realistic goal is to build a working understanding of logic, combinatorics and probability, set theory, graph theory, and a little of the number theory informing cryptography. Linear algebra is an additional worthwhile area of study, given its importance in computer graphics and machine learning.
>
> Our suggested starting point for discrete mathematics is the set of lecture notes by László Lovász. Professor Lovász did a good job of making the content approachable and intuitive, so this serves as a better starting point than more formal texts.
>
> For a more advanced treatment, we suggest Mathematics for Computer Science, the book-length lecture notes for the MIT course of the same name. That course’s video lectures are also freely available, and are our recommended video lectures for discrete math.
>
> For linear algebra, we suggest starting with the Essence of linear algebra video series, followed by Gilbert Strang’s book and video lectures.
>
>
>
> > If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.
>
> — John von Neumann
>
>
>
> ### Operating Systems
>
> Operating System Concepts (the “Dinosaur book”) and Modern Operating Systems are the “classic” books on operating systems. Both have attracted criticism for their writing styles, and for being the 1000-page-long type of textbook that gets bits bolted onto it every few years to encourage purchasing of the “latest edition”.
>
> Operating Systems: Three Easy Pieces is a good alternative that’s freely available online. We particularly like the structure of the book and feel that the exercises are well worth doing.
>
> After OSTEP, we encourage you to explore the design decisions of specific operating systems, through “{OS name} Internals” style books such as Lion's commentary on Unix, The Design and Implementation of the FreeBSD Operating System, and Mac OS X Internals.
>
> A great way to consolidate your understanding of operating systems is to read the code of a small kernel and add features. A great choice is xv6, a port of Unix V6 to ANSI C and x86 maintained for a course at MIT. OSTEP has an appendix of potential xv6 labs full of great ideas for potential projects.
>
>
>
> [Operating Systems: Three Easy Pieces](https://teachyourselfcs.com//ostep.jpeg)
>
>
>
> ### Computer Networking
>
> Given that so much of software engineering is on web servers and clients, one of the most immediately valuable areas of computer science is computer networking. Our self-taught students who methodically study networking find that they finally understand terms, concepts and protocols they’d been surrounded by for years.
>
> Our favorite book on the topic is Computer Networking: A Top-Down Approach. The small projects and exercises in the book are well worth doing, and we particularly like the “Wireshark labs”, which they have generously provided online.
>
> For those who prefer video lectures, we suggest Stanford’s Introduction to Computer Networking course available on their MOOC platform Lagunita.
>
> The study of networking benefits more from projects than it does from small exercises. Some possible projects are: an HTTP server, a UDP-based chat app, a mini TCP stack, a proxy or load balancer, and a distributed hash table.
>
>
>
> > You can’t gaze in the crystal ball and see the future. What the Internet is going to be in the future is what society makes it.
>
> — Bob Kahn
>
> [Computer Networking: A Top-Down Approach](https://teachyourselfcs.com//top-down.jpg)
>
>
>
> ### Databases
>
> It takes more work to self-learn about database systems than it does with most other topics. It’s a relatively new (i.e. post 1970s) field of study with strong commercial incentives for ideas to stay behind closed doors. Additionally, many potentially excellent textbook authors have preferred to join or start companies instead.
>
> Given the circumstances, we encourage self-learners to generally avoid textbooks and start with the Spring 2015 recording of CS 186, Joe Hellerstein’s databases course at Berkeley, and to progress to reading papers after.
>
> One paper particularly worth mentioning for new students is “Architecture of a Database System”, which uniquely provides a high-level view of how relational database management systems (RDBMS) work. This will serve as a useful skeleton for further study.
>
> Readings in Database Systems, better known as the databases “Red Book”, is a collection of papers compiled and edited by Peter Bailis, Joe Hellerstein and Michael Stonebreaker. For those who have progressed beyond the level of the CS 186 content, the Red Book should be your next stop.
>
> If you insist on using an introductory textbook, we suggest Database Management Systems by Ramakrishnan and Gehrke. For more advanced students, Jim Gray’s classic Transaction Processing: Concepts and Techniques is worthwhile, but we don’t encourage using this as a first resource.
>
> (continues in next comment)
Professional ASP.NET Design Patterns
My suggestion would be to go to a library, walk down the aisle scanning each graph theory textbook for a few minutes and then choose one which clicks. Having access to exercises is super important (accompanying solutions is nice.)
Having access to a few textbooks is nice because if you get stuck on a concept in your 'main' text you can see if the others explain the idea better (wikipedia is also good for this.) Be alert for problems arising from differences in notation if you do this though.
Free online text book: http://diestel-graph-theory.com/basic.html
One I purchased: http://www.amazon.com/Modern-Graph-Theory-Bela-Bollobas/dp/0387984887
Combinatorics via exercises: http://www.amazon.com/Combinatorial-Problems-Exercises-Second-Lovász/dp/044481504X
Check a university library for copies of the latter two.
University courses:
http://www1.maths.leeds.ac.uk/~pmt6sbc/3032/3032.html
http://users.utu.fi/harju/graphtheory/graphtheory.pdf
http://www-math.ucdenver.edu/~wcherowi/courses/m4408/gtln.html
http://www.personal.psu.edu/cxg286/Math485.pdf
http://www.warwick.ac.uk/~masgax/notes.pdf
http://www.cs.elte.hu/~karolyi/GT/index.html
http://ocw.mit.edu/courses/mathematics/18-315-combinatorial-theory-introduction-to-graph-theory-extremal-and-enumerative-combinatorics-spring-2005/
For iPhone? Get a book. This one is ok. Not great though. How is your programming? Are you a programmer just looking to learn AS3? Or are you a non-programmer?
I don't know if this will help, but this is the method I took (I have a background in graphic design, marketing, and advertising). I first went to some hacker friends who are very good at what they do and showed them all kinds of mockups, illustrations, and venn diagrams, everything about how everything would work. I even wrote out the logic for some of my algorithims in plain english.
Their response: they thought it was an interesting idea, but being the awesome programmers that they were, they had very high paying jobs, and their own side jobs, so they didn't have time to work on my project. They did however give me some great advice: (Note I already knew css and html) They said start with these 3 books:
Jquery Novice to Ninja which really helped me get started with basic animation techniques and understand a bit of Jquery. This really helped me lead into javascript programming with Dom Scripting. This book was great for really fleshing out what the jquery was doing, and helped me with a lot of other questions. I feel very comfortable working in jquery and javascript now. The last book they recommended for me was php solutions to teach me about teh back end of what I wanted to build.
Now learning on my own with just a couple books, stackoverflow, and a few questions sent to my friends/mentors was not easy by any means, but I don't regret it for a second.
Some might not like my choices of using jquery, javascript, or php, but one of the reasons my friends recommended me using those languages in particular was how much documentation is readily available, making them a little easier then some other launguages to learn on your own.
Good luck to you.
http://www.amazon.com/Introduction-Computational-Chemistry-Frank-Jensen/dp/0470011874/ref=pd_bxgy_b_img_b
This is a good book. Also you should become familiar with programs like Gaussian (http://www.gaussian.com/) and BOSS (http://zarbi.chem.yale.edu/software.html)
I'm afraid I can't answer your question specifically, but Stephen Marsland has written a great introductory level machine learning book that has all of the example code written in python.
I would check out Ben Fry's book first.
Then Beautiful Visualization.
There is another good McCandless eyecandy.
Manuel Lima did an amazing book on network visualization with excellent essays from other people. Visual Complexity. Network vis is very difficult and if you want to "game up" understanding the taxonomy he built for network vis will give you a real perspective on the taxonomy in other types of vis.
There are things outside of the "take data and render visualization" world that are critical to data vis, imo. For moving data vis, start with the godfather, Muybridge
And look way way back for the long human history of data vis in cartography with stuff like Cartographia.
Hope to see some more books and discvoer a reading list on this thread! Great idea for a post.
Hi,
I am currently a grad student in comp/theory.
A background in programming is not necessary, but you may find yourself wanting to get familiar with programming for data analysis. You should feel comfortable in the terminal to begin something like this (but I've seen people with almost no computer skills learn to run MD simulations within a few weeks of struggle).
The points I give below apply to most questions of this sort.
A good starting place is considering what do I want to simulate?
Consider the underlying physics. If you can write the formulas of what you want to observe with statistical mechanics consider molecular dynamics or monte carlo. If you require quantum mechanics use post Hartree-Fock or DFT.
Do you want to simply look at a change in structure? MD will work. Do you want to look at the process of polymerization? That requires bond formation which requires quantum mechanics. A note: SCF is not orthogonal to MD; there exists QM/MD origrams such as terachem but its sounds fairly impractical to apply to a polymer.
Lets say you wanted to observe the unfolding of a polymer w.r.t. temperature. This could be observed with molecular dynamics. Now you need to consider what program to use. I don't know your system but NAMD is a good starting place because it is fairly user friendly. If you'd like some reading suggestions I'd suggest skimming Jensen's comp chem Let me know if you have more questions
DISCLAIMER:
I am a QM guy specifically quantum Monte Carlo, but I work closely with NAMD users
Actualy, this is one of the first things you get taught if you take a visualization class, Never ever alter the data. And the slope is a very basic property of the data.
> it simply wouldn't be effective communication to make the decrease look really small
Again, if the data doesn't provide a big decrease, don't make it look like it does.
e.g. Try reading this nice one. http://www.amazon.com/Interactive-Data-Visualization-Scott-Murray/dp/1449339735
For learning about RDBMS internals (i.e. not how to use one), you should at least add Architecture of a Database System by Stonebraker et al. to your reading list. It'll give you a decent overview.
From there, it depends a little on what you're interested in. Transaction processing? Replication? Query evaluation? Query optimizing? The latter specifically is hard to find much useful on in books --- most useful information is found in scientific papers. (I just wrote a master's thesis about query optimization... :)
If you want something inbetween, i.e. necessary knowledge about internals to use the systems more efficiently, Physical Database Design is worth a read.
It's a poor representation of data. In pie charts you compare angles. Humans are poor at comparing the magnitudes of angles. Without the table, labels with the actual numbers, etc. it would be very difficult to compare the information.
For instance, it is difficult based just on the visualization if Instinct or Valor has more players. A bar, column, or dot plot will show things much better. Humans are far better at perceiving differences in length or position. That table on the right is necessary - that means the pie chart is useless.
If you are serious about designing visualizations of data, I suggest you read some books by Willilam Cleveland or Edward Tufte.
EDIT: Here is article I often share with people on this topic.