With the Data Science field gathering an increasing amount of attention over the past few years, many people have redirected their career focus in an attempt to get into this fascinating occupation. But, with the learning journey being a long and challenging one, how long would it take one to learn Data Structures and Algorithms?
Data Structures and Algorithms can be learned in approximately 6 – 12 months with quality resources and guidance, depending on the individual’s learning capacity for this field and other influencing factors. Data Structures & Algorithms is a continuing area of extensive research, and absolute efficiency can take a lifetime.
Data Structures and Algorithms is one of the foundational branches of data science, as a predominant amount of the work in this field is based on these principles. There is no easy way to learn these elements, but there is an efficient way. In this article, we’ve broken down the fundamentals to help you figure out how long it would take to learn Data Structures and Algorithms. So, irrespective of whether you are a complete newbie for this vertical or simply looking for a more efficient way to expedite your learning, I am confident that you will find this article very helpful.
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How Long Does It Take to Learn Data Structures?
Data Structures can primarily be learned in approximately 6 – 12 months, depending on various factors that influence your learning capacity for this field. It should also be noted that it will be necessary to learn Data Structures in conjunction with Algorithms.
With regard to the function of Data Structures and Algorithms (commonly abbreviated as DS&A), DS (Data Structures) can generally be defined as a storage structure for a particular set of data, and you create these Data Structures to run different Algorithms.
There are plenty of more advanced variations within DS&A (such as – bipartite, graphs, and maximum streams) that can take a lengthy amount of time and practice to master. However, these are seldom used in typical development and data science work, and will not be necessary to learn in the beginning. There are a few basic aspects of Data Structures that should be focused on when learning this branch of the data science domain.
Basic Data Structures for starting off include array, linked list, stack, queue, hash table, map, heap, binary tree, trie tree, skip list, and graph. Additionally, the Data Structure APIs and the complexity of their operations within programming should be the focal point when you begin your educational journey in DS&A.
How Long Does It Take to Learn Data Algorithms?
The fundamentals of Algorithms can be learned in approximately 6 – 12 months, depending on various factors that can influence your learning capacity and efficiency towards learning this field. Within Data Science, an A (Algorithm) refers to a method or pattern for solving problems, which can be implemented within programming. The Algorithms will typically pull inputs from the Data Structures and then process those inputs to reach the desired output.
When one studies Algorithms, it’s more about comprehending reinvention, reason, and reimplementation of Algorithms as opposed to memorizing the steps and details of Algorithms themselves. While details will certainly need to be learned, the ideas that create the foundation for Algorithms is more crucial to its effective implementation within practical development.
There are some essentials that create the basis for understanding Algorithms, which should preferably be learned one after the other. These include sorting, binary search, search, string matching, recursion, hash algorithm, greedy algorithm, divide and conquer algorithm, backtracking algorithm, and dynamic algorithm.
For efficiency and quickness, the recommended learning path for these Algorithm fundamentals is as follows:
- Read the overall ideas of Algorithms
- Run some basic cases mentally or on the whitepaper
- Try to implement the algorithm in the editor or on a whitepaper by yourself
- Finish or optimize it
If there are hiccups in this process, one should repeatedly read the details of the Algorithm, read sample code from books, and repeat relative steps.
The Relationship Between Data Structures and Algorithms
When one discusses DS&A, there is a strong reference to queues, stacks, heaps, binary search, dynamic programming, etc. These are invented by computer pioneers, and created with the purpose of creating patterns for abstraction and problem-solving. They are used to resolve many practical development problems throughout the process.
The two aspects work cohesively to achieve this goal, complementing each other throughout each process. The Data Structure is designed for an Algorithm specifically, and the Algorithm is applied to a specific Data Structure.
Therefore, while there can be a particular focus on one of these two at any given time, these two components cannot necessarily be understood, implemented, and practiced in isolation. They will need to be learned and practiced in cohesion with each other in order to truly grasp the purpose and function of each principle within the bigger picture.
Is It Hard to Learn Data Structures and Algorithms?
Learning DS&A is certainly not easy, but frustration usually arises as a result of the theoretical and abstract nature of DS&A, as opposed to it being genuinely hard to learn. The underlying concept itself is quite complex and is far more complicated than most pieces of code. Writing, reading, understanding, and remembering relative information is the easiest way to fully comprehend this in the most stress-free way possible.
It’s important to note that programmers and those in relative occupational fields never stop learning DS&A, as this area is under consistent, on-going, and extensive research. This means that despite the fact that one could learn the fundamentals of DS&A within an approximate period of time, it would take an infinite amount of time to master it completely.
The difficulty level at which DS&A can be learned will primarily be based on prior knowledge of these principles, the purpose of learning them, the quality of the resources being used, as well as the guide or mentor.
Of course, any prior knowledge in this area will give you a head start in your learning journey, as opposed to someone who knows absolutely nothing about this occupational field. For someone who already has prior programming experience and is now hoping to learn DS&A, it could take a few days to a few months. Additionally, your motivation for learning DS&A will also play a key role in the difficulty level – whether you are simply trying to do well in an interview, or are going for competitive programming.
The resources available to you will have a massive impact on how challenging DS&A is for you to learn at your level. Data Visualization will aid the process of learning Data Structures and Algorithms effectively by giving visual representations of these principles, their functions, and how they assist each other.
There are some really great online resources and textbooks for DS&A. But, when starting your learning journey, you will need to focus on resources that are coded in a programming language that you are comfortable with or have started learning already. These would most likely be C++, Java, and Python.
A great plus for your learning journey is to have an experienced professional as a guide or mentor and establish yourself within the community. This allows you to learn and be provided with support, advice, and invaluable experience from those within the field.
Learning by doing is one of the best ways to combine theoretical and practical knowledge. There are also some really useful online websites such as Leetcode and HackerRank, which both contain fantastic online judge systems. These were created specifically for the purpose of deliberate practice with DS&A, which provides a solid support system and community for you to effectively learn the practice, and grow your knowledge and skills.
Should You Learn Data Structures and Algorithms First?
While learning DS&A individually may not require specific prerequisite skills other than a holistic understanding of the field, there are some recommendations if you want to implement DS&A and solve problems effectively. Get comfortable with C++, Java, and/or Python, and practice by writing and reading simple codes for a stronger foundation prior to learning DS&A.
DS&A is primarily the heart of Computer Science and relative fields and is one of the first elements that should be learned. They are the ultimate basis of any good programmer, and a solid knowledge of these two principles is key for improving relative skills and expertise. It will allow you to make thoughtful decisions and write programs that can perform well and handle alterations more flexibly over time.
Many beginners are often discouraged and frustrated by the abstract and obscure nature of DS&A. However, this is frequently due to lack of prior knowledge and understanding of the basics, lack of confidence, and lack of a solid learning method that works for the individual.
That being said, it’s important to understand that how long it takes to learn is not nearly as important as the learning process itself, and the speed at which you learn DS&A will not land you a good position in this profession. Focus on today, dedicate what time you have, and commit to improving your theoretical and practical skills with quality resources, a support system, and mentor guidance.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
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- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
On average, it can take approximately 6 to 12 months for a relatively smart individual to get comfortable with the essential concepts of Data Structures and Algorithms. However, if you have some background in programming and a passion for the domain, you can get up to speed on the bare minimum conceptual requirements of this field in a shorter span. For absolute mastery, one must note that Data Structures and Algorithms, as a field, is continuously evolving and it can take a lifetime to fully grasp every intricate topic within the domain.
That being said, while there are approximate time spans and efficient learning paths to follow, learning Data Structures and Algorithms should take as long as is required by the individual within his/her unique circumstances in order to be effective. The retention and understanding of DS&A and relative principles is ultimately what will allow you to successfully integrate into this occupational field.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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