10 Top Computer Science Courses to Take in 2022

 10 Top Computer Science Courses to Take in 2022

Looking for the best introductions to computer science? I have ranked the best courses available online, following a strong methodology. They are all free to audit. You can read about them below.

What is computer science?

The definition of computer science is almost as broad as that of physics. Therefore, to say that computer science is to study computer concepts and computing is just as "useful" as saying that physics is the study of nature and its phenomena.

Instead, I'll tell you about the major computer science sub-fields that most universities include in their syllabus.

• Computer architecture and organization naively wonder: "How do I design a computer?"

• Programming steps and questions: "But how will the computer understand the human?"

• Operating systems interjects: "Hold on, how should a human interaction with the computer?"

• Data structures and algorithms chirp in: "After you've figured that out, how do we store and compute data efficiently?"

• Networks and communications wait politely before inquiring: "That's great, but how do we make computers to talk to each other?"

You get the gist. I'm sure you have one of these intriguing ideas popping up in your mind before. Fortunately, these are the questions computer science is trying to answer.

By studying computer science, you can become a better programmer. Just as the veterinarian is likely to understand animals better than the average pet owner, by studying computer science, you can get a better understanding of the features, abilities and limitations of these wonderful blade-powered machines that we call "computers."

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Course Ranking methodology

I have followed a three-step process to build this ranking:

First, we started building this ranking by looking at our database of 50,000+ online training courses. We were interested in things like evaluations, reviews and course bookmarks. This allowed us to make the initial choice. So this phase was driven only by data.

This temporary first step helped highlight some of the best options available quickly. Oral speech is very effective in online learning. Good courses are observed. The best brings a lot of attention and rave reviews.

However, the reviews do not always tell the whole story. In fact, some training courses are so good at highlighting early that other excellent resources can go unnoticed. So the next step was to integrate our personal knowledge of online education into the mix.

Second, we used our experience as online learners to evaluate each of our initial choices.

Thirdly, during our research, we encountered well-made training courses but they were not well known. If we adopt a data-only approach, we will have to leave these courses out of the rankings, just because the number of registrations and ratings has less.

But no. This ranking is deliberate and comprehensive. When we felt confident that the course was worth including, even when the course did not yet contain as many reviews as some of its competitors, we went with our intuition and included it.

We have also improved the list by including a variety of computer science courses that we hope will meet a variety of learners, whether you're a real beginner or someone who has some foundations in computer science or is interested in specific topics such as mathematics.

Course ranking statistics

Here are some overall statistics on the ranking:

• In total, the courses in this arrangement accumulated more than 5 million registrations with two courses with more than 1 million registrations each.

• The most popular course on the list has 3.5 million registrants.

• All courses in this arrangement are either completely free or free to check.

• With 4 cycles each, edX and Coursera have been linked to the most representative provider in this ranking.

Without further ado, let's move on to the best choices.

1. CS50's Introduction to Computer Science (Harvard University)

My first choice should be the CS50 introduction to computer science, provided by Harvard University at edX. CS50 was launched at edX in 2012, an online computer science course. It is renowned for its outstanding production quality and annual curriculum updates.

Provides a brief and comprehensive overview of what computer science is about. Whether you're a beginner who's never heard of "Hello World!" before, or a programmer who knows a thing or two about computers, you'll get out of this course after learning something new.

One Thing to Note

Although the course exercises come in two versions, easy and difficult, I found that even easy exercises can be a bit difficult. If you don't know anything about programming, I recommend you find someone to study this course with.

Fortunately, CS50 has one of the largest and most active online course communities: check out their Discord.

What You’ll Learn

The course begins with the premise that computer science is, in essence, problem-solving. The binary system presents you with the basic language of computers and explains how sequences of 1 and 0 can somehow represent text, photos, videos and even sounds.

You'll learn that algorithms are step-by-step instructions designed to solve a problem. The most common types of algorithms you will deal with during the course are sorting and searching algorithms, such as bubble sorting, sorting integration, and binary search.

You might wonder, "What's the point of having so many different algorithms if they're all doing the same thing?" This is when you'll learn how to measure algorithm efficiency using Big O notation.

The first programming language the course teaches is scratch, which is beginner-friendly. Through block-based coding, Scratch will be used to illustrate basic programming concepts such as functions, conditional phrases, logical expressions, loops, and variables.

Later in the course, you'll notice that these basic concepts continue to appear over and over again, as they can be found in almost every programming language CS50 will teach you.

The course then removes your training wheels and drags you deep into low-level programming languages. With the words "low level", I do not mean "less valuable". In computer science, low-level programming languages are languages close to the machine code: the closer you get to the machine code, the less "less."

The assembly language is as close as it is to the duo, and the session will discuss it briefly. But our first deep dive into traditional programming (writing lines of code instead of arranging coloured blocks like Scratch) will be using C, a low-level programming language where a memory will be manually managed and the first data structures implemented.

You'll learn that computers store data in a sequence of sites in memory, and how computers can locate and access data using addresses and indicators. You'll also learn about the different ways in which we can create and store value lists, such as arrays, linked menus, and trees.

You will compare the advantages and disadvantages of each data structure. For example, retail tables can be accessed at a fixed time, but require mitigating the risk of data conflicts.

You'll then be brought back to the surface towards "top-level" programming, where you'll be able to breathe comfortably when you start working with Python and continue to jump from topic to topic.

SQL, the programming language of many databases, will explore. The final weeks of the course end with the construction and design of an interactive website using HTML, CSS, JavaScript, and a Python framework called Flask.

How You’ll Learn

The course takes ten weeks, plus an open final project that may take an extra week (or more, if you want to work on something really ambitious).

The course is registered annually on the Harvard campus before being launched online the following spring. While recording continues, you may be able to join live with another 100 learners, or if you live near the campus, attend in person - although the epidemic may prevent this for the foreseeable future. Otherwise, you will have access to registrations on request on edX or via Harvard OCW.

Concerning evaluations, you will complete ten sets of problems, eight laboratories, and a final project at the end of the course, which you will have to design and reach on your own or with a team. You'll be able to encode them and send them through a suitable browser editor based on VS Code.

CS50 Lineup

Many people heard about the CS50 introduction to computer science, but many did not realize that there were 10 more courses under the CS50 brand. Some noteworthy follow-up courses are:

• Introduction to Artificial Intelligence using Python

• Introduction to game development

• Web programming with Python and JavaScript

Better yet: Many of these courses offer free certification. If you want to learn more about CS50 courses and how to get a free certificate, you can read Manoel's CS50 guide.

Fun Facts

• Course teacher David J. Malan taught CS50 for 15 years, for the first time on campus at Harvard University and at edX since 2012.

• CS50 has been bookmarked about 30,000 times and has more than 100 reviews on Class Central.

• Every year, CS50 organizes Puzzle Day, a problem-solving friendly competition where you will have the opportunity to collaborate with learners around the world.

• CS50 is part of the list of the most famous courses ever and the best free training courses ever.

• David J. Malan is the founder and president of Diskaster, a hard drive data recovery company and memory card. One of the training exercises is a reference to his previous work.

• CS50 is the longest cycle in this order, due to its coverage.

If you are interested in this course, you can find more information about the course and how to register here.

2. Computational Thinking for Problem Solving (University of Pennsylvania)

My second pick would be to think about problem-solving arithmetic from the University of Pennsylvania at Corcera.

This course focuses on basic computer science skills - computational thinking.

Computer thinking is the process of dividing the problem into parts, then finding a solution method that can be implemented by the computer.

Once you adopt computational thinking, you'll be in the right mindset to deal with additional computer science courses. So you can see this course as the basis before you establish it. However, if your interest lies in solving problems per se rather than computer science as a whole, this course should also be appropriate.

You don't need any previous experience in computer science or programming to take this course, although some basic high school math will be useful.

What You’ll Learn

The course covers four main topics: computational thinking, algorithms, computer engineering, and Python.

First, the course illustrates the four pillars of computational thinking. It will start to decompose, dividing the complex problem into smaller and simpler problems. Then by identifying patterns, the problem will be compared to other similar problems that have already been resolved.

Then, while representing and stripping data, you will simplify the problem further by identifying the characteristics of the important problem and filtering out those that are not important.

The last corner of computational thinking columns, algorithms, forms the second section of the decision. The course defines algorithms as a step-by-step set of instructions to solve a problem. Using algorithms, you can teach your computer how to solve problems without telling them how clearly. Instead, your algorithm will be able to handle some different situations, as long as it meets certain preconditions.

You will explore a variety of algorithms, such as linear and binary research. You'll learn how to represent algorithms using streamlined charts, analyze the complexity of algorithms (Big O), and calculate how many possible solutions to the optimization problem. Finally, you will compare the advantages and limitations of common problem-solving calculation methods.

The third part of the course provides a brief overview of the history of computers, before settling on the computer structure used by modern computers - the engineering of von Neumann. 

It consists of three basic units: memory, CPU, and input/output. You'll learn how data and instructions are stored and accessed in computers in a bit and byte units, as well as how code execution reaches mobile parts of data in memory and runs in the CPU.

In the fourth and final section, the course will guide you to python programming basics. You'll explore iterations, chapters, and error correction. You'll finish the course by encoding your Python program, where you'll be able to implement the algorithms you've already learned in code.

How You’ll Learn

The course is 4 weeks long, with each week containing about 18 hours of course materials. You will learn mainly from video lectures, and after each video, there will be a short test to test your memory. There are complementary materials available in mathematics, for those who are not confident in their athletic abilities.

Each weekend, you'll be presented with a case study where you'll see examples of computational thinking used to solve real-life issues. After that, you will complete a project where you will apply what you have learned. Note that the evaluations in this course are for accredited learners.

Fun Facts

• This course was accredited by Google, which decided to make it part of the Google Academy, a collection of training courses and resources for learners wishing to acquire technical skills.

• University of Pennsylvania professor Susan Davidson, a course teacher, was appointed a fellow of the American Association for the Advancement of Science in 2021.

• Professor Davidson also teaches some Computer and Information Technology Master courses (MCIT) in Pennsylvania, which are offered online through Coursera.

If you are interested in this course, you can find more information about the course and how to register here.

3. Introduction to Computer Science and Programming Using Python (Massachusetts Institute of Technology)

My third choice for the best course in computer science is an introduction to computer science and programming using Python, presented by MIT on edX.

This course deals with computer science and programming through Python. The course focuses on breadth rather than depth, giving students basic knowledge about the many applications of the account.

So this course is similar to our first choices in that it's a survey cycle: it covers a lot, but not in great detail. But it's different in that it focuses entirely on a single programming language, Python, while Harvard's course includes multiple languages.

Depending on your goals, this focus on Python can be considered positive or negative. For what it's worth, I think Python is an excellent first programming language.

Attention! This course tries to simulate the MIT experience on campus, so don't expect it to be a picnic. You won't need any previous experience in computer science or programming to take, but you'll need a background in high school math.

What You’ll Learn

The main topics explored by the course are computational thinking, data structures, repetition and repetition, decomposition, abstraction, algorithms and complexity.

You'll get a brief introduction to computational and computational thinking. You'll learn about computers, how they work, and what their limits are.

By understanding that computers only know what they're telling them (and what they can deduce from what you're telling them), you'll realize that for the computer to accomplish a task, they need a "recipe" that contains a series of instructions they do. You must follow. That's what computer scientists call the algorithm.

Start your programming journey by learning Python and its basic installation. Using Python, you'll explore common concepts in most programming languages. These include variables, conditional data, and control flows.

Moreover, you'll learn about the functions and role you play in decomposition, abstraction and repetition, which are key concepts for solving problems in computer science.

By then, you should be able to encode simple programs that can come up with approximate solutions to difficult mathematical equations through the guesswork and verification method.

Finally, you'll learn about the different ways in which we can represent information in Python, called data structures. It will work with lists, groups, and dictionaries, and you'll know when to use one data structure on another.

How You’ll Learn

The course takes 9 weeks with an expected workload of 14 to 16 hours per week. The main way to learn is through video lectures, and the course includes a lot of activities to put hard-earned skills into practice. You will also have access to the Learner Forum where you can discuss with your educated colleagues.

There are three sets of problems that contain difficult encryption exercises that will help you establish your knowledge. If you are a verified learner, you will have to complete the mid-semester and final time-limited test to obtain your degree.

Fun Facts

• This course contains more than 18,000 bookmarks and 120 class Central reviews.

• It's the first two-session XSeries program on edX. The second is an introduction to computational thinking and data science, which may lead to good follow-up.

• One of the trainers, Professor John Gottage, leads the data-based inference group at the legendary MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

If you are interested in this course, you can find more information about the course and how to register here.

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4. Principles of Computing (Part 1) (Rice University)

The Principles of Computing (Part I), prepared by Rice University in Corcera, is the fourth optional for best introduction in computer science. The course emphasizes doing instead of watching, requiring you to complete many coding tasks.

This course aims to help you increase your programming skills by teaching you to solve computational problems, a skill that underlies computer science, and this was also the focus of our second choice. This will include learning important programming practices and developing a mathematical basis for problem-solving.

To take this course, you should be comfortable writing small programs (more than 100 lines) in Python, as well as having some background in high school math. So this doesn't start from scratch and is therefore aimed at learners who also have some basics.

If you are looking for a problem-solving training course with fewer pre-requirements, you may want to take a look at the second choice.

What You’ll Learn

The course includes updates on Python, code testing, probability and randomness, compatibilities, and job growth.

After a brief review of Python, the course will explain how tests are built, and why testing for Python programs can be useful.

Many programmers hate or don't bother writing tests for their code, but as one teacher explains, it's the best practice worth dealing with as an integral part of the programming process.

Writing tests will help you save time and effort, and will serve as a reusable mental examination that your program is already doing what it's supposed to do. For your first mini-project, you'll recreate the famous 2048 game at Python.

The course then moves on to the role of probability and randomness in computer science. You will learn how to determine unreasonable results in probability, as well as calculate the expected value of multiple results.

For example, what is the probability that the dice will die seven times out of every ten throws? If that happens, how far can we conclude that the template is likely - that is, the lists were unfair?

You'll also see how we can use Python to simulate the probability of results, a valuable tool used in statistical modelling. For your second mini-project, you'll work with the odds to create an opponent you can face in the Tic-Tac-Toe game.

The course also addresses combinations, which deal with the census, indentations and combinations. You'll find out how to calculate the total number of ways an event can be implemented.

This greatly helps calculate how many steps the algorithm will take, allowing you to estimate when the algorithm is running, thereby determining whether the algorithm is worth implementing. You can see why harmonics play a key role in a password and computer security. For your third mini-project, you'll be the familiar dice icon Yahtzee.

In the last part of the course, you will learn the importance of counting in solving complex problems. The count answers the question of how long the algorithm may take to work on a particular task. Another name for the count you may be more familiar with is "time complexity."

You'll also learn about python's top-ranking functions, which take other functions such as algorithms, such as map functions. In your latest mini-project, you'll use these concepts to create your own version of Cookie Clicker.

How You’ll Learn

The course is divided into 5 weeks, each week includes 7 to 10 hours of study. You will learn mainly through video lectures and graduated assignments, although the course provides feedback and complementary activities for further reading and practice.

You will write the code for homework and small projects and send it on the accompanying codeSkulptor website, and the code editor in the browser that will pre-empt the need to set up a local encryption environment.

Fun Facts

• The course contains about 15,000 bookmarks in Class Central.

• This course is the third of seven courses that form the basics of computing. Upon receipt of the specialty certificate, you will have completed more than 20 projects, including the Coronation Project.

• If you are not interested in taking a full major after this course, but you would like to know more about the subject of the course, as the name of the course suggests, there is a follow-up course: The Principles of Computing (Part 2).

• Course teacher Professor Scott Rexner is the faculty director of two online degree programmes at Rice University. His dedication to online education goes beyond the huge courses on his internet.

If you are interested in this course, you can find more information about the course and how to register here.

5. Computer Science 101 (Stanford University)

Computer Science 101 aims to demystify the magic of computers by showing that they work by following some relatively simple patterns.

This course will help you identify these patterns. It will give insight into how computers work and what their limits are.

In addition, the course delves into networks and other key topics within computer science. No prior knowledge of computer science is required!

What You’ll Learn

The course begins with the basic equation of computers: computer = strong + stupid. Computers are powerful because they can perform billions of operations per second. But they are stupid because they need someone to tell them what to do. This is where programmers play.

This course uses small javaScript excerpts to introduce you to programming and other computer science concepts. You'll get an understanding of programming concepts such as variables, loops, repeaters, conditional sentences, etc. The course later covers low-level and high-level languages, as well as translators and interpreters.

A computer is a tool and the programmer uses the tool. Therefore, to program efficiently, it is important to understand how the tool works. The course covers many aspects of the tool mentioned, including devices. You'll learn about the parts that make up your computer, and you'll look at how computers represent different information formats.

The main format you're going to do is the pictures. One of the things you'll do is green screen images, as well as turn colour images into grey tones by running at the individual pixel level.

Another topic addressed by the course is computer networks, namely how computers communicate with each other. You'll learn about different network types.

You'll study what IP addresses are and how to allow computers to locate each other. The course discusses how computers transmit information through data packets, as well as the internet-TCP/IP connectivity protocol.

The course also briefly covers a variety of other topics such as databases, spreadsheets, computer security, analog and digital data.

How You’ll Learn

The course takes 6 weeks and each week takes 4-6 hours to complete. Lessons are provided through video lectures and are supplemented by feedback and evaluations. However, you'll need to be a certified learner to access ratings.

Fun Facts

• The teacher acknowledges Google for supporting his early research into classroom creation. I think that applies to all of us!

• This course contains 3,000 class Central bookmarks.

• Course teacher Nick Parlant's focus is on CodingBat Java, an experimental tool for practising online code.

If you are interested in this course, you can find more information about the course and how to register here.

6. How Computers Work (University of London)

This brief course taught by the University of London on Coursera touches on some of the key topics in computer science but is mostly interested in helping you build a basic understanding of devices. It's really in the title: by the end of the course, you'll know how computers work.

With this understanding, you'll also create a clearer picture of how computers can be used to help solve everyday problems.

The course is well suited for those who want to build solid foundations for further study in computer science, as is someone curious about how computers work and wants to explore some of the main computer science topics but not necessarily deep dive.

You don't need any prior knowledge of computer science to take this course.

What You’ll Learn

This course covers computers, abstraction, modular, computer networks and communications.

The course begins with abstraction - the art of attracting attention to important details while filtering noise. Many disciplines rely on abstraction, and computer science does so heavily, both at the hardware and software levels.

This concept will become clear when the session begins to discuss computers, such as memory, CPU and other devices. The national machine will be used as a means of capturing these abstract ideas.

Next, you'll move on to another key idea: status and stereotype. This will help you answer the question, "Why does turning off and running your computer fix most problems?"

Using theoretical machines, you'll explain how computer applications work by moving through different situations, and how modules allow them to interact with other applications. You will learn how to correct errors, a skill that is already very useful.

As you move forward, you'll learn how computers talk to each other online through networks and communication protocols. You'll also learn about the types of security threats computers (and users) face, and how to protect yourself from malicious devices.

Finally, you will explore the development of the basic web. By applying your new knowledge of abstraction, status and stereotype, you'll be able to understand how websites work clearly.

How You’ll Learn

The duration of the course is 4 weeks, with 10 hours of material per week. It consists of video lectures and competitions to test your knowledge of materials. You will have the opportunity to share your thoughts on discussion claims.

Fun Facts

• The course teacher, Professor Marco Gillis, is the academic director of distance education at Goldsmiths, University of London.

• This course is an introduction to the Bachelor of Computer Science online program at the University of London, presented on Coursera.

• It is the second of three introductions in computer science and programming, with the first course provided in computer programming.

If you are interested in this course, you can find more information about the course and how to register here.

7. CS50's Understanding Technology (Harvard University)

This is another course from the CS50 family. But unlike our first choice, the main CS50 course, this course is for those who work with technology every day but don't understand how everything works undercover or how to solve problems when something goes wrong. It is also for those who do not work (yet) with technology - particularly computers - but still want to understand how it works.

The course aims to fill gaps in your knowledge of devices, the Internet, multimedia, programming, web development, and your preparation for today and tomorrow's technology.

This course has no preconditions.

What You’ll Learn

The course begins with an introduction to the language of the binary computer. Explains how computers use the binary system to represent text and other information. Next, you'll move on to computers: CPU, RAM and key memory. You'll learn about the functions of each of these components.

The course discusses the Internet, multimedia and the techniques underlying it. It will tell you how computers can find and talk to each other. You'll learn about TCP/IP and more.

You'll learn about different multimedia data representations, such as audio, photos, and video. There are many file formats and compression techniques - the course will give you an overview of some of the key formats.

After that, you will be taught how to stay safe online. You'll discover several ways to protect your data and privacy. This section will include lessons on cookies, passwords, binary authentication, encryption, and more.

You will continue to basics web development. You'll learn how web browsers access the web with HTTP requests. Have you ever seen a 404 or 500 error when trying to visit a web page? Maybe you have. Well, in this course, you'll learn what these mistakes mean. A brief overview of languages that allows us to build and design web pages, HTML and CSS is provided.

Last but not least, you'll discover the basics of programming. Scratch block-based language will be used primarily to explore concepts common to almost all programming languages, such as variables, expressions, loops, etc.

In addition, to clarify what the algorithm is (and more specifically the divide-and-conquer paradigm), you will see the teacher tear the phone book in half ... I had to mention this because it is so useful and memorable!

How You’ll Learn

The course takes 6 weeks, and each week takes 2 to 6 hours to complete, depending on your previous knowledge of the content. Each week contains at least one hour of lecture.

Concerning evaluations, you will have to complete a task for each of the six topics presented in the course for a certificate.

Fun Facts

• After completing this course, you will be more than ready to deal with CS50, our first choice.

• This course contains 1.6,000 bookmarks in Class Central.

• Another fact about David J. Malan, the course teacher: He is an active member of SIGCSE, ACM's computer science education arm.

If you are interested in this course, you can find more information about the course and how to register here.

8. Intro to Theoretical Computer Science (Udacity)

For those who have some knowledge of programming and algorithms and want to increase their understanding of problem-solving in computer science, this rigorous but insightful course may be what you're looking for.

Introduced by Udacity, the introduction to computer science explores the theory of what makes solving the problem "difficult,"" even for the computer. Next, it explains how to reduce and simplify these "difficult" issues to make it easier to solve through calculation.

What You’ll Learn

The course covers two main areas of theoretical computer science: complexity theory and computing ability.

Complexity theory questions how much computer resources, such as time or memory, a computer will need to solve a problem. On the other hand, computing asks if a computer can solve a problem at all, even when it gives more time and memory.

The course offers you a variety of real-world problems of communication, bioinformatics and finance. You will realize what makes the problem difficult, and the value of identifying such problems. This will help you understand what NP is complete. After that, you will understand what makes solving the problem "difficult" and you will be able to prove it.

The remaining part of the course discusses what to do with the problem once we prove difficult (or even impossible to solve).

One way to overcome this obstacle is to use effective and intelligent algorithms. Another way is to accept that the problem may not be completely resolvable, and instead find an approximate solution. Another way is to use randomness and probability to look for a solution.

You will be able to describe these methods and use them in practical situations: the theoretical session is discussed but also practical.

Finally, you'll move on to problems that no computer can theoretically solve. You will learn about an inability to make decisions and learn about the limits of computing ability.

How You’ll Learn

The duration of the course is 8 weeks, with a total of 14 hours of video lectures. Some videos have a test to help you practice remembering what you've learned. There are 7 chapters, and at the end of each chapter, you will complete a problem set to use your newly discovered skills well.

Finally, there is a final exam at the end of the course.

Fun Facts

• This course contains 2.2,000 bookmarks on Class Central.

• One of the course's trainers, Sebastian Wernick, spoke several times at TED.

• To deal with this cycle, you may want to recognize algorithms first. Trainers recommend another Udacity course on algorithms as a renewal. In addition, the foundations of good mathematics may also be useful. Check out our choices below if necessary.

If you are interested in this course, you can find more information about the course and how to register here.

9. Mathematics for Computer Science (University of London)

This course, provided by the University of London, provides you with information on mathematics and mathematical thinking used by computer scientists in their work. What distinguishes this course from other mathematics courses is its fun and interactive exercises.

More specifically, the course combines elements of algebra, analysis and engineering - carefully selected subjects to serve as the backbone of computer science education.

The rapporteur discusses, among other things, the rules of number, a key theme for understanding the binary system, and the transformation between binary and other rules, such as the sixteenth system. Explores numerical succession, such as the well-known Fibonacci sequence. It will address engineering and functional charts.

By the end of the course, you will have gained the basis for understanding the mathematics that supports other computer science courses and will be ready to deal with more advanced mathematical subjects.

The course assumes that you know some high school math as well as python basic programming.

What You’ll Learn

The course deals with five main topics: numerical rules, stereotypical calculation, sequences, sequences, graphs, and kinetics.

The course begins with a study of the rules of numbers. You may know that binary is the digital basis used by computers. But did you know that computer scientists also use hexagonal?

It will cover basic concepts of place values and number systems, which will include switching between binary, 17th and 10th, as well as collecting, subtracting and beating together. Oh, the great thing the course learned is the science of hiding information, the art of hiding messages in pictures!

After that, you'll cover the typical calculation. Have you ever wondered what "modulo 7" means? You'll learn about the usefulness of match and standard calculations in computer science (psst, can be used for encryption).

Will identify, describe and calculate the sequences of numbers and their total. You will study a special family of sequences called sequences, which consist of mathematical and geometric gradients. You'll learn how sequences can be used to generate random numbers. In addition, you'll be able to see when the chain converges (meets at some point) or diverges (approaching infinity)

Finally, the course shows how space is numerically represented and described using coordinates and graphs. You'll see how charts can help us visualize and transform functions such as straight lines, squares, cubes, reciprocity and more. An example of the modelling movement will be given: a field of mathematics called kinematics.

How You’ll Learn

The course is 6 weeks long, with 40 hours of material. It comes every week with one or more short tests, allowing you to learn through practice. However, you will need to pay for the certificate for the training program for the course to distinguish your answers.

Fun Facts

• It is the third and final course of the introduction to computer science and programming.

• Dr Sarah Santos enjoys maths busking, which seeks to surprise and entertain people on the streets with performances rooted in mathematics.

If you are interested in this course, you can find more information about the course and how to register here.

10. Mathematics for Computer Science: Essential Skills (University of Hull)

If you've taken a look at the previous two sessions but don't have the mathematical foundations to take yet, this course can help you with the basics.

This course is a short course on mathematics skills for computer science, offered by Hull University on FutureLearn.

Dedicated to learners who start or consider studying computer science at the university level, this course covers Venn's blueprints, group theory, algebra techniques, vectors, and arrays - all basic concepts are available everywhere in computer science.

The course does not assume that there is prior mathematical knowledge. You start from scratch.

What You’ll Learn

Starting with Venn's schemes and group theory, you'll learn how to formalize "collections" (bags of things, if you will). You'll learn to think about accounts and account objects. Finn's plans will help you visualize this kind of thinking.

It will then move on to algebra and its techniques. You'll get an overview of algebra (which can be described as a math procedure using variables rather than explicit numbers) and use it in algorithms and scientific calculations. The course will teach you how to solve linear equations and square equations using algebra.

The course ends with an overview of vectors and arrays. You'll learn about routers and why they're particularly important in graphics programming. You'll learn how we can represent vectors as arrays, and how to modify, convert and reverse arrays to solve complex problems.

How You’ll Learn

This course is 3 weeks long, with about 3 hours of material per week. You'll learn mainly through video material, although there are discussion forums where you can discuss issues with your educated colleagues.

Every weekend, there is a test that will help you enhance your understanding of mathematical concepts and applications.

Fun Facts

• The course's coach, Laura Broddle, joined Hull University in 2015 as a maths fellow.

• She also visited a sister school in Uganda and was awarded a landmark teacher rating by Ofsted in 2013.

If you are interested in this course, you can find more information about the course and how to register here.