Computer Science Bachelor Degree Online – Many universities make their courses public and list all courses required to earn a degree. The field of computer science is no exception. Using such free resources (see MIT (English), JMU (German), and KIT (German) as starting points), it is possible to create custom schedules.
This post attempts to recreate the BS in CS, but using online resources. Some of these are available for free, while others require a small fee. Overall, they’re an inexpensive alternative to learning something similar to what you’re taught in college. One note: In the case of using resources, I usually try to stick to the reference class. This isn’t always possible; taking a university course will obviously get you a wider range of lectures.
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Of course, the first courses and semesters focus on the basics, and many courses at this stage are compulsory. After fulfilling these obligations, you are free to choose a series of lectures. Here you can choose a track leading to software engineering, logic or machine learning, which is the focus of this article. Before we go into the details of the course, here is an overview of the course:
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Course overview, image courtesy of the author. The list is available on Concepts here and on GitHub here.
The first semester consists of introductory courses only. You’ll build a foundational knowledge of mathematics, logic, algorithms and data structures (often abbreviated ADS), as well as programming.
“Algorithms and Data Structures” focuses on algorithms and data structures. The first part, Algorithms, introduces you to sorting, which includes quick sort, merge sort, and more. Additionally, you will learn to investigate the running time of such algorithms on the input, which is done using Big O notation.
These structures are used to store data so that relevant information can be quickly found or updated. Structures used include red-black trees, binary search trees, and graphs. Of course, you will also learn how to traverse such structures. One course that teaches such topics is the Data Structures and Algorithms specialization, available on Coursera.
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This lecture will help you understand the fundamentals of mathematics used in computer science. Mainly about matrix calculations, derivatives and functions. One course that covers these topics is the Mathematics for ML specialization offered by Imperial College London. You start with linear algebra, move on to optimizing functions, and finally learn how to compress high-dimensional data.
This lecture expects you to gain hands-on experience with programming. Since this article is about machine learning, choose
Favorite language. I love the simplicity of Python—you’ll see what I mean when you complete the Python for Everybody specialization.
Logic lectures focus on relational algebra, deriving facts from knowledge, negation, and other concepts of mathematical proof.
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These subjects may sound boring, but studying them will also help you beyond your studies. I found the introductory logic course to be the best fit – not always 100% coverage like the previous course. You can view courses, which gives you free access to resources, but not graded exams – a fair deal!
That’s it for the first semester. The second semester continues to focus on the basics. You start by studying statistics and probability theory to deepen your understanding of mathematics. After ticking this, you’ll learn about software engineering and computer fundamentals.
Working as a data scientist or similar involves working with data. Working with data includes exploration. Obviously, you can’t browse through 1000 images at once. This is not required, we can use statistics to do this. Check the average shape? possible. The point of the list? possible. Math comes in handy here. Probability and Statistics: To p or not to p? The course is perfect for this. That, and the title conveys the humor.
With our mathematical skills, we can move on to learn more computer related things. Software Engineering teaches the concepts of software engineering: the basics of creating software, making it customizable, making it fast, clean code, inheritance, and patterns. There’s a lot going on in this field, and the Software Design and Architecture specialization will teach you more about UML, OOP, and design.
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This lecture covers the components of (modern) computers. I’m having trouble finding lectures that cover topics that correspond to my undergraduate studies. In the end, I decided on Khan Academy’s Computing and Internet courses. If you’re looking for a book, read Computing with Quantum Cats, which also covers the history of computers.
That’s it for the second semester, and the third semester is already on the way. It includes signaling, advanced programming, and undergraduate seminars.
This talk focuses on getting digital data from A to B. But what if the transmission leaks and we lose information? Or is there background noise? Well, there are some techniques to solve this problem, such as filters or encoding schemes. Switch to the Digital Signal Processing specialization to learn more.
The second programming lecture will build on the first but introduce Python in more depth. The Python 3 Programming Specialization is the next step after Python for Everybody.
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The courses I checked for reference included placeholder seminars like this. As long as you pick a subject from your field, CS & ML, you’re good to go.
When you visit a university, you have to cover a topic based on one or more given essays. You will then conduct an analysis of the field and present your results in a short discussion.
This approach is difficult to replicate in a purely online course. There are two solutions to this problem: you can find a like-minded colleague and discuss your chosen field, or you can study another course. The last one is my preferred solution, and since this repeat exam focuses on machine learning, it’s the result of following Stanford’s Andrew Ng’s CS230 Deep Learning course. This course provides an overview of the field, presented by one of the leading researchers in the field.
This is not required, you can consider it as a bonus: listen to lectures from another field. I suggest you choose Biology to broaden your knowledge. You can also choose economics, physics, chemistry, whatever interests you.
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The fourth semester starts with a tough course: Theoretical Computer Science. I remember listening to this lecture with many like-minded people. This is a tough lecture, and we have a lot of respect for the subject. It covers computability, language theory (not necessarily human languages), determinism, convergence and complexity theory.
It was challenging to find an online course that covered most of it, I ended up taking Introduction to Theoretical Computer Science, and
How to find the shortest route between A and B? Modeled as a graph, we can take the shortest subpath between A and B. But graphs exist in such (theoretical) problems, social networks (who follows whom), molecular theory, and many more fields where they are often used. The following two courses will teach you more about them: Graph Theory and Introduction to Graph Theory.
This course expects you to create something. It doesn’t have to be any ridiculously complicated. I recommend a simple script that runs on the web server. Check out the gallery on streamlit.io and get inspired. It’s not so much about creating a great product as it is about learning something new.
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Similar to last semester, look outside your area. If you listened to a biology lecture last time, listen to a chemistry class now. Or physics, economics, medicine, mathematics.
In previous lectures, we heard about the components that make up a computer. We’ll expand on this and shift the focus to interacting with lower-level hardware. Check out the Embedded Software and Hardware Architecture course to learn more about low-level firmware. If you have a passion for inventing new components, a course describing hardware might be a good fit. In that case, check out the Hardware Description Languages course for FPGA Design.
Using a computer is easy now. Effectively using your toolset is another story. When I first started programming with PyCharm, I often consulted the documentation to find keyboard shortcuts. With them in mind, programming is much easier. Knowledge of programs goes beyond the IDE and extends to using the command line, working with text editors, and rights management. You don’t want to spend your life figuring out tools without creating something with them. That’s what your CS education and Operating Systems and Your Missing Semester: Become a Power User course is about.
Instead of storing your data in named folders and watching the chaos grow, you can use a database as an alternative. Find the average number of images? Find all users who gave 5 stars? Through the query language, the database can easily extract useful information. And since SQL is the de-facto standard language for interacting with databases, all major programming languages have interfaces to interact with it. Since python is one of them, it also provides packages that hide all the complexity. everything left for you
