Data Structures and Algorithms Every Data Scientist Should Know

The best practices for handling and organizing information should familiarize you as we are in the Age of Data. However, understanding ideas like data structures and algorithms is crucial if you want to work as a software engineer or in a field related to data science.

In this blog, the basic principles of data structures and algorithms will be discussed, along with their definitions, significance, fundamentals, and suggestions for learning them. We start our investigation with a few definitions.

What Is a Data Structure?

The quick answer is that a data structure is a particular way of arranging data in a system so that it may be accessed and used.

The long answer is that a data structure combines data organization, administration, retrieval, and storage into one format that enables effective access and modification. It involves gathering data values, their connections, and the relevant actions or functions.

Here is a case from the real world. You would go to the History section of the library if you wanted to find a book about 20th-century military history. You would then find the section allocated for military history and browse the books in chronological order until you came to the 20th century.

What Makes Data Structure Important?

Every year, the amount of data processed in the digital world grows. According to Forbes, every day, 2.5 quintillion bytes of data are produced. Over 90% of the currently available data was produced worldwide in the two years before 2018! The Internet of Things (IoT) is largely to blame for this data deluge.

The vast amounts of created data must be managed, and data structures are essential for increasing algorithm efficiency.

Last but not least, studying data structures and algorithms is a requirement for pursuing a career as a data scientist or programmer because they are used in almost all software applications. Knowing more about the ideas can help you respond to data structure interview questions more comfortably and confidently. Interviewers are looking for skilled individuals who understand how to use data structures and algorithms.

What is an Algorithm?

An algorithm is a series of well-thought-out, sequential instructions to solve a problem or carry out a particular task. An easy assignment might be multiplying two numbers, whereas a more difficult one would be playing a music file. Algorithms are typically written as functions in computer programming contexts. Visit the popular full stack developer course, designed by Learnbay in collaboration with IBM, for working professionals wanting to upgrade their DSA and tech skills. Learnbay also offers several data science courses for advanced experiential learning. 

Algorithms are occasionally brought up in social media and advertising. Consider the scenario when one day you decide to look for leather gloves on Google while online. After receiving your results and feeling successful, you take a break to check Facebook to see if any of your pals are online. How come? That is a digital marketing algorithm automating showing you advertising based on your previous searches.

Remember that data structures are split into basic and advanced categories as you consider how to examine them.

Basic Data Structures:

  • Arrays
  • Dictionaries
  • Graphs
  • Hash Tables
  • Heaps
  • Linked lists
  • Queues
  • Stacks
  • Trees

Advanced-Data Structures:

  • Binary indexed tree
  • Disjoint set
  • Segment tree
  • K Dimensional tree
  • Self-balancing BSTs
  • Suffix array and tree
  • Trie

Techniques for Data Structure Searching

Due to the fact that data scientists employ algorithms to search for data, when we refer to data structure-finding strategies, we really mean search algorithms. The two main search algorithms are binary and linear. Thus anyone who wants to be a data analyst or data scientist should become familiar with both.

  • Linear Search 

Checking each item in a data input file until you discover the proper one is the process of a linear search algorithm. Because the number of items in your search and the search time is exactly matched, for example, 40 items/input = 40 checks/complexity, this type of search is known as a linear search. Due to the orderly traversal of the array or list to examine each element, linear searches are also known as sequential searches.

In a movie line, for instance, you might travel down the line and scan each face until you spot your friend Steve. It’s a linear search there.

  • Binary Search 

The cleverly named “binary” search method divides the input into two halves before locating the desired item. The required search item is present in one part but not in the other. The algorithm repeats the procedure until the divided item becomes the sought-after item. Think of it as a very structured and controlled form of the elimination process. Interval searches are another name for binary searches.

Even though binary searches are quicker than linear ones, they can only be used with sorted sequences. Let’s say that Steve, your acquaintance from before, is 5’10”. Everyone in the theater line is arranged in an ascending height configuration from left to right (who knows, maybe the cinema staff has OCD). Your search area has just been trimmed in half. Then you choose the middle individual from the remaining group on the right, and so on, until you find Steve. Why Steve didn’t speak out and save you the hassle earlier is a mystery to us. Perhaps Steve is a jerk. The other possibility is that he wants to teach you binary search algorithms.

Sorting Algorithms

One of the most typical programming tasks that developers are expected to complete is sorting, often known as ordering. Binary searches are made possible by ordering, which transforms your chaotic data into a structured shape. It should come as no surprise that data scientists do a lot of searching and sorting.

Some of the common sorting algorithms are:

  • QuickSort
  • HeapSort
  • Insertion Sort
  • MergeSort
  • Bubble/ Selection sort 

Summing up, these are the fundamentals of DSA you have to know as a data scientist. If you are a beginner, and want to advance your DSA skills, register in Learnbay’s data structures and algorithms course, and learn directly from the tech leaders.