Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People

Key Takeaways

“Grokking Algorithms” was the first programming book I read purely for personal interest rather than academic or professional requirements. While aimed at beginners, the book provides value through its visual approach to algorithm explanation and practical examples that demonstrate theoretical concepts.

Visual Learning Approach

The book’s greatest strength is its commitment to visual learning. Complex concepts like recursion and graph algorithms are illustrated with clear diagrams that help build mental models:

• Binary search is depicted as a process of eliminating half of the remaining values with each step
• Sorting algorithms are shown through animated-style progression of array transformations
• Graph theory is introduced through intuitive node-edge diagrams that clarify abstract concepts

This visual approach makes typically challenging computer science topics more accessible than traditional textbooks.

Algorithm Coverage

For a relatively slim volume, the book covers a surprising breadth of fundamental algorithms:

• Search algorithms (binary search)
• Sorting algorithms (selection sort, quicksort)
• Recursion with practical examples
• Hash tables and their applications
• Breadth-first search
• Dijkstra’s algorithm
• Greedy algorithms
• Dynamic programming

The explanations prioritize intuition over mathematical rigor, making them accessible to readers without extensive math backgrounds.

Code Examples

The book uses Python for its code examples, which I found helpful as a Python user myself. The code follows this pattern:

def binary_search(list, item):
    low = 0
    high = len(list) - 1

    while low <= high:
        mid = (low + high) // 2
        guess = list[mid]

        if guess == item:
            return mid
        if guess > item:
            high = mid - 1
        else:
            low = mid + 1

    return None

The Python examples are straightforward and focus on the algorithm itself rather than language-specific features, making them transferable to other languages.

Applications in My Work

While I was already familiar with many of the algorithms covered, revisiting them through Bhargava’s explanations helped solidify my understanding. Several concepts have proven useful in my daily work:

1. Hash tables for optimization - The discussion of hash tables and their O(1) lookup time reminded me to consider dictionary/map data structures when optimizing search operations in scripts.

2. Dijkstra’s algorithm for dependency resolution - Though not directly implementing the algorithm, understanding the principles has helped me think through dependency chains in complex systems.

3. Dynamic programming approach - Breaking down problems into smaller subproblems has informed my approach to tackling complex programming tasks.

Critical Assessment

The book has clear strengths but also limitations worth noting:

First, the simplified explanations occasionally sacrifice technical precision for accessibility. The big O notation discussion, while approachable, doesn’t fully explore the mathematical underpinnings that would be needed for more advanced algorithm analysis.

Second, some important algorithm categories receive minimal coverage. For instance, tree traversal algorithms are touched on briefly but not explored in depth, and there’s limited coverage of more advanced data structures like balanced trees.

Third, the exercises are relatively basic, which limits opportunities for readers to challenge themselves with algorithm implementation. More complex problem sets would strengthen the learning experience.

That said, the book succeeds in its primary goal of making algorithms approachable. Its visuals and clear explanations serve as a valuable foundation for further exploration of computer science topics.

For Whom Is This Book?

“Grokking Algorithms” is best suited for:

• Programming beginners seeking an introduction to fundamental algorithms
• Self-taught developers looking to fill gaps in theoretical knowledge
• Experienced programmers wanting a refresher on algorithm basics
• Visual learners who struggle with more text-heavy computer science resources

For readers already well-versed in algorithms through formal computer science education, the book may feel too basic, though the visual explanations might still offer fresh perspectives on familiar concepts.

Additional Resources

For those wanting to dive deeper after reading this book, I’d recommend:

• “Introduction to Algorithms” by Cormen, Leiserson, Rivest, and Stein (for a more comprehensive and rigorous approach)
• “The Algorithm Design Manual” by Steven Skiena (for more practical applications)
• Online platforms like LeetCode and HackerRank for practicing algorithm implementation

Conclusion

“Grokking Algorithms” stands out as an accessible entry point to algorithm study. While it won’t prepare you for advanced algorithm research or competitive programming, it builds a solid foundation and—perhaps more importantly—makes algorithms engaging through its visual approach.

At 3 stars, it’s a worthwhile read for beginners or as a refresher for experienced programmers, though those seeking depth may need to supplement with more advanced resources.