Suppose you want to learn something new, gain a new skill, apply it to your job, get a position in a brand new field. You open edX or Coursera or YouTube, and you type, “Python Programming”. Tons of courses appear on the result, and it isn’t easy to decide which one is best. Even if you do start the course, there are a lot of barriers to complete it. Maybe the video is too long; the content is too easy, too complicated. So here, I will introduce some of the courses that I found great to work on.
UCSanDiegoX CSE100x Data Structures: An Active Learning Approach (edX)
This is undoubtedly the best course I ever took via MOOC.
This course is on edX, but the actual course content is provided through Stepik. It is entirely free, and since it is an archived course, you can start it anytime and you don’t have to finish it during a required timespan.
The greatest thing about this course is that it takes “An Active Learning Approach”, as it says in the title. The course consists of short youtube videos, short explanations, multiple choice quizzes, and programming exercises with an embedded code editor, which you can code in either C++ or Python. It covers various data structures from array lists, AVL, graphs and trees. It even covers some basics of terminal and git! The videos are also short, very clear and easy to understand. By writing some actual code and solving some multiple-choice questions along the way, you can get hands-on experience learning data structures. You should know some C++ or Python(preferably C++) before you start the course, but only some basic syntax is needed.
2. Deep Neural Networks with PyTorch by IBM (Coursera)
There are tons of machine learning courses at Coursera, including the famous deeplearning.ai.
This course by IBM is excellent because it breaks down the PyTorch code for Neural networks into building blocks. Sometimes, when you listen to a lecture in deep learning, you are just given a chunk of code, but the details aren’t explained. This course by IBM starts with 1D tensors and explains step by step towards deep Neural networks. One great thing about this course is that it provides many Jupyter Notebook examples, and often it gives you a section to write some PyTorch code yourself. At the end of the course, you build a complete deep learning model for an MNIST dataset. Also, downloading the notebooks is a good idea since it gives you the prototype to build a model by yourself. Completing the course, I felt like I finally understood what was actually going on behind deep learning.
This course is not free, but you will get a 7-day trial. Of course, you can always complete the course within a week and cancel the subscription (which is exactly what I did!).
3. Model Thinking by University of Michigan (Coursera)
This might not be necessarily a practical course, but it was pretty interesting, and I thought it is worthwhile to share it. It is taught by Professor Scott Page, who is well-known for his research on how diversity contributes to the group’s productivity.
The course covers various modelling methods in modelling people and society. The course is free, so you don’t have to pay to access the videos or the well-structured quizzes!
4. Responsive Web Design Certification (freeCodeCamp)
https://www.freecodecamp.org/learn/?messages=success%5B0%5D%3Dflash.signin-success
This course covers the CSS and HTML5 necessary for front-end engineering. It consists of over a hundred example coding exercises, with a final coding project at the end. You can get hands-on experience coding with CSS and HTML, which directly help you earn an internship in web design. Further, you can earn a proper certification that you can share for free!
freeCodeCamp has other courses in this format, such as JavaScript, which you should complete if you want to be a front-end engineer.
5. CORE
Not necessarily a MOOC, but it is a completely free interactive textbook on economics. Not only you can view it as a web page, but you can also download it as a smartphone app. It explains the concepts of economics from the very start, so you don’t need any previous knowledge in economics. CORE explores not only the keywords in economics but also the history and the global inequality. Many professors around the globe is a contributor to CORE. It takes time to go through all the material, but it is certainly interesting!
Because it’s online, the textbook is not just words, but there are interactive quizzes, useful links to get you further.
