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Coursera VS Udacity | Data Science & AI ML Courses

You always want the vest for yourself right? And in doing so, you make comparisons between two or more things. You start to look for similarities between them or what is offering what. Similarly, you’ll be curious to know the pros and cons of both the items and then make a decision about which one to take. We will choose between Coursera vs Udacity. But if you have to choose between two things that are very much similar to one another but are not? Then what would you want to do, how are you going to decide which one to choose?

 

To explain this more clearly, let’s take a look at an example. You have tea and coffee on your table and then you’re being asked to choose one from it. How are you going to choose? How will you be deciding which one is best? Though both are liquids and have almost similar color and are used with milk and yet both are different from one another right? The same is the case with Coursera vs Udacity. It is hard to decide which platform offers the best AI machine learning and data science courses. Both are pretty much similar to one another, both offer outstanding, extra-ordinary, and engaging courses by world-class professors and yet the platforms are different from one another. So how can you choose between the two?

How to choose between the two?

There are many criteria that you can set and then make a comparison between the Coursera vs Udacity. Keeping that in mind, takethiscourse.net also has set some criteria, and then we are going to make a comparison as to which one is more suitable.

Based on the content offered:

This is the first criteria with which we can make a comparison as to which platform is better at providing AI machine learning and data science courses.

  • It is true that both Udacity and Coursera are partnered with world-class organizations and have the best of the professors who instruct the courses.
  • Similarly, both platforms have the state of the art learning platforms, which is correct too.
  • The learning material available in the courses offered by the two platforms is up-to-mark, easy-to-understand, engaging, challenging, and whatnot.
  • Up till now, there hasn’t been any difference here, but the difference comes in the duration. For example, Udacity’s Nanodegree programs are longer in duration than Coursera. Whereas, Coursera’s Nanodegree programs are often similar in length. But in a specialization, you can take a break after completing a course and then start the next part whenever you’re ready.

Based on Cost:

  • Udacity follows a pay upfront model pricing which means that you have to pay on the spot to enroll in a course.
  • Whereas Coursera follows a month-to-month pricing model.
  • Both offer scholarships and financial support and many courses are available at discounts several times a year.

Based on continuation:

  • Ok so you have completed an AI machine learning course for example from either Coursera or Udacity. What will happen now? Do you move on to taking another course? Or apply for a job or what? Well, Udacity has a policy or you can say a service in which they partner all their students who have completed a Nanodegree with a career counselor. He is going to help you get along with an organization or something similar to that.
  • Whereas, Coursera doesn’t offer such a service.
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Conclusion:

After this little comparison, you can see it’s really hard to decide which one is better between Coursera vs Udacity because, in reality, both are exceptional. And deciding maybe Coursera is better and can help you make a bright future for yourself and vice versa is just too cliché. So we would suggest if you only want to learn and not mingle in this war of which one is better or which one to take. You just make a plan of what it is that you exactly want to learn from the course. Afterward, experiment with both the platforms to see which one is according to what you want. That way it would be simpler for you to decide which one to choose. So try this method now and never stop learning. 

TTC Team

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TTC Team

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