Top Data Structures and Algorithms Every Data Scientist Should Know

What Is a Data Structure?

The short answer is: a data structure is a specific means of organizing data in a system to access and use.

The long answer is a data structure is a blend of data organization, management, retrieval, and storage, brought together into one format that allows efficient access and modification. It’s collecting data values, the relationships they share, and the applicable functions or operations.

Here’s a real-world example. If you go to the library and want to find a book on 20th-century military history, you’d go to the History section. From there, you’d find the designated area set aside for military history, then go through the books, sorted in chronological order, until you found the 20th century. Now, consider the books as your data, and the library’s method of sorting the books as the data structure, and you’re all set!

Why Data Structure is Important?

The digital world processes an increasing amount of data every year. According to Forbes, there are 2.5 quintillion bytes of data generated daily. The world created over 90 percent of the existing data in 2018 in the previous two years! The Internet of Things (IoT) is responsible for a significant part of this data explosion.

Data structures are necessary to manage the massive amounts of generated data and a critical factor in boosting algorithm efficiency.

Finally, since nearly all software applications use data structures and algorithms, your education path needs to include learning data structure and algorithms if you want a career as a data scientist or programmer. Interviewers want qualified candidates who understand how to use data structures and algorithms, so the more you know about the concepts, the more comfortably and confidently you will answer data structure interview questions.

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What Is an Algorithm?

An algorithm is a set of well-designed, step-by-step instructions designed to solve a problem or perform a specific task. The task can be something as simple as multiplying two numbers, or a more complex operation, like playing a music file. In a computer programming context, algorithms are frequently created as functions.

Sometimes you hear people talk about algorithms in the context of social media and advertisement. For instance, say one day you’re online and you conduct a search on Google for leather gloves. You get your results and, feeling like you’ve accomplished something, you take a break and see if any of your friends are on Facebook. When you log in, you find yourself face to face with a Facebook ad for gloves! What gives? That’s an algorithm at work in digital marketing, automating the task of displaying ads for you based on your previous searches.

When you’re figuring out how to study data structures, keep in mind that they are divided into basic and advanced data structures.

Common Data Structures and Algorithms

Data scientists often rely on a core set of data structures and algorithms to analyze data efficiently and solve problems. Understanding these fundamentals can significantly impact your ability to process and analyze data effectively. Here's a list of the top data structures and algorithms every data scientist should know:

Data Structures

1. Arrays and Lists: Essential for storing collections of data. Arrays are fixed in size, while lists can grow dynamically.
2. Linked Lists: Consist of nodes that together represent a sequence. Each node contains data and a reference to the next node in the sequence. Useful for efficient insertion and deletion.
3. Stacks and Queues: Stacks follow the Last In, First Out (LIFO) principle, while queues follow the First In, First Out (FIFO) principle. Both are pivotal in managing data in a specific order.
4. Hash Tables: Implement mappings of keys to values, making data retrieval efficient. Excellent for lookup operations and data indexing.
5. Trees, especially Binary Search Trees: Trees represent hierarchical data, and binary search trees allow for efficient searching, insertion, and deletion of data.
6. Graphs: Represent networks of nodes connected by edges. Crucial for modeling relationships and networks, including social networks, transportation networks, and dependency trees.
7. Heaps: A special type of binary tree where the parent node is either greater than or equal to (max heap) or less than or equal to (min-heap) its child nodes. Useful for implementing priority queues.

Algorithms

1. Sorting Algorithms: Such as QuickSort, MergeSort, and BubbleSort. Sorting is foundational for many data processing tasks.
2. Searching Algorithms: Including Binary Search (efficient on sorted data) and Depth-First Search (DFS), and Breadth-First Search (BFS) for traversing trees and graphs.
3. Dynamic Programming: A method for solving complex problems by breaking them into simpler subproblems. It's used in various tasks, including optimizing algorithms for data analysis.
4. Greedy Algorithms: Make the locally optimal choice at each stage to find a global optimum. Useful in optimization problems.
5. Graph Algorithms: These include Dijkstra's algorithm for shortest paths, Kruskal's or Prim's algorithm for minimum spanning trees, and network flow algorithms.
6. Machine Learning Algorithms: Understanding the data structures that underpin machine learning models (e.g., decision trees, neural networks) is crucial for efficient data science.
7. Hashing Algorithms: Used for efficient data retrieval, cryptographic applications, and data deduplication.
8. Tree Traversals: In-order, pre-order, post-order, and level-order traversals are essential for processing data stored in trees.

Data Structure Searching Techniques (a.k.a. Algorithms)

When we talk about data structure searching techniques, we mean search algorithms, since data scientists use algorithms to conduct data searches. That’s why any aspiring data analyst or data scientist should become acquainted with the two primary search algorithms: binary and linear.

Linear

A linear search algorithm entails checking each item in a data input file until you find the right one. It’s called a linear search because the search time precisely matches the number of items in your search, e.g., 40 items/input = 40 checks/complexity. Linear searches are also called sequential searches because the array or list is traversed in sequence, checking each element.

For example, if you’re looking for your friend Steve in a movie queue, you go down the line, looking at each face until you find Steve. That’s a linear search.

Binary

A binary search algorithm divides the input into two parts (hence the clever name, “binary”) until it locates the item in question. One half has the desired search item, and the other half doesn’t. The algorithm continues the process until the divided item becomes the searched-for item. Consider it a very organized and disciplined version of the process of elimination. Binary searches are also called interval searches.

Binary searches are faster than linear searches, but they only function with ordered sequences. Using your friend Steve again, let’s say that Steve is 5’10”. Everyone in the theater line stands in ascending height formation from left to right (who knows, maybe the cinema staff has OCD). You choose the middle person in the line, who happens to be 5’6”, and eliminate them and everyone to their left. You’ve just cut your search field in half. Then you select the middle person from that right-hand side remainder and keep repeating this until you finally find Steve. We have no idea why Steve didn’t speak up sooner and save you the trouble. Maybe Steve’s a jerk. Or perhaps he wants to teach you binary search algorithms.

In summary, binary searches are faster and more efficient, but the information list needs to be in sorted order. If you need to search through messy, disorganized data, opt for the linear approach. Otherwise, stick with binary searches.

There are many other types of searching available besides linear and binary. For example:

• Breadth-first search
• Depth-first search
• Exponential search
• Fibonacci search
• Interpolation search
• Jump search
• Sublist search (searching a linked list in another list)
• Recursive function to conduct a substring search
• Recursive program to conduct a linear search an element in a particular array
• Ubiquitous binary search
• Unbounded binary search example (Find the point where a monotonically increasing function becomes positive first time)

Sorting Algorithms

Sorting, also known as ordering, is one of the most common programming tasks expected of developers. Ordering takes your disorganized data and places it in a structured form, making it possible to use binary searches. Unsurprisingly, data scientists work a lot with searching and sorting.

Here are some of the more popular sorting algorithms:

• Mergesort
• QuickSort
• HeapSort
• Introsort
• Insertion Sort
• Bubble/Selection Sort

A Closer Look at Two Valuable Data Search Techniques

Here are two essential tools to use in the world of data structures and algorithms.

Dynamic Programming (DP)

If you’re stuck on a massive, unwieldy programming problem that threatens to overwhelm you, use dynamic programming. DP takes its cue from the old riddle, “How do you eat an entire elephant?” The answer is, “One bite at a time!” Dynamic programming breaks the big problem into many smaller problems. Each time DP solves a sub-problem, it saves the results. Eventually, DP combines all the saved results to solve the big problem.

String Pattern Matching

Instead of searching for a particular item, you’re looking for a pattern found in a group of items. These pattern matches help narrow down the search.

The Best Path for the Data Science Professional

Now that you’ve endured a barrage of data science-related information and technical jargon, you’re probably wondering where to go next. Believe it or not, there is a recommended path for data science/software programming professionals.

First, master Search and Sort, specifically Linear and Binary in the former case, and SortMerge and QuickSort in the latter. If you master these, you already have the basics nailed down and can give a good account of yourself in programming and data analysis.

Follow up those initial subjects with dynamic programming, graph traversal (Breadth-First Searches and Depth-First Searches), string pattern matching, and trees.

Finally, gradually change your perspective on solving real-world problems, moving towards imagining step-by-step answers, and reducing complex scenarios to simple data structures. If you cultivate this mindset, programming will become an intuitive thing for you.

Advance Your Career with the Right Program

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How to Become a Better Data Scientist?

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