6 Algorithms Every Aspiring Programmer Should Learn Early
In a world where coding skills are increasingly essential, aspiring programmers often wonder which algorithms are crucial to grasp early on. Insights from a CEO and a Distinguished Professor shed light on this important question, ensuring the advice comes from top-tier professionals. The article starts with a deep dive into the mastery of the Binary Search Algorithm and wraps up by emphasizing the importance of choosing the appropriate algorithm, featuring a total of six expert insights. These foundational algorithms form the backbone of efficient and effective programming.
- Master Binary Search Algorithm
- Implement Double Hashing
- Understand Algorithmic Mathematics
- Learn Gradient Descent
- Study Huffman Coding
- Choose Appropriate Algorithm
Master Binary Search Algorithm
One foundational algorithm that every aspiring programmer should learn early in their journey is the binary-search algorithm. This algorithm is critical because it introduces several key concepts that are essential for effective problem-solving and programming.
Binary search operates on sorted arrays and efficiently finds the position of a target value by repeatedly dividing the search interval in half. This contrasts with a linear search, which checks each element one by one, making binary search significantly faster, especially with large datasets, as it has a time complexity of O(log n). Understanding how binary search works not only enhances an aspiring programmer's ability to work with data structures effectively but also emphasizes the importance of algorithmic efficiency and the concept of divide-and-conquer strategies.
Additionally, mastering binary search lays the groundwork for learning more complex algorithms and data structures, such as trees and graphs, where similar searching techniques can be applied. Overall, binary search is a fundamental algorithm that fosters critical thinking and algorithmic reasoning, making it an essential part of any programmer's toolkit.
Implement Double Hashing
Double Hashing.
Hashing is one of the most important algorithms used to look up items in a set. It's used everywhere for efficient lookup (not just data indexing, but places such as packet processing in network equipment). Hashing operates faster than binary search or binary search trees. Unfortunately, many courses and textbooks discuss a form of hashing that uses "open addressing." Open addressing performs a linear search when two items hash to the same slot in the hash table. As a consequence, all items that hash to a given slot in the hash table will clump in the following slots. Double hashing takes just about the same effort to implement as open addressing and avoids clumping entries. Try it. Measure the performance for yourself as a hash table fills up, and you will never use open addressing again.
Understand Algorithmic Mathematics
It's not so much a specific algorithm, but the mathematics behind the algorithm. Taking simple binary searches or traversals through binary trees, or setting up permutations of n items—doesn't really matter the algorithm that is taught, but rather how to train a programmer to think through the mathematics behind the algorithm.
The more mathematics understood, the more creative and efficient a programmer can be in developing their own algorithms and discovering new ones in the future!
Learn Gradient Descent
Gradient Descent is a fundamental algorithm with many important applications, including areas of machine learning, deep learning, artificial intelligence, engineering, physics, and mathematics. Intuitively, in machine learning, the algorithm tries to minimize a first-order cost function of a neural network or minimize error in predictions, which are critical to efficient progress. The function involved needs to be differentiable and convex. However, there can also be some success with quasi-convex functions, which contain saddle points where the computational progress can stall or become trapped by local minima. In that case, second-order methods may be more effective. Some variations of the algorithm, like the momentum-based gradient descent, attempt to speed up the process by using information from past iterations of the algorithm. In mathematical physics, the predictor-corrector method can give numerical solutions of ordinary differential equations. Techniques from predictor-corrector methods can enhance the efficiency of gradient-descent algorithms, especially when compared to the results of stochastic-based methods.
Study Huffman Coding
Every aspiring programmer should learn Huffman coding. It is foundational, as it introduces the concepts of data compression and optimal encoding, which are critical in fields like file compression, cryptography, and telecommunications. Huffman is a beautiful algorithm that gives satisfying and tangible results.
Choose Appropriate Algorithm
There shouldn't be one algorithm that every programmer should learn. The more important skill is to know which algorithm would be appropriate to use, depending on the task at hand.
For example, there is no one best algorithm for sorting. I once had to design a system that would sort 10,000s of customer data records. Normally, one might reach for quicksort. However, these records were already broken into many small groups, and each group was nearly sorted, so insertion sort was a much more efficient choice.
Gaining a breadth of knowledge as quickly as possible is the key to becoming a successful programmer. Go beyond just memorizing Big-O complexity—learn how to analyze an algorithm and understand the pros and cons so you can make informed decisions.
