Further Reading: Algorithms and Problem-Solving Strategies

Big-O Notation and Algorithm Analysis

Bhargava, A. (2024). Grokking Algorithms, 2nd Edition. Manning Publications. (Tier 1) The best visual introduction to algorithms for beginners. (The 2016 first edition is just as readable if that's the one your library has.) Each chapter uses illustrations to explain concepts like Big-O, binary search, and graph algorithms. If this chapter's Big-O explanation clicked, this book will deepen your understanding with more examples and pictures.
Cormen, T. H. (2013). Algorithms Unlocked. MIT Press. (Tier 1) Written by one of the authors of the definitive algorithms textbook (CLRS), but aimed at a general audience. Covers algorithm analysis, searching, sorting, and graph algorithms without heavy math. A great bridge between this chapter and a full algorithms course.

Skiena, S. S. (2020). The Algorithm Design Manual, 3rd Edition. Springer. (Tier 1) A practitioner's guide to algorithms. Part 1 teaches design techniques; Part 2 is a catalog of algorithmic problems and solutions. The "war stories" — real-world cases where algorithm choice mattered — are particularly valuable.
Roughgarden, T. Algorithms Illuminated series (4 volumes). Soundlikeyourself Publishing. (Tier 1) Based on Stanford's algorithms courses. Accessible, rigorous, and well-paced. Volume 1 covers the basics: Big-O, divide and conquer, and sorting. Available in both book and free online lecture format.

McDowell, G. L. (2015). Cracking the Coding Interview, 6th Edition. CareerCup. (Tier 1) While aimed at interview preparation, the first few chapters provide an excellent practical introduction to Big-O analysis and problem-solving strategies. The worked examples are clear and build intuition.
Heineman, G. T., Pollice, G., & Selkow, S. (2016). Algorithms in a Nutshell, 2nd Edition. O'Reilly. (Tier 1) A concise reference that focuses on when and why to use each algorithm, not just how they work. Includes empirical performance data alongside Big-O analysis.

Big-O Cheat Sheet (bigocheatsheet.com) (Tier 2) A quick-reference poster showing the time and space complexity of common data structures and algorithms. Useful to pin up near your monitor while you're developing Big-O intuition.
VisuAlgo (visualgo.net) (Tier 2) Animated visualizations of data structures and algorithms. Watching a sorting algorithm execute step-by-step builds intuition far faster than reading about it. Free and covers searching, sorting, graph algorithms, and more.

Knuth, D. E. The Art of Computer Programming, Volumes 1–4B. Addison-Wesley. (Tier 1) The magnum opus of computer science. Dense, mathematical, and comprehensive. You won't read this cover-to-cover in CS1, but browsing through it gives you a sense of the depth behind the concepts introduced in this chapter. The opening quote of this chapter comes from Volume 1, Section 1.1, where Knuth continues: "the best way to learn what an algorithm is all about is to try it" — the same advice behind this chapter's emphasis on tracing.
Dasgupta, S., Papadimitriou, C., & Vazirani, U. (2006). Algorithms. McGraw-Hill. (Tier 1) One of the most elegant algorithms textbooks — concise, mathematically clean, and surprisingly readable. The chapter on greedy algorithms is particularly good.