If you're looking to learn Python then Practical Programming: An Introduction to Computer Science Using Python 3 is what I recommend. How to Think Like a Computer Scientist: Learning with Python is also very good.

Now in terms of the question that you ask in the title - this is what I recommend:

Job Interview Prep

1. Cracking the Coding Interview: 189 Programming Questions and Solutions
   
2. Programming Interviews Exposed: Coding Your Way Through the Interview
   
3. Introduction to Algorithms
   
4. The Algorithm Design Manual
   
5. Effective Java
   
6. Concurrent Programming in Java™: Design Principles and Pattern
   
7. Modern Operating Systems
   
8. Programming Pearls
   
9. Discrete Mathematics for Computer Scientists
   
   

   Junior Software Engineer Reading List
   

   
   

   Read This First
   

   
   

10. Pragmatic Thinking and Learning: Refactor Your Wetware
    
    

    Fundementals
    

    
    

11. Code Complete: A Practical Handbook of Software Construction
    
12. Software Estimation: Demystifying the Black Art
    
13. Software Engineering: A Practitioner's Approach
    
14. Refactoring: Improving the Design of Existing Code
    
15. Coder to Developer: Tools and Strategies for Delivering Your Software
    
16. Perfect Software: And Other Illusions about Testing
    
17. Getting Real: The Smarter, Faster, Easier Way to Build a Successful Web Application
    
    

    Understanding Professional Software Environments
    

    
    

18. Agile Software Development: The Cooperative Game
    
19. Software Project Survival Guide
    
20. The Best Software Writing I: Selected and Introduced by Joel Spolsky
    
21. Debugging the Development Process: Practical Strategies for Staying Focused, Hitting Ship Dates, and Building Solid Teams
    
22. Rapid Development: Taming Wild Software Schedules
    
23. Peopleware: Productive Projects and Teams
    
    

    Mentality
    

    
    

24. Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency
    
25. Against Method
    
26. The Passionate Programmer: Creating a Remarkable Career in Software Development
    
    

    History
    

    
    

27. The Mythical Man-Month: Essays on Software Engineering
    
28. Computing Calamities: Lessons Learned from Products, Projects, and Companies That Failed
    
29. The Deadline: A Novel About Project Management
    
    

    Mid Level Software Engineer Reading List
    

    
    

    Read This First
    

    
    

30. Personal Development for Smart People: The Conscious Pursuit of Personal Growth
    
    

    Fundementals
    

    
    

31. The Clean Coder: A Code of Conduct for Professional Programmers
    
32. Clean Code: A Handbook of Agile Software Craftsmanship
    
33. Solid Code
    
34. Code Craft: The Practice of Writing Excellent Code
    
35. Software Craftsmanship: The New Imperative
    
36. Writing Solid Code
    
    

    Software Design
    

    
    

37. Head First Design Patterns: A Brain-Friendly Guide
    
38. Design Patterns: Elements of Reusable Object-Oriented Software
    
39. Domain-Driven Design: Tackling Complexity in the Heart of Software
    
40. Domain-Driven Design Distilled
    
41. Design Patterns Explained: A New Perspective on Object-Oriented Design
    
42. Design Patterns in C# - Even though this is specific to C# the pattern can be used in any OO language.
    
43. Refactoring to Patterns
    
    

    Software Engineering Skill Sets
    

    
    

44. Building Microservices: Designing Fine-Grained Systems
    
45. Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools
    
46. NoEstimates: How To Measure Project Progress Without Estimating
    
47. Object-Oriented Software Construction
    
48. The Art of Software Testing
    
49. Release It!: Design and Deploy Production-Ready Software
    
50. Working Effectively with Legacy Code
    
51. Test Driven Development: By Example
    
    

    Databases
    

    
    

52. Database System Concepts
    
53. Database Management Systems
    
54. Foundation for Object / Relational Databases: The Third Manifesto
    
55. Refactoring Databases: Evolutionary Database Design
    
56. Data Access Patterns: Database Interactions in Object-Oriented Applications
    
    

    User Experience
    

    
    

57. Don't Make Me Think: A Common Sense Approach to Web Usability
    
58. The Design of Everyday Things
    
59. Programming Collective Intelligence: Building Smart Web 2.0 Applications
    
60. User Interface Design for Programmers
    
61. GUI Bloopers 2.0: Common User Interface Design Don'ts and Dos
    
    

    Mentality
    

    
    

62. The Productive Programmer
    
63. Extreme Programming Explained: Embrace Change
    
64. Coders at Work: Reflections on the Craft of Programming
    
65. Facts and Fallacies of Software Engineering
    
    

    History
    

    
    

66. Dreaming in Code: Two Dozen Programmers, Three Years, 4,732 Bugs, and One Quest for Transcendent Software
    
67. New Turning Omnibus: 66 Excursions in Computer Science
    
68. Hacker's Delight
    
69. The Alchemist
    
70. Masterminds of Programming: Conversations with the Creators of Major Programming Languages
    
71. The Information: A History, A Theory, A Flood
    
    

    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.
    
    

72. Peter Norton's Assembly Language Book for the IBM PC
    
73. Expert C Programming: Deep C Secrets
    
74. Enough Rope to Shoot Yourself in the Foot: Rules for C and C++ Programming
    
75. The C++ Programming Language
    
76. Effective C++: 55 Specific Ways to Improve Your Programs and Designs
    
77. More Effective C++: 35 New Ways to Improve Your Programs and Designs
    
78. More Effective C#: 50 Specific Ways to Improve Your C#
    
79. CLR via C#
    
80. Mr. Bunny's Big Cup o' Java
    
81. Thinking in Java
    
82. JUnit in Action
    
83. Functional Programming in Scala
    
84. The Art of Prolog: Advanced Programming Techniques
    
85. The Craft of Prolog
    
86. Programming Perl: Unmatched Power for Text Processing and Scripting
    
87. Dive into Python 3
    
88. why's (poignant) guide to Ruby

It varies in industry. I think it's a great idea to have a general understanding of how processors execute out of order and speculate, how caches and the cache consistency protocols between cores work, and how the language implementation transforms and executes the source you write.

The Hennesy and Patterson book covers almost everything hardware wise. Skim the areas that seem interesting to you. For language internals I like Programming Language Pragmatics. Compared to other "compiler course" textbooks like the famous dragon book it's got a lot more of the real world engineering details. It does cover quite a bit of theory as well though, and is written in a really straightforward way.

Skimming these two books will give people a pretty accurate mental model of what's going on when code executes.

Computer Science is a pretty big field, so "strong foundation" can mean different things to different people.
You will definitely want the following:

1. Introduction to Algorithms and Data Structures
   
2. Introduction to Computability
   
3. Introduction to Operating Systems
   
   For algorithms and data structures, a very commonly used textbook is Cormen.
   For computability, Sipser.
   
   Operating Systems I don't remember off the top of my head.
   That said, you are probably much better off finding a high-quality university course that is based on these textbooks instead of trying to read them cover-to-cover yourself. Check out lecture series from places like MIT on youtube or whatever.
   
   After that, you can take an Intro to Artificial Intelligence, or Intro to Communication Networks, or any other intro-level course to a more specific sub-area. But if you lack basis in computability to the point where you don't know what an NP-Complete problem is, or have no idea what a Binary Search Tree is, or do not know what an Approximation Algorithm is, then it would be hard to say you have a strong foundation in CS.

My compilers course in college used the Dragon Book, which is one of the more quintessential books on the subject.

​

But you might also consider Basics of Compiler Design which is a good and freely available resource.

​

I'd also suggest that you have familiarity with formal languages and automata, preferably through a Theory of Computation course (Sipser's Introduction to the Theory of Computation is a good resource). But these texts provide a brief primer.

Turing's Cathedral: The Origins of the Digital Universe https://www.amazon.com/dp/1400075998/ref=cm_sw_r_cp_api_i_AToIDb7FX1A9D

Its about the creation of the institue for advanced study with Von Neumann, Oppenheimer and the rest of them. Fascinating story about the politics around the creation of modern computer science.

I'm partial to Hopcroft and Ullman. I'm usually not the "open a book, read the explanations..." type of learner but Hopcroft&Ullman is very clear and concise, so I found myself doing just that. No need to skip ahead to figure out the point of it all, they just explain it in logical order. This text goes quite a bit deeper than most algorithms courses will go but it's a great way to pump iron with your brain and everything you learn will be applicable to whatever curriculum is taught in your algorithms course.

First, read this article, The Visualization Zoo:
http://queue.acm.org/detail.cfm?id=1805128

And then read this article, Interactive Dynamics for Visual Analysis:
http://queue.acm.org/detail.cfm?id=2146416

Buy and read this book, Readings in Information Visualization: Using Vision to Think:
http://www.amazon.com/dp/1558605339

And then start creating some visualizations. Try creating some visualizations using Many Eyes and NodeXL. You can also try coding up some visualizations in JavaScript using http://d3js.org/

The Annotated Turing is fantastic! Also check out Turing's Cathedral for some insight into how his 1936 paper influenced computing into the next few decades and The Essential Turing to read Turing in his own words.

For a look at how Turing influenced information theory (and a fascinating general introduction to its history), check out The Information.

It's a bit vague but yes, this is correct as an outline.

I just replied to what I think was another similar question of yours. I'd strongly suggest finding a copy of https://www.amazon.com/Computer-Organization-Design-MIPS-Architecture/dp/0124077269

It's the more mild textbook from two of the biggest people in the computer engineering world. They're the people that invented RISC.

It's a pricey book but for the sorts of questions you're after, any edition from the 3rd on to newer should be fine.

If you only read one work on the topic, it should be The Art of Computer Programming by Don Knuth: https://www.amazon.com/dp/0321751043/

The textbook for MIT's 6.001 (introduction to computer science) is the much loved Structure and Interpretation of Computer Programs by Abelson, Sussman, and Sussman: https://www.amazon.com/dp/0262510871/ . Originally it was in Scheme but the 2nd edition is in Python.

Finally, because people asking about computer science are often asking about something a bit broader than pure computer science, I recommend Code: The Hidden Language of Computer Hardware and Software by Charles Petzold. It is a thorough tour of computing in practice at every level, top to bottom. https://www.amazon.com/dp/073560505X/

It seems that your comment contains 1 or more links that are hard to tap for mobile users.
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Thanks, I already have a book on this I was planning on reading: Introduction to Automata Theory, Languages, and Computation. I just started reading CLRS though, do you think it would be helpful to finish it or are the two mostly unrelated?

A good intro book might be Programming Language Pragmatics:

http://www.amazon.com/Programming-Language-Pragmatics-Third-Edition/dp/0123745144


A more theoretical treatment that builds a language from the lambda calculus can be found in Types and Programming Languages:

http://www.amazon.com/Types-Programming-Languages-Benjamin-Pierce/dp/0262162091


Lastly, I think Practical Foundations for Programming Languages strikes a nice balance between theory and practicality:

http://www.amazon.com/Practical-Foundations-Programming-Languages-Professor/dp/1107029570

I've read most of the last two books, and they're both excellent resources for learning how to think rigorously about programming languages. They're challenging reads, but you'll walk away with a higher understanding of programming language constructs as result. A draft version of the latter book can be found on the author's website, here.