Reddit mentions: The best business statistics books

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!

I work with econ/stat people who are great at running and interpreting models and thinking about causality issues, but don't know much about programming. They've specialized, I get it, but in the future teams would benefit from everyone knowing some basics. It'll also make stats people more productive and help prevent errors. Also also, econ, other sciences, and the policy world really should embrace open source, open science, open access, etc.

But anyway, here's how to do it.

Below are a bunch of random resources. If you're looking for free courses, Software Carpentry has a bunch on the topics listed below and more. The terminal and Bash, Python, R, Matlab, Git, SQL, GNU Make, continuous integration, and data visualization. Data Carpentry has lessons for some of these topics, geared more toward social scientists. Apparently they're developing a course for doing econ with Bash(?). If you're into macro or computational stuff and want to learn Python, can't do wrong with QuantEcon.

I'll echo what the other guy said. If you have a Mac, cool. If not, consider dual booting with linux. It has a reputation for being difficult to use, but Ubuntu, Mint, and ElementaryOS are all very simple and work just like what you're used to in Proprietary World. It's possible to do the following with Windows, but requires a more setup work.

Learn to use the terminal (this is the point of using Mac or Linux, they come with a terminal and unix tools). Here's a decent book on the basics. Learn to navigate around your filesystem, run programs from the terminal, and use a bit of Bash. You can probably skip the chapters on actually programming with Bash. Bash as a programming language is cool, but not super necessary, and kinda quirky. It wouldn't be a waste of time though, since you can do certain things in Bash very quickly and easily. And you'll be a master haxxer.

Check out Data Science at the Command Line for a decent overview of stats programming in a linux environment. Goes over basic Python and R, and other tools to make life simple. There's also The Plain Person's Guide to Plain Text Social Science, geared toward people who do science but may not do programming atm. Covers more useful tools.

Learn Python or R or both. If Python, here. If R, here. If you're into ML, here for Python and possibly here for R but the code may be dated. Still, that book is The intro book for ML.

Learn Git. You should be in the habit of tracking changes you make to your code and the data/results it produces, especially if your data is being shared with anyone. If you use R, here's a great intro to Git and RStudio's fantastic Git integration.

Learn SQL. This one's harder to pick up on your own, at home, since you need a database set up to query. Look at the software/data carpentry courses.

Learn Docker. It makes your analyses/projects more shareable and--gasp--more reproducible (though I've gotten shit in the past for this, so let's compromise and say it helps but doesn't GUARANTEE reproducibility). This one is more optional than the others.

Once you have the basics down, you can do what interests you and learn best practices. Perhaps you want to know about Efficient R Programming (and general best practices). Or best practices in Python and more comprehensive coverage. Or how to make reports and papers with RMarkdown (want to make a paper that looks like it's published in AER? there's a template for that in Rmd).

You've got a lot of options, so I'm going to throw out a few I recommend, no particular order:

• Easy course on financial modeling that has a pretty good overview of basic excel functions. Great quick overview and an added bonus of learning some basic financial modeling concepts: https://www.udemy.com/financial-modeling-for-startups-small-businesses/learn/v4/t/lecture/6759908?start=0
  
• Book that I used in my summer before starting my post-undergrad consulting gig. Really good but a little deep/dense: https://www.amazon.com/Microsoft-Excel-Analysis-Business-Modeling/dp/1509304215/ref=sr_1_1?ie=UTF8&qid=1526764344&sr=8-1&keywords=excel+for+business+analysis
  
• Great website that starts from the basics, and you can skip forward to learn more:https://www.excel-easy.com/
  
• Surprisingly, some really great financial modeling videos put together by Martin Shkreli. As much of a dick that he is, he has some pretty good Excel skills and walks people through his methodology of building out models. He doesn't teach functions but you can look up what he does while you watch:https://www.youtube.com/watch?v=ARrNYyJEnFI
  
• My personal favorite for analysts that would reach out to me before starting asking for help. Great YT channel with lots of tips:https://www.youtube.com/channel/UCkndrGoNpUDV-uia6a9jwVg
  
  Disclaimer: I have not gone to B-School yet, but my experience with analysts that come from technical backgrounds is that Excel is a fairly easy tool to pickup. I wouldn't stress too much about learning before you get in.
  
  Hope it helps.

My journey started back in ~2012 and since then I've read every blog post from all SME's on the topic/related topics, so it's difficult to answer your last question but I'll try: becoming comfortable in this space, I would guess, is about a 60/40 split between: (1) physically using these tools to gain a competent understanding of how they work; and (2) following industry articles/studying to expand your knowledge and keep up with the fast changing pace of this technology.

Regarding your first question - if I were to master "Power BI" all over again, then I would do two things:

1. Master Power Query. There is no "ultimate" resource for Power Query unfortunately, i.e., there's no SME that says, "this is how it should be done!". The best way to master it is to use Power Query inside Excel as much as humanly possible (assuming you use Excel a lot - most people do). So start automating everything! I would also read articles from the greats like Imke Feldman & Chris Web. There are a few others that aren't as advanced, but they're good for beginners, e.g., Ken Puls . Why is this ranked before DAX? Well, Power Query's language (M) is much more versatile than DAX. You will see it propagate throughout the Microsoft landscape in the coming years. DAX will continue to remain within Power BI, Excel and SSAS.
   
2. Master DAX. There are two people who reign supreme in this area: Marco Russo and Alberto Ferrari. They both run SQLBI. They set the industry best practice, bar none. Buy their book, aka what I call the DAX Bible. All other SME's on the planet (excluding some lessor-known Marco-level SME's like Jeffrey Wang) refer to these two gurus.

I pretty much only read non-fiction, so I'm all about books that are educational but also interesting :) I'm not sure what your educational background is, so depending on how interested you are in particular subjects, I have many recommendations.

Naked Statistics and Nate Silver's Book are both good!

Feeling Good is THE book on Cognitive Behaviour Therapy.

The Omnivore's Dilemma is good, as is Eating Animals (granted, Eating Animals is aimed at a particular type of eating)

Guns, Germs and Steel is very good.

I also very much enjoyed The Immortal Live of Henrietta Lacks, as well as Surely You're Joking, Mr. Feynman :)

edit to add: Chris Hadfield's Book which I haven't received yet but it's going to be amazing.

For "books written for kids", I was a child prodigy and I liked the cartoon guides - I read the stats and the physics ones. I liked the how did we find out series as well, I think?


Textbooks might not be so bad. You find them dry, but he might not.


Emailing professors in the area was very helpful. If he understands what he's learned from the differential and integral calculus textbook, he's probably ready to talk to professors.


You don't necessarily need to teach him to follow in anyone's footsteps. If he's reading textbooks for fun, he's probably enjoys doing that. It's more important that he keep doing what he enjoys than that he imitates someone else who was successful. Still, it's nice to know about people who were similar to you in history! But it's not like he needs to go into physics or math to take advantage of his genius - some former child prodigies are already working in those fields, and while I like to think most are doing good work and advancing the state of their fields, none of them have revolutionized them recently.

Heya!

First things first, it's going to be ok! Lots of people enter sociology PhD programs with no background in sociology (or even a related social science!) at all. So, having majored in Sociology means you should have a much better lay of the land than some of your peers. That said, your classes will likely assume very little specific knowledge of sociology. For better or for worse, Sociology in undergrad is not treated as a "cumulative" subject where students are expected to master material in one class and then apply it in another. Grad school will expect that of you, to some extent, but it will not assume you start with much.

Second, if you are specifically worried about stats, I'd highly recommend reading some very light introductions that familiarize you with the concepts and ideas. Don't spend a lot of time with specific formulas, derivations, or software - your graduate stats sequence will cover all of that, again assuming you know basically nothing to start with. Instead, try to get a feel for statistical arguments, and for the basic ideas of probability, distributions, sampling, and so on. Start with something like The Cartoon Guide to Statistics. Then, read some quantitative sociology. Check out ASR or AJS or other big journals in the field. Find some articles on the topics that interest you and try to read through them to get a sense of how they employ quantitative methods. Don't expect to understand everything, but see what you can piece together.

Beyond that, I'd highly recommend checking out Fabio Rojas's guide Grad School Rulz (most of the content is available as a set of free blog posts on OrgTheory). I don't agree with absolutely everything Fabio says, but his advice is generally solid, and he covers all the important topics. Even if you don't take all his advice, reading the book will help you figure out what sorts of questions you should be asking and thinking about.

If you have any other specific concerns, let me know and I'm happy to give more targeted advice! Beyond that, good luck, and welcome to Sociology!

First, thank you for the taking the time to respond to my question - I really appreciate it. Second, congratulations on receiving the MCSA: BI Reporting badge!

The reason I am struggling to prepare for this exam is because there is no real comprehensive prep course like there is for the CPA exam. There's no "Becker for MCSA: BI Reporting". Can you share how you went about preparing for these exams? I feel like I have learned a lot from edX but not enough to pass the exam and I cannot find any additional practice questions/tests to study - anywhere. Do you have any insight on this?

Yes, the edX Excel course was very good and has significantly improved my Excel skills. However I am still unsure what to expect as far as test questions are concerned. Are they similar to the ones on the edX course?

As far as DAX and M, I assume DAX will be more heavily tested on the Power BI exam then the Excel one. Would you say that's correct? At the moment, my DAX skills are limited because I haven't been able to sit down and really run through it yet - but I will do so. I am waiting for my DAX book to come in the mail. M doesn't seem that difficult but nonetheless I have to run through it.

Again, thank you for your insight - I really appreciate it!

Definitely interested me with what you're saying. I'm unemployed but had some very basic analysis experience with Excel in my last role.

A few questions. Hope you don't mind answering. Didn't PM you in case others are interested in this.

• What do you consider to be the essential material to study to get a strong understanding of these technologies? I found this. Would you consider that to be the sort of material to study to gain competence in this area? (As well as studying the blogs of Chris, Marco, and Alberto?)
  
  
• What steps would you recommend a novice in data analysis do to quickly get a foot in the door? What positions / roles to look for specifically? What companies are big on hiring people competent in these technologies?
  
  
• Can you accrue a "portfolio" using these skills outside of the workplace to get your foot in the door at a higher level? Or would you need to start at the bottom, provide insane value then jump ship?

As far as "best" I can't say. It also depends on what in statistics you want to learn, but I agree with /u/solkim that probability and stats go hand in hand and if you want a good grounding in statistics you will also need a good grounding in probability. Having said all of this, and as silly as it may sound, the Cartoon Guide to Statistics is actually quite good at helping to understand and learn statistics (and probability) concepts.

If the teacher has a rubric that gives an operational definition of what an A means in their class then the grade is quantitative. You're right that the absence of a general operational definition of what a grade means is problematic when comparing across classes and schools, but that doesn't mean that in a single classroom the grades aren't quantitative. Anything can be quantified if an operational definition can be crafted for how it is being measured--even "love of learning." How to Measure Anything is a good primer on crafting operational definitions, especially for intangibles like "love of learning."

That said, good assessment and feedback required quantitative and qualitative description. One without the other is like peanut butter without jelly.

Agree completely the Purple Book is the Bible! And yes, wait for the 2nd edition at this point as it's very close. They also just released version 2 of their Mastering DAX video class on SQLBI as well which I'm looking forward to going back over.

DAX patterns is amazing as well, but maybe easier to access via web and I'm sure that will be seeing a new edition soon.

I cant' stress enough how useful the Analyzing Data/Orange Book was for me personally. It was more of a high-level overview of best practices for constructing data models which was crucial for me coming from unrelated backgrounds.

The equation you posted is the full Black-Scholes PDE and the equation you're talking about is presumably a solution to the above PDE. Asking what each of the terms is probably isn't the right question, if that's the kind of info you want you should take a step back and look up a full derivation. There's a pretty common text out there by Marek Capinski (The Black-Scholes Model) that walks through the derivation but unless you have a math background it will be beyond you -- but if you don't have a math background, looking at the full PDE also will be beyond you so.... yeah. You can probably find derivations online as well for free, but I don't have a link to one and can't recommend any.

Just as far as terms, S is probably the price of the stock, sigma is usually the volatility, r would be the risk free rate, t would be time, and V is probably the price of the option.

Right side and left side isn't the right way to look at an equation like this, you need to look at all the terms because you're looking for a solution that satisfies all of it, not two halves that you're trying to match up. If you look at different derivations, they'll likely have the terms organized differently. Probably lots of them isolating V or maybe everything set equal to zero.

ON IOP VS. OTHER CONSULTING ROLES

Have a look at ONETs job descriptions for Management Analysts and Market Research Analysts. Now compare it to the one for IO psychologists. You will get a clue of the large overlap and small differences in job descriptions.

Important differences include:

• projected job openings (800 for I/O psychologists of which nearly half are in academia, 151,400 for market research analysts and 208,500 for management analysts)
  
• typical qualification levels of incumbents (mostly bachelors for market analysts, mostly masters for management analysts and mostly PhDs for IOPs; and
  
• median salaries ($62K p/a for market research analysts, $77K for IOPs and $81K for management analysts)
  
  It is interesting to note that you can earn more with a masters as a management analyst than with a PhD as a IOP and not be far off what IOPs with PhDs earn as a marketing analyst with a bachelors.
  
  
  ON LANDING A JOB AS A CONSULTANT
  
  Now, you're an IO psychologists. I shouldn't need to tell you that to find work the most important things are to be smart, honest and well connected. For management analyst positions, masters degrees are plenty proof that you're smart enough. The big deal maker is going to be in the number of people who can hook you up with a job that trust you enough to hook you up.
  
  So number 1 tip would be to start making connections in hiring positions asap. Find out who you need to know and then ask the people you already know to introduce you to those people, or at least to people closer to them. Imagine highly connected nodes in graphs, 6 degrees of separation and all that cool discrete math stuff about social networks.
  
  
  In terms of technical skills you should develop, I highly recommend Wayne Winston's books on Business Analytics and Marketing Analytics. You'll see that you already know most of that stuff.
  
  If you look at the tools and tech section you'll notice that everyone except maybe Business Informatics graduates has a shit ton of IT stuff to learn after school to be effective in the work place. A couple of certificates in relevant ITs will likely give you a leg up in the competition for entry level jobs.
  
  I'm planning to add Microsoft Certified Solutions Expert: SQL 2016: Data Management and Analytics and SAS Certified Statistical Business Analyst to my CV before the end of the year.

One of the best not-very-technical books on data science in business is Thinking With Data. It's quirky but gets at the core of what good data science is supposed to be.

Beyond that, Data Science for Business has some great stuff in it, but you would probably want to skip the more technical parts, which might end up being most of the book, depending on your interest in that. Same for Think Like a Data Scientist (apologies for the self-promotion).

Medium.com has some solid articles about data science and various aspects of business, but they are scattered and I haven't yet seen a collection of articles that broadly cover what you're looking for.

A faculty member at my university put together a workshop and actually included stuff about implementing lavaan for Latent Growth Curve Models (LGCM). Notes for that workshop can be found here. I would also second using anything by Grimm. I took a class on LGCM and it was taught with this book by Grimm and it was extremely helpful.

I have become the go to person in my lab (psychology) for using lavaan and have helped my peers become more familiar with LGCM. If you have any specific questions, or would want someone to take a look at your code, I would be happy to! I really enjoy this type of stuff. Google and the lavaan Project will be super helpful! There are plenty of additional resources out there!

This will sound wimpy, but The Cartoon Guide To Statistics is pretty good, as in awesome for concepts. I'd recommend it as a supplement to whatever textbook you choose.

Hey now, I've got a masters in applied linguistics. Anyone can do it!. If you're super super super stuck check out this book by Andy Field. He is very good and literally goes step by step by step. Unless of course you're doing the actual math by hand. In which case you just need to go slow and check you're work twice over.

EDIT: Read: I believe in you.

I'm in a similar situation (requiring to be proficient in statistics), and here's what I'm doing.

1. Started with a really basic book that told me more about the ideas than the math. I used Andy Field's Statistics with SPSS book because it was recommended somewhere on reddit. The book definitely skims on the maths, but it gives you a good idea about the different tests and concepts.
   
   
2. Following Berkeley's Statistics Major/Minor route. Specifically:
   
   a. Stats 133 - Computing with Data: A course on using R, SQL, and other technologies useful in statistics.
   
   b. Stats 102 - Intro to Statistics I found multiple versions of this course, but I'm going to pick this one because it uses this interesting book which emphasizes case studies
   
   c. Stats 135 - Concepts of Statistics : More advanced treatment of the same concepts from 102.
   
   d. If you want to brush up on probability, you should look at Stats 101 and Stats 134.
   
   e. After this level, they have a series of electives, such as Stochastic Processes (Stats 150), Linear Modelling Lab (151A and 151B), Sampling Surveys Lab (152), Time Series Lab (153), Game Theory (155), and seminars.
   
   The classes don't have videos or audios, but they have syllabuses, lecture notes and assignments. So far I've found them to be more than sufficient.

Weird how I just finished the book Designing Data-Intensive Applications, and it ended with a section on ethics in computer science/big data that ties into this article really well. I'll add some of the sources from that section of the book here if people are curious. Cathy's book is in there, too.

• https://www.linkedin.com/pulse/making-hard-choices-quest-ethics-machine-learning-igor-perisic
  
• https://www.theguardian.com/commentisfree/2015/dec/06/algorithm-writers-should-have-code-of-conduct
  
• https://www.theatlantic.com/technology/archive/2014/02/welcome-to-algorithmic-prison/283985/
  
• https://www.theatlantic.com/magazine/archive/2013/12/theyre-watching-you-at-work/354681/
  
• https://www.theguardian.com/technology/2016/aug/03/algorithm-racist-human-employers-work
  
• https://www.scientificamerican.com/article/how-a-machine-learns-prejudice/
  
• http://idlewords.com/talks/sase_panel.htm
  
• https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815
  
• https://www.nytimes.com/2016/08/01/opinion/make-algorithms-accountable.html
  
• https://www.theguardian.com/technology/2016/nov/10/facebook-fake-news-election-conspiracy-theories
  
  There's also this fiasco: https://www.theguardian.com/technology/2018/jan/12/google-racism-ban-gorilla-black-people
  
  We're definitely having our machine babies learn from our own racist, classist, sexist data and giving people with malicious intent access to unprecedented amounts of data.
  
  Some forms of machine learning, like decision trees or random forests, have output that resembles a flow-chart which is nicer for humans because you can follow the decisions the algorithm is making. Deep learning with neural nets is real hard to understand. The model is basically just a ton of numbers.
  
  If you're curious about deep learning from the "how" side, Andrew Ng's deep learning courses on Coursera are really good: https://www.coursera.org/specializations/deep-learning
  
  Andrew Ng is kind of like the Fred Rogers of machine learning. He also has a machine learning course on Coursera that I've heard is great.

https://www.amazon.co.uk/Discovering-Statistics-Introducing-Statistical-Methods/dp/1847879071

This book by Andy Field is by far my favorite. His writing style is really laid back and funny, which helps me concentrate as statistics can be pretty dry/boring. And he is good at explaining the statistic theories in an easy way. If you dont want to use spss while learning he has a statistics book in which he doesnt use a statistics program as part of teaching (I haven’t read that one though). He also had books on how to use Stata, R etc.

I'm in the middle of reading Naked Statistics which is a pretty good and easy to understand intro. I've taken a few stat courses before and this book covers everything in really easy to understand terms.

I'm also a fan of The Drunkard's Walk which is mostly aimed at randomness, but because randomness is such a large part of statistics it really does cover many of the basic concepts.

Neither of these are textbooks, so they don't get too technical and instead neatly explain concepts. Enjoy!

With mathwashing and related discussions on algorithmic bias, you guys have scratched the surface of an amazing discussion on bias and the ethics of Big Data. Cathy O'Neil is an awesome writer to follow on this topic. Just last week she released a new book Weapons of Math Destruction that discusses how algorithms are used to oppress and marginalize people throughout their lives and the guise of 'objectivity'. Here is a link if you want a quick review or countless others.

I'd love to hear more from Mike on this topic and the injustices perpetuated by algorithms for the sake of efficiency.

There is a bunch of engineering stats books out there. The one we teach out of at my uni is the one by Devore. I think it does a good job of teaching what it does. I know Ross has an engineering stats book out there, and so does Montgomery, and they are both people who have written good books in the past. The one by Ross seems to have some good topics in it from reading the table of contents.


Also, you probably want to pick up a regression book. I like the one by Kutner et al., but it is ungodly pricey. This one has a free pdf. I don't like a lot about it, but the first few chapters of every regression book are pretty much the same.

If you want to go deep into statistical theory, there is Casella and Berger as well.


For programs, I know MATLAB has a stats package that should be sufficient for the time being. If you want to go further in stats, you might want to consider R because it will have vastly more stats functions.

Good to know! As far as a good book goes, depends on what sort of level you are looking for. This book looks like an interesting sort of introand seems to be well-reviewed , http://www.amazon.com/Naked-Statistics-Stripping-Dread-Data/dp/039334777X/ref=sr_1_2?ie=UTF8&qid=1453406226&sr=8-2&keywords=statistics , although I haven't actually read it.

Statistics is a really useful subject!

I dove into this stuff almost two years ago with very little preparation or background. Now I'm in an MS program for Applied Statistics, and doing quite well. Here are some tips that worked for me:

• If you don't have time to back up and regroup, check out Khan Academy, and this guy's YouTube videos. These can help with specific concepts.
  
  
• If you have time to back up and regroup, check out Coursera, Udacity, EdX, and the other MOOCs. Coursera in particular has some very good courses dealing with statistics.
  
  
• Take a look at Statistics for Dummies and Naked Statistics.
  
  
• Use Reddit and StackOverflow. But use them wisely, and only after you've exhausted other means.
  
  Good luck.

• Clean code (mentioned elsewhere)
  
• The Mythical Man Month
  
• Code: The Hidden Language of Computer Hardware and Software
  
  Docker or Kubernetes:
  
  
• Docker Deep Dive
  
• The Kubernetes Book
  
  ​
  
  Data Science, Machine Learning:
  
  Hands-On Machine Learning with Scikit-Learn and TensorFlow
  
  
• Now that you understand Python and the applicable libraries, you can actually use it for ML
  
  Python for Data Analysis
  
  
• Fantastic for learning Python and growing familiar with the libraries you’ll use in data analysis. It is from the creator of the Pandas framework.
  
  Python Data Science Handbook
  
  
• Great overviews of Juypter notebooks, NumPy, Pandas, Matplotlib, Scikit-learn
  
• Free lite version & code from the author here.
  
  Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
  
  
• So helpful for explaining the business use case for ML and data science to non-technical individuals
  
• Also helps boil down problems into data science terms, and realize if it really is an ML problem
  
  I put together a list of resources on my blog.

It's very clear for a book on mathematical statistics. It also considers the Bayesian (and even Empirical Bayesian) approach. I'm sometimes shocked at what it covers and how well it covers it in so few pages. For example, there's a nice section on the EM algorithm, which most books in the same class don't cover (unless they're huge).

Edit: I should mention... if you're a scientist looking for how statistics works this is the book for you. If you want to learn a ton about regression/ANOVA, time-series, covariance structures, blah, blah, blah, this book is not for you. A great introduction (for all scientists) that covers this stuff quickly and effectively (as well as MLE, optimization, and R) is Ecological Models and Data with R.

Edit 2: If you want applied linear models, Applied Linear Statistical Models is good, but doesn't use R. Luckily formula objects and delayed evaluation give R some beautiful expressivity here.

Thanks. The program is Data Science and prereqs are Calc, Lin Alg and basic stats.

I started my review using https://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X but the book assumes you have basic stats. I took these courses 5+ years ago so I only vaguely remember the material.

Good example with hetero/homoskedasticity. I want to make sure I understand things like random variables and different types of distributions.

If you want to be valuable to companies post graduation you should learn more about programming (design templates, how to write tests, how to go from a paper to code). I recommend this book as a good starting place. Once you're comfortable with how the different methods work, pick up this book.

You can try an alternative like FIXD. They work in Canada.

Also, Weapons of Math Destruction is a great read for anyone who loves or works with large sets of data.

By the way, do you know if things like linear/nonlinear regression, ANOVA and multivariate statistics is useful for me? Like stuff from https://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X/ref=dp_ob_title_bk or https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954/ref=pd_sim_14_8?_encoding=UTF8&pd_rd_i=1439840954&pd_rd_r=c9c0f3c5-f332-11e8-9f0a-0f336f5f387a&pd_rd_w=xHkKH&pd_rd_wg=7lXm5&pf_rd_i=desktop-dp-sims&pf_rd_m=ATVPDKIKX0DER&pf_rd_p=18bb0b78-4200-49b9-ac91-f141d61a1780&pf_rd_r=PFCZ1JM04FMAVAHG6VNP&pf_rd_s=desktop-dp-sims&pf_rd_t=40701&psc=1&refRID=PFCZ1JM04FMAVAHG6VNP

​

You can tell how little I know since I'm kinda shooting topics at the wall hoping something sticks

Similar to what /u/Cyzzie said, you are looking for some business intelligence tools. I would start at the beginning and read a book like http://www.amazon.com/Data-Science-Business-data-analytic-thinking/dp/1449361323/ref=sr_1_2?ie=UTF8&qid=1417617015&sr=8-2&keywords=big+data+business+intelligence to get an idea of the concepts behind business intelligence, and then look into tools like Pentaho or Hadoop.

https://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025

If your just looking for a concept overview the cartoon guide to statistics is great. It's easy to read and filled with great visuals and examples.

If you want to learn how to do intro statistics/practice, look no further than khan Academy.

Big Data: A Revolution that will Transform how We Live, Work, and Think by Mayer-Schonberger and Cukier (https://www.amazon.com/Big-Data-Revolution-Transform-Think/dp/0544227751)

Data Science for Business: What you Need to Know about Data Mining and Data Analytic Thinking by Provost and Fawcett (https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/ref=pd_bxgy_14_img_3?_encoding=UTF8&psc=1&refRID=XSYTKYEVG8W52XART2BD)

Data and Goliath by Schneier (https://www.amazon.com/Data-Goliath-Battles-Collect-Control/dp/039335217X)

Cathy O'Neill's book is ok. It is worth reading, I thought it could have been better.

Dataclysm is great.

Any tutorial by Andy Field is excellent, he was our statistics tutor at the University of Sussex and is one of the best lecturers I have come across. Someone has already linked you to his 'statistics hell' website below. Although you are looking for online tutorials id strongly recommend buying or borrowing his textbook 'Discovering Statistics using SPSS'. It is very easy to follow and starts with all the basic aspects of using SPSS before going into the complexities of the program.

link: http://www.amazon.co.uk/Discovering-Statistics-Introducing-Statistical-Methods/dp/1847879071/ref=sr_1_2?ie=UTF8&qid=1404320969&sr=8-2&keywords=discovering+statistics+using+spss

>A steep learning curve is one where you gain proficiency over a short number of trials. That means the curve is steep.

Congratulations, you've lost all credibility as an analyst by using a technical term to mean the exact opposite of what you were trying to say. Learn the technical jargon before you fuck up mean and median in front of your boss. A Cartoon Guide to Statistics does an amazing job of explaining most of the terms and formulas. If you don't want the book, watch Khan Academy's Statistics Course on YouTube.


Next, if you have Microsoft Excel, enable the Solver and Analysis ToolPak. This will allow you to perform all of the analyses performed in the book/videos. OpenOffice has a similar toolpak.

As long as you know how to perform an ANOVA or solve for a p variance, everything else should be industry specific models/applications and no one will realistically expect for you to know how that company's particular processes work.

I have a different suggestion - try learning Power BI instead. It is the "next step" from Excel and has a lot of deep analytic and data transformation / automation functionality via Power Query and DAX. Tableau is an excellent (and more mainstream) option as well. Also, you may as well start slowly working on Python.

The book that you want the person to look up is Applied Linear Statistical Models. It is a great reference book and gets into the nitty gritty calculations for figuring out the appropriate degrees of freedom in some pretty ugly experimental designs.

It sounds like you have the easy part, which is learning the technical skills. The hard part is knowing what questions to ask of the data, it's about identifying the right problems to solve, if that problem can be solved by data, then actually convincing your boss or client of the results.

As quantitative people, we're often too quick to assume that a problem can be solved with a data-driven approach. A good data scientist knows when the data is useful, when it isn't, and what questions to ask. I suggest reading a book called Data Science for Business, it will get you in the right mindset.

So suitonia's stats are now better than "the most vibrant summer in the past 5 years" stats? Numbers can tell any story you want. Bottom line is we, all of us, don't have all the numbers just what's available on various 3rd party tools and the info CCP provides allows us to see. Every conclusion based on partial numbers is not the truth - and let's not kid ourselves; numbers can say whatever we want them to say and follow any narrative. Make up your own mind, not based on anyone's swiss cheese statistics.

I'll leave this right here:

https://www.amazon.com/How-Lie-Statistics-Darrell-Huff-ebook/dp/B00351DSX2

There is a book which is kicking up a lot of fuss about this - "Weapons of Math Destruction" https://www.amazon.co.uk/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815. I haven't read the book, but I have heard various comments on it by its author and others. E.g. http://www.econtalk.org/archives/2016/10/cathy_oneil_on_1.html

Some of this has been argued about before - if you run the numbers and find that women have fewer car accidents than men, is it OK to charge them less for car insurance?

Some of this boils down to "what this company is doing offends my political principles. That can't be right!"

If you really decided that the government had the right to regulate company choices that involved algorithms and it did make sense, there is still no requirement for the government to vet particular algorithms. Instead you could have a government algorithm which monitored the company algorithm. One obvious way of doing this would be with some sort of quota system e.g. our insurance company will be free to set different insurance rates for different women drivers as it chose according to some trade secret formula, as long as the average female driver was charged the same as the average male driver. So in this particular case of equality, female drivers would subsidize male drivers. Come to think of it, I wonder if insurance companies would advertise more in women's magazines - there's got to be lots of clever ways to game this particular system.

As far as I know there is no link between this sort of concern and concerns about things like the safety of automated cars. Safety-critical software is a very specialized and extraordinarily expensive area, because it is enormously difficult to guarantee that software doesn't have dangerous bugs. I think the concern here is that the software is working properly, in that it takes decisions that are competitive in whatever the company's market is, but somebody has an objection to whatever that winning strategy turns out to be.

Data Science from Scratch

Python Machine Learning

DSFS covers basics of Python. If you're comfortable with that and want to dive into implementing algorithm (using Tensorflow2, for example), then PML is a great book for that.

Take a look at Data Science for Business. It covers a lot of other topics and are more theoretical, but I think it is pretty nice. Let me know what you think

https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323

I'm doing the same. Here are a couple of resources that you may find helpful.

• Book: Cartoon Guide to Statistics (better than it sounds)
  
• OCW: Statistical Thinking for Managerial Decisions
  
• OCW: Introduction to Probability and Statistics
  
  That should be enough to get you going. Good luck!

There is a great book by Wayne Winston which we pass around to all the new hires. It is excellent https://www.amazon.com/Microsoft-Excel-Analysis-Business-Modeling/dp/1509304215

I mainly use SAS nowadays, but Andy published one of the most popular and easy-to-use SPSS and introductory/intermediate statistics books.

I highly recommend it.

http://www.amazon.com/Discovering-Statistics-Introducing-Statistical-Method/dp/1847879071/ref=dp_ob_title_bk

Not exactly an answer to your question, but I found the book, The Cartoon Guide to Statistics (authored by one of my Temple Statistics Professors, Woollcott Smith), to be both helpful and entertaining.

https://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025

If you want to learn a lot about PowerPivot and (don't mind reading) I'd recommend anything written by Alberto Ferrari and Marco Russo. They write on PowerPivot / SSAS / Power BI for the Microsoft Press. One of their books was recommended by Michael Alexander who's a Microsoft MVP (I think for Access, but he also knows Excel very, very well.) Take a look at a few of the books below:

https://www.amazon.com/Definitive-Guide-DAX-intelligence-Microsoft/dp/073569835X/ref=asap_bc?ie=UTF8

https://www.amazon.com/Microsoft-Building-Models-PowerPivot-Business/dp/0735676348/ref=asap_bc?ie=UTF8

I only read a bit of their 2013 book, but it's very comprehensive and of high quality.

I also want to discuss a few other things mentioned here in the comments: PowerPivotPro by Rob Collie and SQLBi. Rob Collie is a former Microsoft engineer on Excel, is an expert on it, and still talks to many engineers on the Excel team. And SQL Bi is run by Marco Russo, who I mentioned above.

I'm reading Naked Statistics right now and I feel like a lot of the concepts that you deal with in stats are very well explained and put into context in the book.. Might be worth giving it a try

You're amazingly optimistic, I'll give you that.

I know I am not eloquent enough (or even picking the right arguments here) to convince you.

I encourage you to read Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil. She details a lot of the ways that the "data" Big Tech gathers to help governments ends up doing a lot of harm. She's way smarter than I am (and it's a really good book).

Found one on amazon for $66 here. Other than that, pirate/torrent/whatever. Google it. Good luck

Okay.. Someone had to do it.. right?

Statistics for business and economics ~$131.15 Used and ~$188.39 New.

Principles & Practices of Physics v1 hardcover ~$51.55 Used and ~$164.02 New.

Chemistry - The Molecular Nature... ~$124.00 Used and ~$239.87 New.

Principles & Practices of Physics v2 ~$129.74 Used and ~$126.78 New.

Differential Equations and Linear Algebra ~$79.89 Used and ~$151.29 New. I am the least sure about this book in particular. But for a wag, I'm sure the numbers will work.

Calculus - Early Transcendentals ~$86.03 Used and ~$236.81 New.

So by my calculations your current "TV Stand" cost ~$1107.16. I'd recommend you go to amazon and sell the books you probably aren't ever going to crack the cover on again for ~$602.36 and buy yourself an actual TV stand with a little money left in your pocket.

I do all this because most of my friends in college complained about the costs of text books and then never sold them again. Or did the absolutely stupidest thing you could ever do with a book you've paid over $200 for and sold them back to the bookstore for ~$20 a pop. Don't be lazy, use amazon to sell your books back and the sting of your new found education won't be so bad. The idea is to get smarter right?

This book is pretty good:

https://www.amazon.com.au/Data-Science-Scratch-Joel-Grus/dp/1492041130/ref=sr_1_2?keywords=joel+grus&qid=1557969275&s=gateway&sr=8-2

Andrew Ng's Machine Learning Online Course

Deep Learning with Python Book by Francois Chollet

​

Bonus Gift:

Manga Guide to Regression Analysis

This whole mess-up is a textbook "weapon of math destruction"... for anyone looking to learn more about how big data is making the wealth gap wider, this book is a great read.

This isnt a job posting. I am posting this for a discussion raised from a website I have no connection with.

Firstly these are interesting ideas and seem ideal for blockchain based business models.

Secondly I think the Question at the end about whether these suit men or women is a good one

Thirdly on a weapons of Math destruction level what does it mean to do jobs effecting peoples lives that involve only maths and not meeting the people?

I posted this to start a discussion about the particular ideas and the concept of interaction free jobs and I'd like to hear your opinion

Andy Field is both very informative and entertaining. I highly recommend this book.

It sounds like your questions are mostly statistics based. This book is a good intro - not too intimidating.
http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025

Are there any good resources for learning more about this?

I have a tech sales background and have an interest in analytics. I picked up this book as a springboard- https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/ref=nodl_

Practice, practice, practice. The more DAX you wrote the more you will know...and the more you will know you don't know ;).

By the way, the book you need to read is The Definitive Guide to DAX, if you learn from this book you will know "everything". Another good source is sqlbi.com, you can find useful articles about the language.

>The majority of adults today, even highly educated, do not know basic math.

Yes.

>they rely heavily on technology to do easy calculations

I don't reliance on technology is the issue.


>they do not understand basic statistics.

Yes, but most adults have never seen a course in basic statistics/probability so this is to be expected.

> Do you think this is an issue?

Absolutely.

> Do you think this affects the society as a whole?

Without a doubt. For a little slice of this, check out Weapons of Math Destruction or for an explanation of how Republicans are able to maintain their grip on Congress see Gerrymandering

If you need to make an argument for the application of a data science tool, I recommend to read Data Science for Business. The book does not focus on R (or any other tool/language), but makes a compelling case for the value of data science, that aims at establishing an understanding for people not concerned with the technicalities of data science.

that's a good last point. I would like that.

yea it was a text-book for example.

But other books such as: https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323 is $18 on kindle.

IIRC, they already tried this in New Orleans, and before that, with the military (!).

Speaking as someone who has heard a bit about this sort of thing IRL, I'll make a couple of points about this:

(1) It's about the datasets as much as anything else. Ghouls like Thiel will roll out these half-baked products and 'work with' local authorities, and in the process get access to loads of sensitive data to train their technology on. For people working in AI, these sorts of mass public datasets are extremely valuable. So, this is not only about racism, but about privacy and the enclosure of the intellectual commons.

(2) If we don't get proactive, the end result of this sort of thing (also being trialled in e.g. university admissions, and potentially in all sorts of stuff) will be a crappy version of Gattaca. As with eugenics, the technology doesn't actually have to be premised on truth to work as a technology of control.

(3) I really should get around to reading this book.

Surprisingly effective intro to probability

might be too informal for your purposes though...

Read this book Its a little deep, but I believe its a good start if you want to get into data science. Also, check out the Tableau and Alteryx tutorial videos.

Don't want to be the devil's advocate here, but I think everyone interested to get into this field must read the book Weapons of Math Destruction by Cathy O'Neil
Of what 'good' DS can do, that has been well promoted everywhere.. Of what 'disaster' it can bring, few would want to shine a spotlight on... Pursue this field, knowing both its light and dark side...

I've never read it myself, but I've heard quite good things about this book: https://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025

At my old job, we used to keep it on our team's bookshelf, along with a bunch of very dry graduate texts in statistics.

For Economics, I think Freakonomics does a bit of this. As a statistics person, I love Naked Statistics.

> make his claims but not provide souce code showing how a bias could be hidden in an algorithm without it being immediately obvious to many coders at google

Because with machine learning and AI, even the developers don't understand how the decisions are made.

You should read Weapons of Math Destruction by Cathy O'Neil, which goes into how biased training data, programmers, etc can result in biased algorithms. It's pretty fascinating.

Honestly, ignore the "for engineering" part of "Statistics for Engineering." They're largely the same content.

How much calculus have you taken? Does the class use calculus?

First, the cartoon guide to statistics is surprisingly helpful for some people.

For a more traditional textbook, you might try Devore's main intro book.

Almost every student finds statistics confusing and it's either difficult to teach, or just difficult to learn. It's also a fractal discipline, since you can keep going deeper and deeper, but it's generally just going over the same few concepts with additional depth. If you end up in a class that's not well suited to your mathematical background it's especially frustrating.

Good luck.

There's a great book called Weapons of Math Destruction. If you're interested in these kinds of problems, this is a quick resource to get up to speed.

Awesome book
http://www.amazon.com/Data-Science-Business-data-analytic-thinking/dp/1449361323/ref=sr_1_1?ie=UTF8&qid=1416173703&sr=8-1&keywords=big+data+for+business

Applied Linear Statistical Models by Kutner is a far better reference for statistical modeling compared to ISLR/ESLR or any kind of "machine learning" text, but it sounds as though you did a stat masters since you're asking about stat modeling instead of the new buzzwords. The latter options are certainly more narrow.

https://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X
Considered a cornerstone, of sorts.

Data Science = Technical Skills + Stats Skills + Business Expertise. So, for technical skills, start with Python, SQL, and Tableau. For Stats Skills, pick up 2nd, 3rd, and 4th year stats book. For business experience, work on business projects where use Python and Stats skills to solve them.

EDIT:

• For business acumen:
  Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Storytelling with Data: A Data Visualization Guide for Business Professionals
  
  
• For technical skills:
  Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython , Python Data Science Handbook: Essential Tools for Working with Data
  
  
• In regards to stats, I would suggest speaking with the Stats prof. Also, you might as well take a look at courses at Udacity and Udemy.
  
  So, you gotta sharpen up your programming skills.
  
  Godspeed

Hi, There are so many resources out there I don't know where to start! I would work through some kind of beginner python book (recommendation below). Then maybe try Andrew Ng's Machine Learning Coursera course to get a taste of Machine Learning. Once you have completed both of those I would reassess what you would like to focus on. I will include some other books I would recommend below.

Beginner Python - https://www.amazon.co.uk/Python-Crash-Course-Hands-Project-Based/dp/1593276036/ref=sr_1_3?keywords=python+books&qid=1565035502&s=books&sr=1-3

Machine Learning Coursera - https://www.coursera.org/learn/machine-learning

Python Machine Learning - https://www.amazon.co.uk/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_7?crid=2QF98N9Q9GCJ9&keywords=hands+on+data+science&qid=1565035593&s=books&sprefix=hands+on+data+sc%2Cstripbooks%2C183&sr=1-7

https://www.amazon.co.uk/Data-Science-Scratch-Joel-Grus/dp/1492041130/ref=sr_1_1?crid=PJEJNNUBNQ8N&keywords=data+science+from+scratch&qid=1565035617&s=books&sprefix=data+science+from+s%2Cstripbooks%2C140&sr=1-1

Statistics (intro) - https://www.amazon.co.uk/Naked-Statistics-Stripping-Dread-Data/dp/039334777X/ref=sr_1_1?keywords=naked+statistics&qid=1565035650&s=books&sr=1-1

More stats (I haven't read this but gets recommended) - https://www.amazon.co.uk/Think-Stats-Allen-B-Downey/dp/1491907339/ref=sr_1_1?keywords=think+stats&qid=1565035674&s=books&sr=1-1

Came here to say this, thank you. I recently read How to Lie with Statistics and this would be a prime example of laziness on the researchers part.

Edit: Bitwise is correct, for all we know the researchers may not be the lazy ones, we wont know unless we pay for the complete article, but the journalist who titled the article definitely is (no surprise)

Also check out "How to Lie with Statistics". It takes on the more nefarious side of this topic. I really enjoyed it.

I've never heard of that book before, but I took a look at their samples and they all seem legitimate.

I would just buy the Ebook for $59 and work through some problems. I'd also maybe purchase some books (or find free PDFs online). Given that you don't have a deep understanding of ML techniques I would suggest these books:

1. Intro to Statistical Learning
   
2. Data Science for Business
   
   There are others as well, but those are two introductory-level textbooks I am familiar with and often suggested by others.

Well that's what I'm asking for, are there any websites besides the publisher that sell them? I know amazon does, but take for instance the Mymathlab thing.
Nearly half of the people who bought it say that the code was invalid. I'm fine with just getting replacements through amazon's customer service, but it would take far too long and I wouldn't be able to start in my math class.

It requires study so you might not have any sudden moments of clarity, but this is pretty much the Bible of regression.

http://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X


Highly recommended.

>Em tese, de uma forma lógica e isenta. Analisaria de forma fria e calculista todos os aspectos positivos e negativos de cada candidato

Não confie tanto nos algoritmos, eles são feitos por humanos. Reproduzem o mesmo sistema de valores mas de forma 100% automática e sistemática. Eles parte de uma base de treinamento não ideal.

"Algoritmos são armas de destruição matemática"

https://youtu.be/_2u_eHHzRto

​

Além do mais política está em outro plano, não é uma questão técnica de otimização. Por exemplo, o desmonte da educação é um projeto de poder e não um incidente de má gestão. Se o objetivo da classe dominante é explorar e extrair riqueza, o problema é o próprio sistema e não a arquitetura dele.

​

Queremos uma polícia que não seja corrupta?

https://youtu.be/2NYtJ9LrXhk

ex-chefe de Polícia Civil do Rio de Janeiro Hélio Luz

“Competing on Analytics” is a classic.

Competing on Analytics: Updated, with a New Introduction: The New Science of Winning https://www.amazon.com/dp/1633693724/ref=cm_sw_r_cp_api_iDdSBbNABWMN6

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking https://www.amazon.com/dp/1449361323/ref=cm_sw_r_cp_api_8EdSBbKH69PFN

So I hope everyone has seen the manga version of C21: https://twitter.com/EDerenoncourt/status/1170343228819853313

But there's also manga linear algebra: https://nostarch.com/linearalgebra

and manga Bayesian Statistics: https://twitter.com/tkasasagi/status/1154975832361717765

EDIT: it never stops https://www.amazon.com/Manga-Guide-Regression-Analysis/dp/1593277288

This doesn't present like a well-researched or well-executed engagement campaign, though. I've run a multi-touch product team and I've worked very closely with places like Re-Sci and this is not that.

This is someone who was promoted to their level of incompetence playing business by trying to do what everyone else does. They run the experiment, get the stats they need to make their point, and then use it to justify some new poorly thought-out initiative.

I've had Reddit as a client. Reddit is a clusterfuck. We took our Reddit contact to LIV to entertain them while at a conference in Miami a couple years back and she spent the entire night trying to convince us - and anyone else who would listen - to find her cocaine. It was embarrassing. Complete waste of a table and a night. I've never worked with them since.

Stats book: $188.39

Physics Books: 4x $69.99?

DEs and Linear Algebra: $151.29

Single Variable Calc: $138.76

Getting a college education: Priceless (Just kidding, you're in the US.)

(Lowest Amazon prices, BTW, not even from the bookstore)

This is a good resource: https://www.amazon.com/Microsoft-Excel-Analysis-Business-Modeling/dp/1509304215/ref=sr_1_1?ie=UTF8&qid=1536926123&sr=8-1&keywords=excel+analysis

Totally depends on your stat background. If it's minimal id do khan Academy. This is a good into book http://www.amazon.com/gp/aw/d/0062731025/ref=redir_mdp_mobile/192-3922040-7644940

What's your background?

Try this one. It's as good as any

http://amzn.com/1449361323

I liked this one Weapons of Math Destruction.

I agree with all of the above. Also, here's the Linear Models tome we used: http://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X

Im currently a DBA transitioning into a (somewhat) BA role. Its difficult to say what patterns to look for or books to read in regards to that position in the field of healthcare field. But bridging the gap between data and your new role this could be a good start. OH and be prepared to document more than ever have before.

My favorite is not in my field, but related: Discovering Statistics Using SPSS

>If you Google KNNL it'll know what you're looking for).

Mine certainly didn't, I got two pages of Karnataka Neeravari Nigam Limited and associated projects.

If anyone else is wondering, I'm assuming this is the book, I eventually found it on a CSU syllabus: https://www.amazon.ca/Applied-Linear-Statistical-Models-Student/dp/007310874X

Not to be confused with: https://www.openhub.net/p/knnl

Read this book, Data Science for Business. It sounds like you don't need to code, but need to be able to converse.

Well I'd recommend:

• The Elements of Statistical Learning - this is kind of the go-to standard book recommendation
  
• GitHub: Python Data Science Handbook
  
• GitHub: iPython notebooks for data science - really useful to explore concepts
  
• Numerical Linear Algebra - if you need a linear algebra refresh
  
• Machine Learning: A Probabilistic Perspective
  
• Data Science for Business - provides a high level overview of techniques
  
  For a more basic stats refresh before you dive in, pretty much any introductory textbook will be sufficient. For a very basic but quick and dirty refresh on basic stats you can get: Statistics in Plain English

It's this book: Field, Andy (2009). Discovering Statistics Using SPSS, 3rd edition. London: Sage.

Cartoon Guide to Statistics

Check out Data Science for Business by Foster Provost & Tom Fawcett

https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323

vllt zu grundlegend für dich, das hier fand ich ganz gut:

https://www.amazon.de/Cartoon-Guide-Statistics/dp/0062731025

https://www.amazon.com/Probability-Statistics-Engineering-Sciences-Devore/dp/0538733527

5 dollars for each textbook found

The first is ISBN-13: 978-0321826237 https://www.amazon.com/Statistics-Business-Economics-James-McClave/dp/032182623X

The second is ISBN-13: 978-1305511064 https://www.amazon.com/Foundations-Business-Standalone-MindTap-Course/dp/1305511069/

From another discussion (http://www.reddit.com/r/statistics/comments/2mqyn5/where_to_learn_statistics/):

http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025/ref=sr_1_1?s=books&ie=UTF8&qid=1416393382&sr=1-1&keywords=cartoon+guide+to+statistics

https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323

Weapons of Math Destruction

I can't, but this is a great resource: https://www.amazon.com/Naked-Statistics-Stripping-Dread-Data/dp/039334777X/ref=sr_1_2?ie=UTF8&qid=1497463745&sr=8-2&keywords=statistics

Naked Statistics

Weapons of Math Destruction and Reality Mining

https://www.amazon.com/Manga-Guide-Regression-Analysis/dp/1593277288

It currently costs $97.70, so yep, the book is cheaper.

http://www.amazon.com/Discovering-Statistics-Introducing-Statistical-Method/dp/1847879071/ref=sr_1_1?s=books&ie=UTF8&qid=1342130113&sr=1-1

https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815

How to lie with statistics.

http://www.amazon.com/gp/product/032119991X/ref=oh_aui_detailpage_o00_s00?ie=UTF8&psc=1

For a novice who is looking primarily to understand statistics rather than perform statistical analysis, I would consider The Cartoon Guide to Statistics. It is intended for total beginners and the medium makes the content a lot less dry than most statistics books.

Good list of books.

I've also heard good things about Weapons of Math Destruction written by one of the authors of Doing Data Science. Haven't read it myself though.

https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815

You will find it hard to beat "Data Science for Business: What you need to know about data mining and data-analytic thinking". This book is used as a MBA course book at NYU. The book is not full of algorithms, nor does it overload you with complex math. It teaches you how to break common business problems down into fundamental ideas and provides a framework to help you learn the techniques to solve these problems. It's just the right balance of theory and practical knowledge. You will learn about many of the modeling techniques used today with just the right amount of detail. I can't say enough about this book and I'm not the only one. http://www.amazon.com/Data-Science-Business-data-analytic-thinking/dp/1449361323

> Stats don't lie

Feel free to order these books online:

http://www.amazon.ca/How-Lie-Statistics-Darrell-Huff-ebook/dp/B00351DSX2/ref=sr_1_1?ie=UTF8&qid=1422311052&sr=8-1&keywords=statistics+lie

http://www.amazon.ca/Damned-Lies-Statistics-Untangling-Politicians/dp/0520274709/ref=sr_1_2?ie=UTF8&qid=1422311052&sr=8-2&keywords=statistics+lie

>“There is a tendency to want to see AI as a neutral moral authority,” Riedl told BuzzFeed News. “However, we also know that human biases can creep into data sets and algorithms. Algorithms can be wrong, and there needs to be recourse.” Human biases can get coded into the AI, and uniformly applied across users of different backgrounds, in different countries with different cultures, and across wildly different contexts.

This is the Garbage-In-Garbage-Out problem. For more on this check out this book:

https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815

TL;DR of this article

• It's going to take a long time for this to be of any use at all.
  
  
• AI can totally be biased anyway.
  
  
• Even worse, AI APPEARS to be neutral but is in fact biased by its human creators.
  
  
• AI got us into this mess in the first place.
  
  
  I'd like to add an additional issue: The same powerful AI tools that Facebook may one day in the future have access to in order to clear up fake news on the platform will ALSO be used by the powerful nation-state actors that are trying to make fake news and bot posts go viral on Facebook.

Ugh, this should not be a pie chart. uh...here.

You might need this book

95% confidence with a margin of error of ±5% for a population of 25,000,000 you would need about 385 people. The real problem here is how biased is that sample. By that I mean, do they have a statistically representative collection of people?

Some are downright riveting...

Many are just pamphlets, cheat sheets, or quick introductions - most don't go beyond introductions or very specific features they're trying to push. Some are tied to new books being published, like this and this.

So, your mileage may vary - they're not really technical books in the typical sense though. I'm also not sure why they say 'millions' when it's very obviously 404 downloads (based on the link /u/tommis posted). It seems misleading at the very least.

I knew what book this was the moment I read this headline. (To those asking: http://www.amazon.co.uk/Discovering-Statistics-Introducing-Statistical-Methods/dp/1847879071/ref=pd_sim_b_2) This is the greatest stats book ever: I failed Stats in my second year, went to my professor, who wouldn't help me out. I picked up this book in desperation, read it, instantly went "WOW." I will permanently have Fugazi stuck in my head now though. Also: I aced my stats text. The moment a lower year friend of mine complained about the stats lecturer, I gave him my copy. Never went to a class again, passed his test.

Honestly, the more acute danger is shitty pattern matching. A lot of machine learning models applied to targeted policing leads to more people of color getting locked up, for example. Live in a zip code with a lot of delinquent borrowers? You have to pay a higher mortgage rate. Weapons of Math Destruction explains this really well

I am a BIG believer in the need to make data-backed decisions as business owners and entrepreneurs. With this in mind I suggest the following:

A Field Guide to Lies and Statistics
By Daniel Levitin

Statistics Done Wrong: The Woefully Complete Guide
By Alex Reinhart

[How to Lie with Statistics](https://www.amazon.com/Statistics-Penguin-Business-Darrell-Paperback/dp/B010IKB3WU/ref=sr_1_4?s=books&ie=UTF8&qid=1521735434&sr=1-4&keywords=how+to+lie+with+statistics+by+darrell+huff - Darrell Huff)
By Darrell Huff

How Not to be Wrong: The Hidden Maths of Everyday Life
By Jordan Ellenberg

Naked Statistics
By Charles Wheelen

The Truthful Art: Data, Charts, and Maps for Communication
By Alberto Cairo

Bad Science
By Ben Goldacre

Recomendo ler: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy por Cathy O'Neil

I disagree - we need transparent and honest systems. If we're going to use racial / gender discrimination, we shouldn't be able to hide behind 'big data'.

> Mr Murray denied it was racial profiling and said immigrants' gender, age and the type of visa they whītiki would all be fed into the data sets.

If you don't think this guy is either a liar or an idiot, I recommend reading Weapons of Math Destruction (or reading about it).

I highly recommend Weapons of Math Destruction to understand the impact of data science applied in the wrong way:
https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815

Ain't No Makin' It: Aspirations and Attainment in a Low-Income Neighborhood

• Meritocracy in the U.S. (probably global world) is a lie and being poor is complex and intergenerational
  
  
  Naked Statistics: Stripping the Dread from the Data
  
  
• Statistics is cool
  
  Fablehaven
  
  
• Made an entire D&D campaign based on Fablehaven's premise of a magical preservation sanctuary
  
  The Origins of Totalitarianism
  
  
• How totalitarianism is built and maintained
  
  Freakonomics: A Rogue Economist Explores the Hidden Side of Everything
  
  
• Humans are weird

This was an interesting and thought provoking read. Not too long either.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy https://www.amazon.co.uk/dp/0553418815/ref=cm_sw_r_cp_apa_pp5UBbY4DGFYA

The information feeding these rankings is unscientific and lacking in grounding in statistical analysis. There is no measurement of learning, nor of much other actual student experience. Instead there is lots of measurement of easy-to-collect and far less important information such as percentage of alumni who contribute money or the opinions of college administrators collected in surveys.

In fact, when the US News study began, it was a profile in weak study design, producing worthless yet popularly quoted results: all it did was survey college presidents. That this obviously empty practice is what gave us the US News college rankings in the first place should make everyone slow their roll about this "authoritative" study.

Source: Weapons Of Math Destruction, Cathy O'Neill, Crown.

I wouldn't describe any of those situations as unethical per se. Bad business decisions yes, but it's not inherently unethical for a company to make a bad product due to crappy or poorly run research.

That being said, if you worked for a government agency with a duty of care, then perhaps.
Or if you were conducting research to be the basis of an algorithm which would potentially have a social impact - like for example approving morgage loans - and were pressured to do an incomplete job which might impact, for example, a particular minority group. But by and large doing bad research is just bad business.

You might be interested in a book called Weapons of Math Destruction which investigates how algorithms and other models used by businesses and governments can have social impact, although I think it's less a matter of user research, and more generally about the topic of poor or limited research more generally