Best business operations research books according to redditors
We found 65 Reddit comments discussing the best business operations research books. We ranked the 21 resulting products by number of redditors who mentioned them. Here are the top 20.
We found 65 Reddit comments discussing the best business operations research books. We ranked the 21 resulting products by number of redditors who mentioned them. Here are the top 20.
Code: The Hidden Language of Computer Hardware and Software
The Mythical Man-Month
Peopleware: Productive Projects and Teams
Gödel, Escher, Bach: An Eternal Golden Braid
The Pragmatic Programmer: From Journeyman to Master
Coders at Work: Reflections on the Craft of Programming
Linear programming:
Combinatorial optimization:
Game theory:
Combinatorial game theory:
Maybe not exactly typical Data Science but as an introduction, a background I recommend "Data Smart: Using Data Science to Transform Information into Insight"
https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X/ref=asap_bc?ie=UTF8
I would start with Cover & Thomas' book, read concurrently with a serious probability book such as Resnick's or Feller's.
I would also take a look at Mackay's book later as it ties notions from Information theory and Inference together.
At this point, you have a grad-student level understanding of the field. I'm not sure what to do to go beyond this level.
For crypto, you should definitely take a look at Goldreich's books:
Foundations Vol 1
Foundations Vol 2
Modern Crypto
I've heard good things about Data Smart, though I have yet to read it myself. It's an introductory text from the big data guy at MailChimp.
I love Coders at Work. Not at autobiography though, but a set of interviews. Very entertaining.
There is also an older book with interviews: Programmers at Work.
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.
Data Smart and Data Science for Business
I'll just leave these two quality references here for those of you that care about these branches of mathematics:
https://www.amazon.com/Convex-Optimization-Stephen-Boyd/dp/0521833787/ref=pd_lpo_sbs_14_img_1?_encoding=UTF8&psc=1&refRID=TVGGPQ59DZ58S2XSXMGD
https://www.amazon.com/Combinatorial-Optimization-Algorithms-Complexity-Computer/dp/0486402584
And for those of you who like to throw in a little probability theory to the mix for more real-world situations...
https://www.amazon.com/Introduction-Stochastic-Programming-Operations-Engineering/dp/1461402360
Billingsley,
Resnick, and
Chung are probably the 3 standards, each with their own advantage. Resnick's is the easiest to read from, Chung's has the most rigor, Billingsley is a balance of the two. You'll probably want Resnick (it seems to be the favorite of young people), but Billingsley is the only one I've used for a class. It's OK. If it weren't for lectures it would have been unreadable for me.
Edit: these are all grad level probability books. You'll need your analysis knowledge to make any use of them.
I've posted this before but I'll repost it here:
Now in terms of the question that you ask in the title - this is what I recommend:
Job Interview Prep
Junior Software Engineer Reading List
Read This First
Fundementals
Understanding Professional Software Environments
Mentality
History
Mid Level Software Engineer Reading List
Read This First
Fundementals
Software Design
Software Engineering Skill Sets
Databases
User Experience
Mentality
History
Specialist Skills
In spite of the fact that many of these won't apply to your specific job I still recommend reading them for the insight, they'll give you into programming language and technology design.
I'm not sure what you mean by motivation, except to say that planar graphs are rather interesting and Kuratowski's theorem (and forbidden characterisations in general) gives a nice way to check whether a graph is planar or not.
In regards to where to learn more about graph theory, there are many excellent books on the subject, for something free, I hear Deistel's graph theory book which can be found his webpage is rather good
If you're interested in buying a book, here's the one I learnt from, it's jam packed of exercises
http://www.amazon.co.uk/Graph-Theory-Graduate-Texts-Mathematics/dp/1849966907
https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X
In my case I am a software engineer by trade so I don't use excel as much
I thought Coders at Work: Reflections on the Craft of Programming was a really enjoyable read.
It's just a collection of interviews. The book features some really interesting programmers such Ken Thompson, Joe Armstrong, Peter Norvig, and Donald Knuth. I had a great time reading their stories.
Check out Data Smart. http://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X
It shows you how to perform linear regression in Excel as well as loads more Data Science techniques such as time series forecasting, clustering, prediction etc.
Assumes no background in Maths/stats and all you need is excel
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.
As other mentionned, your question is pretty hard to answer on a post, even if it is pretty interesting. But having done a bit of research in discrete math & algorithm design and being a math lover myself, I'll try to talk about why I find my area interesting.
Being a software developper, you should pretty much know what an algorithm is. Given a problem, an algorithm is a recipe/step-by-step set of instructions that, if followed exactly, solves your problem. Algorithms can be classified in a lot of categories: deterministic algorithms (given some input, you always get the same right answer), probabilistic algorithms (you get the right answer 'on average'), approximation algorithms (you get an answer that is within some (provable) factor of the optimal answer) and so on. The main measure (there are multiple others) for the performance of an algorithm is the time it takes to find an answer with respect to the size of the input you are giving it. For example, if your problem is to search through an unordered list of n items for a given item, then the trivial solution (look at every item) takes time n. This is what we call linear in n, but algorithms can run logarithmic in n (takes time log(n) ) (very fast), exponential in n (c^n for some c) (very slow) or polynomial in n (n^k for some k) (efficient).
So the fun part is to find THE best algorithm for your problem. For a particular problem, one may ask how fast can the best algorithm run. Well surprisingly, it turns out that some problems will never admit an efficient EXACT solution. I'll give an example of such problem, and then come to the main point of my discussion.
Consider a graph/network (we love these things in discrete math), which is basically a set of points/nodes/vertices that are connected by links/edges, and its size is usually the number of nodes (the maximum number of edges of a (simple) graph is n^2 - the maximum number of pairs you can make with n elements). The Internet is the best example : nodes are webpages and edges are hyperlinks between theses pages. We say that two nodes are neighbors if they are connected by an edge. A fundamental problem of discrete mathematics goes as follows: what is the minimum number k such that you can color the nodes of a graph with k colors such that not two neighboring nodes share the same color? (http://en.wikipedia.org/wiki/Graph_coloring). It turns out that this problem can be proven to be inherently hard - if we can find an efficient deterministic algorithm (we strongly believe we can't) to solve this problem, than there is an efficient (=fast) algorithm to solve many "hard" problems (ex.: proving a theorem, or solving a sudoku ! - probably not going to happen). Such hard problems are said to be NP-complete. It also turn out that most real life (interesting) problems are also that kind of hard (http://en.wikipedia.org/wiki/List_of_NP-complete_problems).
This sounds quite desperate. However, here is where the research starts. It is said that these NP-complete problems cannot have efficient DETERMINISTIC and EXACT algorithms. Nothing prevents us from producing randomized and approximate solutions. So with some clever analysis, input from other areas (read algebra, geometry, probability, ...) and other tricks, some algorithms are built to find solutions that are usually within a good factor of the optimal. Hell, some NP-complete problems (i.e. Knapsack Problem) even admit an arbitrarly precise efficient algorithm. How is this possible? Reading required!
I don't know of non-scholar books that covered this subject, but if you are motivated, here are the books I suggest:
I guess there wasn't at lot of fireworks, but the goal was really to show an area of math that is not often brought up in 'mainstream math discussions'. So feel free to ask questions, I'll try to answer them as far as my knowledge goes.
P.S. I guess I've have put down the foundations for the discrete math/ algorithm design fortress, so every knowledgeable person should add his/her own comment!
yeah that's a bit more advanced than just reading up on some functions like /u/aristite said.
I am guessing that's not the kind of stuff they'll want you to do in the interview session, 60 minute is a short amount of time once you start working with bigger/more advanced datasets.
I would go with the stuff already mentioned in this thread, + array formulas, and for the more advanced statistical/analytical methods (monte carlo simulations etc.) and how to do them in excel, have a look at this book:
http://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X
It's something that becomes a fad every couple years for about a week, and then dies out again. I think I first heard about it in the interview with Brad in Coders at Work and I'd been meaning to try it.
To be honest, I'm not sure if I'd start any new project this way now that I've tried it, but I'd recommend anyone who considers themselves a programmer try to do something with it, for the same reason I'd recommend trying out any other programming paradigm that they're not familiar with..
The first one isn't too off: Amazon link to a book
Granted, it's not distributed, but I read that book given its high rating and the author really jumps through hoops trying to figure out how you'd do k-means in a spreadsheet without macros or anything.
I can tell you that I used this one in my graph theory class and was pretty happy with it. It has a nice clean layout and it's one that I actually kept.
In my actual discrete math class my crazy stuttering bearded professor just passed out hand written xeroxed notes so...no textbook. This one is highly rated, but if you have some background in logic then you're already halfway there. I remember a lot of truth tables and logic statements.
This one.
I forget how deeply Data Smart delves into correlation, but you may want to give it a shot. Also, here's a summary on the book. It's very to the point and written in very clear English.
The bookshelf next to my desk has Zimmermann, Fuzzy Set Theory and Its Applications. I can't vouch for how good it is because I haven't personally read it, but someone around here has. The bookshelf also has Slotine and Lewis! I'll have to check those out sometime.
Data Smart
Whole book uses excel; introduces R near the end; very little math.
But learn the theory (I like ISLR), you'll be better for it and will screw up much less.
If you want super beginner, Data Smart by John Foreman is probably the best. It isn't free and it is very basic.
http://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X
Like you I work at a tech startup. When we were just starting, our business/strategy people asked the question you just asked. They opened a dialog with development team, and found good answers. I attribute our success in large part to that dialog being eager and open-minded, just as you are being right now. So, it's good tidings that you are asking.
For us, the answer came from conversation, but it also came from reading the following books together:
Here is a book that is written by the research lead. It seems to deal more with game theory than Geometry
http://www.amazon.com/Linguistic-Geometry-Construction-Operations-Interfaces/dp/0792377389
I loved your interview the most in this book. You seem to be an awesome guy.
Sounds like you are interested in Operations Research as a discipline.
If you are looking for something to give you ideas about what longterm projects and outcomes look like, something like this book here(The Applied Business Analytics Casebook: Applications in Supply Chain Management, Operations Management, and Operations Research) might be good.
If you are looking for something more hands on, then either the Rardin or Nocedal and Wright books might be a good starting point.
Didn't he also attempt to shut down the Apple Macintosh project because he didn't really get it?
*Edit:
Wow. Thanks for the downvotes guys, talk about shitty reddiquette. I'm at home now, so I've got some time to dig out some citations.
The article from which I originally picked up that idea is this one, quoting Jef Raskin's interview in Peter Siebel's Coders at Work.
>What I proposed was a computer [the Macintosh] that would be easy to use, mix text and graphics, and sell for about $1,000. Steve Jobs said that it was a crazy idea, that it would never sell, and we didn’t want anything like it. He tried to shoot the project down.
When originally posted on Reddit, who should pop up in the comments but
Paul fucking Lutus! His summation was that yep, that's pretty much how it went down.
So, yea. If people who were actually there at the time (one of who is the guy that created the thing) are saying that technically Steve Jobs headed up that group, but only after trying to trash it because he couldn't get his head around it, I'm going to put some credence in those claims.
Stay classy /r/apple.
Get hold of a text like Modeling, Analysis, Design, and Control of Stochastic Systems (Springer Texts in Statistics) that covers both basic probability theory and markov chains and markov matrices (which you're likely to need for analysing turn based games).
(Edit) You might also want to look at discrete simulation (running a random simulation) if the problem gets too complex. There are packages around like SimPy for the purpose.
Coders at Work: Reflections on the Craft of Programming
I'm not actually familiar with that text. We used Probability for Statisticians - Shorack in my measure theory course but mostly because it was taught by Shorack. Looking through the Billingsley text it appears like it covers all the right material. I can't attest to how well it's written. For a more math based one (more focus on measure theory) I quite liked A Probability Path - Resnick as he assumes a little less mathematical maturity than e.g. Halmos Measure Theory.
No problem. The article (not technical) that really opened my mind and got me excited about the future of AI/machine learning was this one called "The Artificial Intelligence Revolution: The Road to Superintelligence" by Tim Urban. From there, I went on to discover a book called "Data Smart" by Jon Foreman which uses spreadsheets to teach machine learning. Both are excellent reads if you find yourself wanting more :)
Do you mean you want to get to know practical statistics/data science approaches to some practical problems? This book is pretty good, I think:
https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X/ref=sr_1_14?crid=1UOK00O4C8BY3&keywords=data+science&qid=1554939330&s=gateway&sprefix=data+scie%2Caps%2C133&sr=8-14
There is a whole sub-profession built around requirements engineering/management that may be of use:
https://www.amazon.com/Requirements-Engineering-Projects-Management-Industrial/dp/3319185969/ref=mt_hardcover?_encoding=UTF8&me=
http://dl.acm.org/citation.cfm?id=2227400
Also, UML and SysML have helped me a whole lot to clarify design elements to junior devs throughout my career. If you aren't expert-level in those, you may want to consider bootstrapping up some expertise ;)
Edit - Books more relevant to Software Requirements Management:
https://www.amazon.com/Visual-Software-Requirements-Developer-Practices/dp/0735667721/ref=sr_1_5?s=books&ie=UTF8&qid=1478992958&sr=1-5&keywords=software+requirements+management
https://www.amazon.com/Writing-Effective-Cases-Alistair-Cockburn/dp/0201702258/ref=pd_sim_14_4?_encoding=UTF8&psc=1&refRID=7273H45W7ZCJHCFZVP7M
Edit 2 - TL;DR - Build out your internal Software Requirements Process.
Really annoying - available for the Kindle for $9.99 but to simply buy the eBook in PDF is $20.99.
I'd like to read this on my nook. Do I really need to pay 2x the price as a Kindle user for that honor?
Bondy and Murty, Graph Theory.
Simon Peyton-Jones IS interviewed in the book. See the amazon page: "Simon Peyton Jones: Coinventor of Haskell and lead designer of Glasgow Haskell Compiler"
The 15 people interviewed are mentioned on that page.
https://class.coursera.org/ml-005
This started today. SVM's will be covered in a couple of weeks.
Also, if you're brand new to data, and you're stuck with just excel chops, this is supposedly a good place to start.
Also, there is an /r/machinelearning
This is a thinly disguised ad for "Coders at work". Indeed, as I have added it to my wish list...
I saw the name of a book by Lange (https://www.amazon.com/Optimization-Springer-Texts-Statistics-Kenneth/dp/1461458374/) in a Quora question: (https://www.quora.com/What-are-the-major-subfields-of-optimization-theory-What-textbooks-are-good-for-learning-about-them)
Can you comment on that book? It seems uptodate, general, and introduces math too.
Sure, there are a few directions you could go:
Algorithms: A basic understanding of how to think about and analyze algorithms is pretty necessary if you were to go into combinatorial optimization and is a generally useful topic to know in general. CLRS is the most famous introductory book on algorithms, and it gets the job done. It's long, but I thought it was decent enough. There are also plenty of video lectures on algorithms online; I liked the MIT OpenCourseWare of this class.
Graph Theory: Many combinatorial optimization problems involve graphs, so you would definitely want to know some graph theory. It's also super interesting, and definitely worth learning regardless! West is a good book with lots of exercises. Bondy and Murty and Diestel also have good books, which are freely available in PDF if you do a google search. Since you're doing a project on traffic optimization, you might find network flows interesting. Networks are directed graphs, where you think about moving "flow" across the edges of the graph, so they are useful for modelling a lot of real-life problems, including traffic. Ahuja is the best book I know on network flows.
Linear and Integer Programming: Many optimization problems can be described as maximizing (or minimizing) some linear function subject to a set of linear constraints. These are linear programs (LPs). If the variables need to take on integer values, then you have an integer program (IP). Most combinatorial optimization problems can be formulated as integer programs. Integer programming is NP-hard, but in practice there are methods that can solve most IPs , even very large ones, relatively quickly. So, if you actually want to optimize things in real-life this is a very useful thing to know. There's also a mathematically rich field of developing methods to solve IPs. It's a bit of a different flavor than the rest of this stuff, but it's definitely a fertile area of research. Bertsimas is good for learning linear programming. Unfortunately, I don't have a good recommendation for learning integer programming from scratch. Perhaps the chapters in Papadimitriou - Combinatorial Optimization would be a good introduction.
Approximation Algorithms: This is about algorithms which quickly (in polynomial time) find provably good but not necessarily optimal solutions to NP-hard problems. Williamson and Shmoys have a great book that is freely available here.
The last book I'd recommend is Schrijver. This is the bible for the field. I put it here at the end because it's more of a reference book rather than something you could read cover to cover, but it's REALLY good.
Lastly, if you like traffic optimization, maybe look up what people are doing in operations research departments. A lot of OR is about modelling real problems with math and analyzing the models, so this would include things like traffic optimization, vehicle routing problems, designing smart electric grids, financial engineering, etc.
Edit: Not sure why my links aren't all formatting correctly... sorry!
I am currently taking a Optimization & Control course, using this textbook: https://www.amazon.com/Numerical-Optimization-Operations-Financial-Engineering/dp/0387303030
I haven't spent much time with it yet, but I have had several people tell me it's a highly recommended book.
I will just add, A Probability Path is a fantastic, straightforward introduction to measure-theoretic probability. Highly recommended for anyone working in operations research and many other fields.
Rotina? Que rotina? :D
Eu trabalho como Quality Engineer; fazendo um trabalho misto entre "Software Engineer in Test", desenvolvendo soluções pra testes de produtos, e CI Engineer, desenvolvendo em mantendo o ambiente de integração contínua do time.
O meu dia a dia depende do tipo de produto, da fase do projeto ou da tecnologia envolvida. Então, em as vezes estou fazendo design/arquitetura de sistemas, programando, pesquisando, testando, etc.
Meu emprego é fenomenal e eu trabalho com alguns dos melhores profissionais do mundo em suas áreas. Apesar da pressão, o ambiente é relaxado e divertido.
Não creio, porém, que meu dia a dia seja suficientemente interessante além do meu mundo. Então, OP, se você tiver paciência, tempo e quiser se aprofundar mais sobre o assunto antes de tomar a decisão, tem um livro que talvez seja interessante para você e possa te dar uma ideia sobre programação sob o ponto de vista de algumas lendas da área. Talvez esse livro te ajude a reconhecer em você o mesmo tipo de interesse que esses caras tiveram. O livro em questão se chama Coders At Work: Reflections on the Craft of Programming.
Computational Statistics is 13.50 used on Amazon. Sold. Thanks!
RE: Heavy linear algebra prereq: the whole reason I asked this question is because I picked up Maximum Penalized Likelihood Estimation (at a much lower price than is listed here), and found that it expected some measure theory and more knowledge on vector spaces than I have. Is it expected that first year grad students know this stuff? Or do you pick it up as you go along?
I especially like this one : http://www.amazon.de/Approximation-Algorithms-Vijay-V-Vazirani/dp/3540653678/ref=sr_1_1?ie=UTF8&qid=1418204364&sr=8-1&keywords=approximation+algorithms
Aside from the other excellent choices people have recommended, here are a few I liked that I haven't seen in the thread yet.
This one sounds super-obscure. It's basically the design notes for the Common Lisp Object System, which isn't exactly a manual you need to read to get your work done. However, if you look at it less a book about how to use CLOS and more a book about how an object-oriented language can be built from scratch, it's really a fantastic little read.
It's what it says on the tin -- interviews with several programming icons. What makes this one better than the other half-dozen or so similar titles is how well the author runs those interviews.
If I'm honest, I didn't find this one to be that engaging of a read, but it's worth the bit of effort to get through it just to absorb Stepanov's vision for how to express algorithms. He's got a newer book as well that I have high hopes for, but I haven't had a chance to read it yet.
Data Smart: https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X
I think professionally Excel's use is limited to view small csv + run simple calculations, you can do a whole host of "data science" analysis using just excel alone. Check out Data Smart. The first book that introduced me to clustering.
This book by the head data scientist at MailChimp goes through a bunch of sophisticated analyses exclusively in Excel.
Perhaps the best thing to do is ask the people who where there at the beginning.
Here are a few that helped me. I'm always looking for more to keep me sharp.
Web Analytics 2.0
Data Smart
Good Strategy/Bad Strategy
Web Analytics Action Hero
Disclaimer: I'm still a student, so if you want to, go ahead and disregard my response. On the other hand, I have put in many hours contemplating this very question.
In "Coders at Work," Peter Seibel interviews the founder of JSON and JavaScript architect, Douglas Crockford.
Seibel poses the question
> Have you ever had problems ... (with) people who've been successful in one language (who) sometimes have a hard time giving up their old ways, even when working in a new language where they don't really make sense?
Crockford's response:
> I don't care. I just want to know that you know how to put an algorithm together, you understand data structures, and you know how to document it. (...) Generally, I prefer generalists. I want someone who's capable of learning any of those APIs but isn't necessarily skilled in any one.
In The Pragmatic Programmer, authors Andrew Hunt and David Thomas say
>The more different things you know, the more valuable you are. As a baseline, you need to know the ins and outs of the particular technology you are working with currently. But don't stop there. The face of computing changes rapidly -- hot technology today may well be close to useless (or at least not in demand) tomorrow. The more technologies you are comfortable with, the better you will be able to adjust to change.
The emphasis in comfortable is mine. It doesn't say the more technologies you master or are proficient at. Instead, being comfortable with many different areas, topics, technologies, languages, etc., will allow you to express your value to an employer in many different ways.
Now, specific to your current position. I have been with my current company for 9 years now. I started out as a cashier, moved into management after 9 months, and now I am a service technician working with all of the networking, computers, surveillance, construction, project management, etc. I am essentially a corporate representative with a LOT of autonomy, responsibility, and I wear a lot of hats. I am also the highest paid technician in the company for these very reasons. My job is perhaps one of the most stable in the company given the amount of general knowledge I have about the areas I work on actively.
Now, software might be different in that knowing a lot about everything is incredibly hard. However, picking a couple of specialized areas and being comfortable with many other areas is very likely to make you a valuable employee. It allows you to think up insightful solutions to multi-disciplinary problems. You can be the hero who comes up with novel solutions to larger problems, whereas people who specialize in C++, JavaScript, or Haskell might only know how to solve the same problems in their respective languages.
From what I can tell by reading the literature, those are the differences between people who specialize and people who generalize. I think you are experiencing what it's like to be good at generalizing. Incidentally, I would also equate CEO's, CTO's, COO's and other C-level people to generalists. They are capable of abstracting away the minutiae and details of their problems and delegate to others in order to get stuff done. They focus on big-picture stuff and let the specialists (accountants, technicians, programmers, drivers, etc) deal with the details.