For some programmers, there is an important question to ask. This question is, “should I take a data structures and algorithms online course?”

In today’s world of programming, a lot of coding is done with library functions. Some argue that if you use libraries, you don’t need to know data structures and algorithms.

If libraries are used so often, why bother learning about these things?

Understanding data structures and algorithms help you understand the logic of these functions. You can see more clearly why they are important.

Secondly, having improved analytical and logic skills can help you solve a lot of different problems. This is especially true for programming problems. But analytical and logic skills can help you solve problems in everyday life too.

Many coders also think that understanding data structures and algorithms will help you write better code. After all, how can you write good code if you don’t know how the functions of that code work?

The best data structures and algorithms courses are outlined below.

This list of data structures courses and algorithms courses was made based on a methodology we devised. This methodology ranks each course based on three features:

- Affordability
- Course Ratings
- Flexibility

Below, this methodology is discussed in more detail.

## Methodology to Determine the Best Online Courses for Data Structures and Algorithms

There are many reasons why someone would want to take a data structures and algorithms course. But no matter the reason, most people want an inexpensive course.

Taking computer science courses at a college or university can be expensive. That’s why online data structures and algorithms courses are so popular.

Often, these courses are free or low-cost. However, some are every bit as expensive as taking a class from a college.

Since affordability is so important, it makes sense to make it the biggest factor we use to rank these courses.

Our methodology also takes course ratings into account.

Course ratings can offer you insights into the quality of a class from students that have already taken it.

While many people leave legitimate and fair course ratings, some are inaccurate. For example, a disgruntled student might leave a harsh critique. At the same time, a student that’s eager to please might give a course a rating higher than is deserved.

This being the case, course ratings should be viewed with a critical eye. Nevertheless, they can still be informative about a course’s potential value.

The third component of our methodology is the flexibility of the learning experience.

Some data structures and algorithms online courses are self-paced. This gives you the greatest flexibility for completing your studies. Others are instructor-led, which can have fairly strict schedules. Since many online learners have busy schedules, a flexible class is often preferred.

This is just a glimpse at each of the criteria used in our methodology. Let’s take a closer look at each one.

### Affordability

Most people want a good deal when looking for the best data structures and algorithms courses. As such, we’ve put affordability as the most important factor in our rankings.

The value a course gives you should be maximized. For many students, this often means looking for free online courses. You’ll find that most of the courses in our rankings are, in fact, free.

But just because a class is free doesn’t mean you should take it. As you’ll see in the rankings, some low-cost courses could provide you with a higher value than the free ones.

The cost of a course is just one factor you need to look at. But since affordability is the number one priority for many students, we’ve placed higher importance on free courses.

As you can see below, free courses are worth the most points in our rankings:

- Free: 6 points
- Under $10: 5 points
- $11-20: 4 points
- $21-30: 3 points
- $31-40: 2 points
- $41 and above: 1 point

### Course Ratings

The second part of our methodology is course ratings.

As noted above, course ratings can be insightful for you if you look at them with a critical eye.

For example, if a course is new with relatively few ratings, one poor rating could drag down the course’s overall score. So, even though the course might be really good, its ratings might not show that at first.

This is why it’s important to look at two parts of a course’s ratings. First, you should see how many ratings there are. The more ratings there are, the better those ratings reflect the actual quality of the course.

Second, you should read comments or testimonials that come with the ratings. This helps you understand the specific parts of the class that were the best and worst.

Not all courses have ratings. If a course lacks ratings, you shouldn’t disqualify it from being on your list. Instead, you’ll need to rely more on the other two criteria we’ve used to rank these courses.

This criterion is scored as follows:

- 5 stars: 5 points
- 4.5+ stars: 4 points
- 4+ stars: 3 points
- 3.5+ stars: 2 points
- 3.4 stars and below: 1 point
- No ratings: 0 points

### Flexibility

The final part of our methodology is flexibility.

For many online learners, having an open-ended class is best. This allows you to study when you can, not when the teacher has scheduled you to.

Likewise, things often come up in life that require your attention. By having a flexible class, you can address problems that arise without being penalized in class.

This factor is scored as follows:

- Highly flexible: 2 points
- Moderate flexibility: 1 point
- Low flexibility: 0 points

The flexibility a course provides shouldn’t be the only factor you look at. Instead, take all three factors into account. This helps create a complete picture of the value of a course.

Below is our list of the ten best online courses for data structures and algorithms. As you’ll see, each course is scored using these three criteria. Each course is also assigned a point value, which is used to determine its position in the rankings.

Let’s explore each course in more detail.

## Ranking the Best Online Courses for Data Structures and Algorithms

### #10: Easy to Advanced Data Structures

This intermediate course from Udemy dives deep into data structures. Its focus is on how you can effectively use data structures to create both efficient algorithms and quality software.

If you wish to be a programmer, having experience in designing good data structures is a must. This course focuses your attention on the primary skills and knowledge you need to master the use of data structures.

Throughout this class, you’ll learn:

- Algorithms used with data structures
- Dynamic arrays
- Singly and doubly linked lists

You’ll also explore queues and stacks. There is a unit on binary trees and binary search trees as well.

This is one of the most expensive courses on this list, but at less than $200, it’s still significantly cheaper than taking a college course. And since you get lifetime access to the class, you can take as long as you need to work through each lesson.

**Points**: 6

**Affordability**: $199.99

**Course Rating**: 4.3 out of 5 stars (based on 6,538 ratings)

**Flexibility**: Highly flexible. As noted above, you get lifetime access to the course. It is self-paced with many resources to assist your learning too.

### #9: From 0 to 1: Data Structures & Algorithms in Java

Since data structures and algorithms are easiest to learn visually, this beginner course from Udemy is animated. Having a lot of visual aids will help you gain an understanding of how data structures work. You’ll also find that it’s easier to understand how to use algorithms to examine data.

Specific topics you’ll study in this class include:

- Heaps
- Sorting
- Linked lists
- Stacks

You’ll also go over Big-O notation and complexity. There are modules in queues, searching, and trees as well.

As a basic course, this class is intended for anyone that wants to learn data structures and algorithms in a new way. This includes beginners to experienced computer programmers.

**Points**: 6

**Affordability**: $99.99

**Course Rating**: 4.4 out of 5 stars (based on 860 ratings)

**Flexibility**: Highly flexible. This is a self-paced course with access on mobile devices. You also get access to the course for life, so you can work as slowly as you need.

## #8: Foundations of Data Structures

This is an intermediate course provided by edX. It is one of a series of courses in edX’s Xseries on Fundamentals of Computer Science.

The class begins with an overview of basic data types. But as time passes, you progress to more complex topics, including how to organize and manage efficient data structures.

Specific topics of study include:

- Mathematical properties of sequences
- Special types of sequences like stacks and queues
- Trees
- Sets and maps

You can also expect to learn how to implement sequence types, like arrays and linked lists. There’s also a unit of study on graphs.

Since this is an intermediate class, it’s best to already have an understanding of basic programming concepts. If you do not possess this knowledge already, you can take the Programming Basics course.

**Points**: 8

**Affordability**: Free. A verified certificate is available for $149.

**Course Rating**: None

**Flexibility**: Highly flexible. This is a self-paced course. Learn data structures concepts as quickly or as slowly as you need.

## #7: Algorithms and Data Structures in C#

Developed by Microsoft and offered by edX, this intermediate course focuses on using data structures and algorithms in C#.

Not only will you learn how common algorithms work, but you’ll implement them for sorting and searching data in data structures.

Additionally, you’ll learn how to:

- Use simple and complex data structures in C#
- Implement various data structures in C#
- Implement sort and search algorithms in C#

Furthermore, you’ll explore how C# gives you a variety of data structures that range from simple to highly complex. By the end of the course, you’ll have the basic skills needed to take more advanced classes on this topic.

You’ll also be provided with examples that help you understand the logic underlying algorithms.

**Points**: 8

**Affordability**: Free. A verified certificate is available for $99.

**Course Rating**: None

**Flexibility**: Highly flexible. This course is structured to be learn-at-your-own-pace. It’s a short course too – just three weeks – so you can complete it quickly if need be.

### #6: The Coding Interview Bootcamp: Algorithms + Data Structures

One of the most stressful events for coders is their first job interview. Many times, these interviews include tricky questions about data structures and algorithms. That’s where this class comes in.

This video-based course from Udemy is a beginner class that gives you insights into common interview questions. This includes topics that cover:

- String reversals
- Palindromes
- Integer reversals
- Array chunking

Likewise, there are modules in this course that discuss anagrams and max chars.

To aid your learning, the instructor has provided many diagrams and a large collection of algorithm questions for you to consider. You also get straightforward strategies for successfully completing systems design problems.

**Points**: 10

**Affordability**: $18.99

**Course Rating**: 4.7 out of 5 stars (based on 8,995 ratings)

**Flexibility**: Highly flexible. The course is available on mobile devices and TV. You get lifetime access so you can work at your own pace. There are also many videos and articles to assist in your learning.

### #5: Data Structures Concepts & Singly Linked List Implementation

This free course from Udemy is designed for beginners. The class aims to help you understand basic data structures. You will learn Queue, Linked List, and Stack. Each of these structures is taught using the C programming language.

In addition to teaching you how these data structures work, this course dives into:

- How to implement Linked operations
- How to visualize different data structures
- How to implement singly Linked lists

Since this is a beginner course, you will gain essential knowledge to build more skills with regard to using data structures. Most students that take this course have just learned programming and are looking for additional skills.

There are nearly 30 different lectures in this class. Each is very short and easy to digest. This also means you can complete a lesson even if you only have a few minutes at a time.

**Points**: 11

**Affordability**: Free

**Course Rating**: 4.2 out of 5 stars (based on 1,183 ratings)

**Flexibility**: Highly flexible. You can work at your own pace. Additionally, each lesson is quite short and can be completed in just a few minutes. This helps you finish coursework as you can do it in between other tasks or while on the go.

### #4: Data Structures and Algorithms

This intermediate course is offered by the University of San Diego and Coursera. It offers a comprehensive look at both theory and practice in using algorithms and data structures.

If you take this course, you’ll learn techniques for solving many different computational problems. You’ll also learn how to implement around 100 coding problems in the programming language of your choosing.

There is a nice mix of theory and practice in this course. This includes exploring topics like:

- Ideas for computational problems
- Common data structures
- Algorithms on graphs

There are two more primary topics as well. You’ll learn how to use algorithms on strings as well as how to develop advanced algorithms.

This is actually a Coursera specialization. You’ll complete six individual courses to earn the specialist designation.

This is a very long course and one of the more expensive ones on the list. However, it offers unparalleled breadth and depth of learning opportunities.

**Points**: 11

**Affordability**: Free to enroll. A fee (usually around $50) might be required. Other costs may be involved with a specialization. Learn more about Coursera pricing. This course is scored as a free course.

**Course Rating**: 4.6 out of 5 stars (based on 11,060 ratings)

**Flexibility**: Moderately flexible. Though this course is 100 percent online and offers flexible deadlines, it is also very long and requires a lot of work. The level of work and the length of the class make it a slightly less flexible course than others.

### #3: Python Data Structures

This course was developed by the University of Michigan. It is available on Coursera.

Python Data Structures is a mixed-level course in which you will explore many different topics over the course of six weeks.

You’ll begin with an introduction to core data structures within Python. After that, you’ll study:

- Lists
- Dictionaries
- Tuples

More specifically, you’ll learn how to use the data structures listed above to perform complicated data analysis.

This is the second of five courses in the Python for Everybody specialization from Coursera. You can take this class as part of the specialization or as a standalone learning experience.

**Points**: 12

**Affordability**: Free to enroll. A small fee (usually around $50) may be required. Other costs may be incurred if you pursue a specialization. Learn more about Coursera pricing.

**Course Rating**: 4.9 out of 5 stars (based on 6,061 ratings)

**Flexibility**: Highly flexible. This class is self-paced and has flexible deadlines to meet your needs. It’s also relatively short, with about 11 hours of work required to finish.

### #2: Algorithms Part I

This course was designed for intermediate learners. It is offered by Princeton University and Coursera.

The focus of this class is on the fundamental information you need to know about data structures and algorithms. What you learn in this class will help you take your skills from intermediate to advanced.

Topics in this course include:

- Scientific performance analysis
- Java implementations
- Basic data structures

You’ll also get experience in sorting and searching algorithms.

This is part one of a two-part course from Princeton. In the second course, your learning is focused on graph-processing algorithms and string-processing algorithms.

This class can be completed over the course of six weeks. It is self-paced, so you can take longer if need be.

**Points**: 12

**Affordability**: Free to enroll. A small fee (usually around $50) may be required. Other costs may be incurred if you pursue a specialization. Learn more about Coursera pricing.

**Course Rating**: 4.9 out of 5 stars (based on 6,061 ratings)

**Flexibility**: Highly flexible. This is a relatively long class with a suggested six-week timeline for completion. However, with flexible deadlines and self-paced learning, you can complete the course faster or more slowly as needed.

### #1: Algorithms Part II

In the follow up to Algorithms Part I, this intermediate course from Princeton and Coursera explores more advanced topics.

In particular, you’ll learn how to use graph-processing algorithms. You’ll also learn about string-processing algorithms.

Other topics of study include:

- Undirected graphs
- Directed graphs
- Minimum spanning trees
- Shortest paths

This course also examines maximum flows and minimum cuts. This includes using the Ford-Fulkerson algorithm and analyzing its correctness.

Like the first part of this course, the second part is divided into six lessons with one lesson per week. With self-pacing, you can complete the course faster or slower, if you like.

**Points**: 13

**Affordability**: Free to enroll. A small fee (usually around $50) may be required. Other costs may be incurred if you pursue a specialization. Learn more about Coursera pricing.

**Course Rating**: 5 out of 5 stars (based on 1,021 ratings)

**Flexibility**: Highly flexible. This is another relatively long class. There is a suggested six-week timeline for completion. Since it is self-paced and offers flexible deadlines, it gives you greater flexibility to finish the class as you find the time.

## Frequently Asked Questions

**How long does it take to learn data structures and algorithms?**

Learning data structures and algorithms is a process. It is a long process!

You can begin to learn basic concepts in a matter of a few hours of study. However, to become a “master” of data structures and algorithms will likely take months and months of studying and practicing. It’s like learning anything else – you need to put in the time and effort to learn these concepts.

How much time that entails depends on the situation.

For example, if you’re a college student and go to school full-time, you can expect to have a good degree of understanding after one year of studies. After two years of studies, you’ll have that much more skill. If you continue your education and get a four-year degree, your skillset will be even larger.

So, in this example, there are two variables. First, are you a full-time student or part-time? Obviously, the more time you have to dedicate to your studies, the faster you can master data structures and algorithms.

Second, to what degree do you want or need to learn these concepts? If you’re preparing for a beginner computer applications exam, you need a lower level of understanding than if you’re applying for a computer programming job. The purpose for which you wish to learn data structures and algorithms will influence how quickly you can master these topics.

Another factor that impacts how long it takes to learn data structures and algorithms is your prior knowledge. If you’ve never taken a computer programming course before, your road to learning these concepts will be much longer than someone that has an associate’s degree in programming. Likewise, if you have some experience with Java, Python, and other programming languages, the timeline for learning these topics will be shortened.

Of course, the availability of resources will affect your timeline of learning too. The data structures and algorithms online courses listed above are excellent resources for beginning or continuing your journey of mastering these topics.

And since these are among the best data structures courses and the best algorithms courses, you know that the quality of learning is above what you might find elsewhere.

If you look at the courses ranked above, you’ll see that there is wide variability in their length. Some courses require just a few weeks of work to complete the lessons. Others require months of work, with many hours put in each week. So, while the availability of resources impacts your timeline of learning, so too does the length of the courses you take.

**Which is more important – data structures or algorithms?**

Try not to compare data structures and algorithms in terms of their importance. In many cases, you use these tools together to accomplish a goal. Though they might not be equal parts all the time, the notion that one is more important than the other isn’t exactly accurate.

Say, for example, that you have a programming problem that needs to be solved. On the one hand, you need a means by which you encode the problem. On the other hand, you need a procedure by which you can solve the problem. Data structures and algorithms are the tools used to accomplish these tasks.

In most situations, you choose which data structure and algorithm to use depending on the problem. So, rather than determining which of these is most important, you instead examine which data structure-algorithm combination will work best.

Since these tools are so frequently used together to accomplish a common goal, learning data structures and algorithms both is your best bet. That way you can tailor which data structures and which algorithms you use to the specific problem at hand.

**Which should I learn first? Data structures or algorithms?**

In the question above, we noted that neither data structures or algorithms should be considered as more important. Instead, they are tools used to accomplish a goal.

The same situation applies to this question. It’s not always a matter of which you should learn first. Instead, you need to learn both.

Think about it like this: you can’t manipulate data with algorithms unless you have data to manipulate. And getting that data stored, organized, and usable requires a data structure. So, they aren’t necessarily separate concepts, but two parts of a larger whole.

Having said that, if your interests lie in sorting and merging and math, study algorithms first. If your interests are in files, networks, and file systems, prioritize data structures first. Do this knowing that you will learn a data structure and then need to learn an algorithm to use it. Then you’ll need to learn another algorithm. Then another data structure.

Such is the case when learning these concepts. It is a situation in which learning both is required.

**What do I need to know before studying data structures and algorithms?**

First and foremost, most computer programmers will tell you that you need to know a programming language before studying data structures and algorithms.

Whether you prefer C, C++, Python, Java or some other language, having an understanding of how to write code in that language is hugely beneficial. When learning a programming language, consider the following as important topics to learn:

- Different data types
- How memory is allocated to data types
- Different flow types, like loops and switches
- How to control flows
- How to compile and run a program

These are just a few examples of topics with which you should be familiar.

Likewise, advice is often given to have a good handle of mathematics before starting your studies of algorithms. You don’t need to be a calculus guru, but solid skills in number theory and discrete mathematics are helpful.

It’s also important to consider the task at hand. For example, how is coding done? How are the instructions for the code understood by the computer? What programming language needs to be used?

If you focus your learning on addressing these questions and the topics outlined above, you’ll be in a good position to learn data structures and algorithms.

**Why should I learn data structures and algorithms?**

At a basic level, data structures and algorithms allow computer programmers to take inputs, process them, and provide an output. This is the job description of a computer programmer in a nutshell, and these tasks cannot be accomplished without data structures and algorithms.

Think about it like this…

To make the most of this input-process-output sequence, you need to optimize each of the three steps. The step computer programmers have the most control over is the processing step. It’s here where data structures and algorithms are used.

For example, let’s say someone searches for directions in Google Maps. The input is the address they type in. On the backend, the output is the directions provided by Google to get to that address. In between that is all the processing that must take place for the input to result in the output.

Data structures are used to organize the information in the processing step. The better organized that information is, the faster processing will be. Likewise, algorithms are needed to manipulate the data that’s being processed. This being the case, both data structures and algorithms are important to study as without them, carrying out programming tasks can be time-consuming and cumbersome.

**What are some applications of data structures?**

Data structures play an important role in many different aspects of computer applications.

For example, linked lists, which are very simple data structures, are used everywhere. The history in your web browser is a prime example. The list of websites you’ve visited is stored and updated as you view different sites. Then, when you press the back button or check the browser history, each website is listed.

As another example, stacks are used in word processing.

Let’s say you are typing a passage in a word processing document and decide you don’t like how it reads. When you push the undo button, the program reverts to the last version in the stack of changes. By keeping those versions in a stack, you can quickly undo operations.

Queues are another common type of data structure.

A queue in computer applications means the same thing as it does in real life. So, when there’s a long line at the grocery store, you’re in a queue. In the world of computer applications, CPU scheduling, disk scheduling, and IO buffers are all examples of queues. In these instances, resources are shared between multiple users or data is received at a different rate than it was sent. In both cases, a queue exists.

There are also deque data structures. These are also known as double-ended queues.

Basically, a double-ended queue has a front end and a back end, and the items in the queue stay in their position. Prime examples of this are the undo and redo functions in a word processor. For example, if you hit the redo button, the item at the top of the queue is triggered.

Trees are another kind of data structure that is often used in computer applications. A simple example of a tree is the folder structure on your computer. There are main folders, each of which might have multiple subfolders nested within. Those subfolders might have additional folders nested within them, too.

As another example, HTML documents use trees. This means that the text and image attributes of a webpage are organized in a tree structure. This structure is called a Document Object Model (DOM).

Yet another example of a tree data structure is the autocorrect function on your phone. When a word is typed, the function searches through its series of “branches” to suggest the most appropriate autocorrect option.

Heaps are special tree data structures. These structures are binary in form, and most often can be one of two types.

The first type is a max-heap. A max-heap occurs when the root value is greater than any of the other values in the tree. The same must be true for all the sub-trees in the tree. So, if the root value is 100, none of the values below it in the tree can be greater than that.

In a min-heap, the root value must be the smallest value in the entire tree. Again, the same must be true for all the sub-trees in the tree. So, if the root value is 5, no other values in the tree can be smaller than that.

**Why are algorithms important for computer programming?**

Like with data structures, there are many different types of algorithms that computer programmers utilize. The common thread is that algorithms provide you with a step-by-step process for completing a task.

Algorithms also optimize the efficiency of a computer program. For example, there are many different ways a computer program can carry out a task or solve a problem. However, each method varies in its efficiency in accomplishing its task. Algorithms are used to determine which of the possible methods that can be used will be the most efficient.

Algorithms are also important because they help manage resources. An example of this is computer memory. Some programs use more memory than others. An algorithm is used to determine how much memory each program can use. Furthermore, algorithms are used to optimize the amount of memory a program uses so that it draws on the least amount of memory needed.

**What should I study about data structures and algorithms for an interview?**

Interviews for programming jobs always have questions or tasks related to data structures and algorithms.

These questions often put you in a position to explain how you would navigate a data structure or use an algorithm. For example, you might be asked to explain how you sort a linked list or traverse a binary tree.

But there are many, many different topics related to data structures and algorithms. When studying for an interview, you can’t possibly remember every possible problem or question that might come your way.

So, what many programmers will tell you is that you should study the highest-value topics. This includes core concepts such as:

- Linked lists
- Trees
- Hash tables
- Sorting and searching
- Graphs
- Arrays
- Strings

You should understand how to implement each of these tools in whatever programming language you typically use.

Of course, the Coding Interview Bootcamp, which is ranked sixth on our list, is a great resource for focusing your attention on the most important topics.

OCR Staff

March 2020

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