Online Course For Computer Science – This is CS50x, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors, with or without prior programming experience.
An introductory course taught by David J. Malan, CS50x teaches students how to think algorithms and solve problems efficiently.
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This free online course from Harvard University: Introduction to Computer Science is for anyone of any age who wants to learn computer science and master some skills.
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JOIN OUR COMMONWEALTH TO GET MORE HELP FROM THE PROFESSIONALS AND IMPROVE YOUR WAY AROUND THE WORLD Many universities make their curricula available to the public and list all the courses required to obtain a degree. The computer science field is no different. Using such readily available resources (see MIT (English), JMU (German) and KIT (German) as a starting point), a customized timetable can be created.
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This post attempts to recreate a CS Bachelor’s degree, but with online resources. Some of them are available for free, others will cost you a small fee. All in all, they are a cheap alternative to learning things similar to those taught in universities. Note: With the resources I used, I generally tried to follow the referenced curricula. It is not always possible; being enrolled in a university course obviously gives you access to a wider range of lectures.
The first courses and semesters naturally focus on the basics, many courses at this level are compulsory. After these commitments are met, you are free to choose from a range of lectures. That is where one can choose a path towards software engineering, logic or machine learning, which is the focus of this post. Before we go over the courses in detail, here is an overview of the curriculum:
Curriculum overview, image by author. The list is available on Notion here and on GitHub here.
The first semester consists of introductory courses only. You build your basic knowledge in mathematics, logic, algorithms and data processing (often abbreviated as ADS) and programming.
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“Algorithms and Data Structures” focuses on, well, algorithms and data structures. The first part, Algorithms, introduces you to sorting, which includes QuickSort, MergeSort, and many others. Furthermore, you will learn to examine the runtime of such algorithms based on their input, which is done with the help of Big-O notation.
These structures are used to store data so that relevant information can be found or updated quickly. The structures used are red-black trees, binary search trees and graphs. Of course, you will also learn how to traverse such structures. A course that teaches you such topics is the Data Structures and Algorithms specialization, available on Coursera.
This lecture helps you understand the basics of mathematics used in computer science. Mainly it is about matrix calculations, derivatives and functions. A 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 wants you to get practical experience in programming. Since this entry is on the way to machine learning, it is subsequently to choose
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As the language. I like the simplicity of Python – after you’ve completed the Python for Everybody specialization, you’ll know what I mean.
The logic lecture covers relational algebra, deriving facts from knowledge, negation, and further concepts of mathematical proof.
Such topics may sound boring, but learning them will help you outside of your studies as well. I found the Introduction to Logic course the best – as with the previous courses, it won’t always be 100% covered. You can revise the course, which gives you free access to the resources, but not to the graded tests – that’s fair!
That’s it for the first semester. In the second semester, the emphasis continues on the basics. You start by studying statistics and probability to deepen your mathematical understanding. When this is checked, you will hear about software engineering and computer basics.
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Working as a data scientist or similar means working with data. And working with data involves exploration. Obviously, you can’t just go through 1000 photos at once. It is not necessary, we can do this with the help of statistics. Check the average form? Possible. List average? Possible. Math comes in handy here. Probability and statistics: To p or not to p? course is well suited for this. That and the title suggest humor.
With our math skills we can move on to more computer related topics. Software engineering teaches concepts about, well, software engineering: Creating software, making it adaptable, making it fast, the basics of clean code, inheritance, and patterns. There is a lot happening in this field, the software design and architecture specialization course teaches you more about UML, OOP and design.
This lecture covers the components of (modern) PCs. I had a hard time finding a lecture that covered topics equivalent to my undergraduate studies. In the end I settled on Computers and the online course from Khan Academy. If you are looking for a book, you can read Computing with Quantum Cats, which also covers the history of computers.
It’s the second semester, the third is already coming. It covers signaling, advanced programming and basic courses.
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This lecture is about getting digital data from A to B. But what if the transmission leaks and we are losing information? Or is it background noise? Well, there are techniques like filters or encoding that solve this. Take a digital signal processing specialization to learn more.
The second programming lecture will build on the first but provide a more detailed discussion of python. The Python 3 programming specialization is the next step after Python for Everybody.
The syllabi I looked at for reference included such seminars. As long as you are choosing subjects from your field, CS & ML, then everything is fine.
When you visit a university, you have to cover a topic based on one or more given subjects. You then write an analysis in this area and present your findings in a short discussion.
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This setting is difficult to replicate with online-only courses. There are two solutions to overcome this: You can find like-minded people and talk about a field of your choice, or you can go through yet another course. The last one is my preferred solution, and since this repeated degree focuses on machine learning, it follows Andrew Ng’s CS230 Deep Learning course from Stanford. This course provides an overview of the field, presented by one of its most prominent researchers.
This is not required, you can see it as a bonus: Hearing a lecture from another field. I recommend choosing biology to expand your knowledge. You can also choose economics, physics, chemistry, whatever you are curious about.
The fourth semester begins with a hard phase: theoretical computer science. I remember hearing this lecture with many like-minded colleagues. It was a difficult lecture, we had a lot of respect for this subject. It covers computation, linguistics (which is not necessarily human language), determinism, convergence and complexity.
It was challenging to find an online course that covers most of this, in the end I settled on Intro to theoretical Computer Science, and
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How do you find the shortest route between A and B? With a model as a graph, we can take the shortest subpaths between A and B. But graphs are present in such (theoretical) problems, social networks (who follows who), molecular science and other 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 wants you to make something. It doesn’t have to be something ridiculously complicated. I recommend a simple script that you run on a 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.
Similar to last semester, look further
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