Cars that drive themselves… it used to sound like something out of a sci-fi movie. But leading tech companies around the world are working feverishly to make them a reality. Higher education institutions are even teaching the fundamentals behind them!
This extends to e-learning as well, as both Udacity and Coursera have programs on how to make cars drive themselves. The most prominent among them are Coursera’s Self-Driving Cars Specialization (presented by the University of Toronto) and Udacity’s Self-Driving Car Engineer Nanodegree. We’ll review both of them here so you can get a sense of whether or not these programs are for you, and which one you should take.
What is covered in this article
Let’s start our engines! (Sorry; had to say it.)
Coursera vs. Udacity self-driving car course comparison table
Here, we’ll go over some basic facts and observations about the top programs on self-driving cars for each platform. That should give you a general idea of whether to enrol in Coursera’s Self-Driving Cars Specialization vs. Udacity’s Self-Driving Car Engineer Nanodegree, or the other way around.
|Duration & Pace||
Of course, there are many other comparisons we can make, and in more detail. We’ll cover those below.
Compare Coursera vs. Udacity self-driving car courses
If you couldn’t make a decision on whether to take the Nanodegree or Specialization based on the table above, we’ll be diving into the details here. After we contrast the two programs more fully in terms of cost, teaching styles, required knowledge, time commitment, and outcomes upon completion, you’ll hopefully have a better idea of which one you should pick.
Let’s start at the end: what benefits – tangible or otherwise – can you expect to gain upon completing an education program on self-driving cars? This is one area where Udacity and Coursera differ most, as we’ll demonstrate.
After you finish the Self-Driving Car Engineer Nanodegree program, Udacity’s Career Services division will work with you to show off your newly-acquired skills in all the right places. Plus, with all of Udacity’s corporate partnerships, you might even be able to jump straight into a job with one of the companies that worked to create your program! Imagine being able to start a high-paying tech career with the likes of Mercedes-Benz, Nvidia, Uber, Didi, or McLaren Applied Technologies after finishing your Nanodegree! This is one of the big draws that makes a Nanodegree worth its price.
Finishing the Coursera Self-Driving Cars Specialization will award you with a special certificate denoting that you completed the program. In addition, you’ll also earn completion certificates for each individual course in the Specialization: Introduction to Self-Driving Cars, State Estimation and Localization for Self-Driving Cars, Visual Perception for Self-Driving Cars, and Motion Planning for Self-Driving Cars.
These are great credentials that you can show off on your resume, in your portfolio, and on various other professional profiles. However, they don’t count for credit towards academic programs or job certification (unless an institution indicates otherwise). Coursera also isn’t active in helping you job-hunt after you finish the Specialization, unlike when you finish a Udacity Nanodegree.
Duration & Pace
How long does a program on self-driving cars take to complete, and how much time and effort do you have to invest? Udacity and Coursera have different guidelines regarding that, which we’ll outline here.
Udacity advertises that you can complete the Self-Driving Car Engineer Nanodegree in about six months. But there’s a catch: that’s assuming you put in an average of 15 hours per week of work on the program. That’s triple the pace of Coursera’s Self-Driving Cars Specialization! So while you can complete the Nanodegree in a shorter time frame, you need to dedicate much more of your time to it overall – around 390 hours in total.
Coursera’s Self-Driving Cars Specialization takes an average of seven months to complete. While this at first seems longer than the corresponding Udacity Nanodegree, it’s at a much more relaxed pace of about 5 hours per week. Of course, you can devote more time to your studies and work ahead to try and shave a month or two off your completion time, which will save you some money. In all, though, the program lasts only about 150 hours.
Self-driving cars is a cutting-edge technology field, so you should have some high-level knowledge before you attempt to dive into it. Here’s what Udacity and Coursera recommend you know before tackling their programs on self-driving cars.
As an advanced-level program, Udacity’s Self-Driving Car Engineer Nanodegree requires a fairly large skillset from the get-go. You need to be familiar with the basics of physics, statistics, calculus, and linear algebra. You also need to be fairly competent in computer programming using both the Python and C++ coding languages.
Udacity recommends having a background in programming, math, computer vision, or machine learning if you want to successfully complete this Nanodegree. If you’re not sure that you’ll be up to the task, Udacity’s Intro to Self-Driving Cars Nanodegree may be a good place to start, as its programming and math requirements aren’t as steep.
Like its Udacity Nanodegree counterpart, Coursera’s Self-Driving Cars Specialization is one of the website’s more advanced-level offerings. You need many of the same skills: being able to program using the Python 3.0 programming language, as well as having familiarity with physics, statistics, calculus, and linear algebra. It’s also recommended that you have some knowledge of probability and control theory.
The one skill you don’t need for the Coursera Specialization that you do for the Udacity Nanodegree is experience programming with the C++ coding language. Still, having a background in mechanical/electrical/computer engineering, robotics, computer vision, or deep learning will go a long way towards helping you succeed in this Specialization.
It’s being increasingly recognized that how you teach something matters as much as (or more than) what you’re actually teaching. So how do Udacity and Coursera approach instructing the students in their respective self-driving car programs? Let’s have a look.
Udacity designs its Nanodegrees with real-world practical problems at the core, and then surrounds them with lessons that build the skills you need to solve them. And the Self-Driving Car Engineer Nanodegree is no exception. There are nine projects in total in the program, and they challenge you to develop software that can do things like identify and monitor lane lines, traffic signs, and moving vehicles; precisely locate a car if it’s stolen, control a car’s systems while predicting what nearby moving objects will do, and more!
While that all sounds daunting, one of the great things about Udacity is you often have lots of access to outside help. Udacity partners with all sorts of companies who use the technology they teach on a daily basis, and often invites their training teams in to help show you how to work it. Udacity also has a mentoring program where it hires tech enthusiasts to be available for one-to-one help with students. So even if you get stuck on something you don’t understand, you’ll know that there’s somebody who knows their stuff available to help walk you through it.
Coursera’s Self-Driving Cars Specialization is based on a series of courses taught in the real world at the University of Toronto. As such, the courses that make it up are delivered in a similar way to traditional colleges and universities: with lectures, readings, and progress-checking assignments. It’s still a very hands-on program, but in contrast to Udacity’s comparable Nanodegree, it focuses a bit more on theories and questions driving the autonomous technology industry as a whole (pun not intended).
A big perk of Coursera’s system is that this Specialization’s course lectures are subtitled in over 12 languages. Coursera’s interface can be translated into 10 different languages, as well. That makes the course materials a bit easier to understand for people whose first language isn’t English (though some materials may still require some understanding of English). A downside, however, is that Coursera doesn’t have any sort of mentor system like Udacity does. So asking the course administrators something privately, or posing a question to your fellow colleagues in an open forum setting, are your best options for help.
Learning online has become an often-cheaper alternative to frequenting a college or university campus in real life. But feature-rich programs like the Udacity Nanodegree and Coursera Specialization on self-driving cars are still going to cost you some coin. How much? Let’s see.
When you enrol in a Udacity Nanodegree, you can choose to purchase access to the course for the average completion time, and then pay monthly if you need more time. Or you can pay month-by-month from the start. Either way, it’s going to take a toll on your wallet.
Nanodegrees cost $280/month if you buy the recommended time frame bundle, and then $400/month for each month after (or if you choose to pay as you go when you sign up). For the Self-Driving Car Engineer program, which lasts approximately six months, that’s going to cost you in the ballpark of $1700 plus tax.
The Self-Driving Cars program is one of the more expensive Specializations on Coursera. At $80/month, though, it’s still much cheaper than its Nanodegree counterpart. If you follow the recommended time frame of 7 months, it will only end up costing you around $650 with tax included. That’s about three times less than the average cost of the Self-Driving Car Engineer Nanodegree.
Another point for Coursera here is that Specializations come with a one-week free trial. So if you enrol in the Self-Driving Cars program and decide it’s not your bag, you can cancel within a week and not pay a dime. That’s something you can’t do with the Nanodegree.
Coursera vs. Udacity self-driving car programs: the main takeaways
To sum up, both Udacity’s Self-Driving Car Engineer Nanodegree and Coursera’s Self-Driving Cars Specialization are both great options for getting quality education about a rapidly-growing branch of technology. But which one is right for you?
The Nanodegree is probably the better pick if you’re dead-set on directly starting a job or career working specifically with self-driving cars. It gives you more hands-on experience, more one-to-one technical help, and better immediate employment opportunities. It costs a lot of money, though, and you have to devote much more time and effort to finish it on schedule so that you don’t end up paying even more. This can be made all the more difficult if you don’t understand English very well.
On the other hand, you might prefer the Specialization if you want a more theoretical landscape view of autonomous technology as a whole industry, with self-driving cars being one (but still a key) part of it. It’s not as big of a time or skill commitment, and it’s more accessible for people who aren’t that comfortable with English. It costs much less money, too. But personalized help is harder to come by in the program, and the credentials you get on finishing it aren’t guaranteed to help you advance your academic or professional career. They still could in some cases, though.
And that’s the finish line for our comparison of self-driving cars programs on Udacity and Coursera. But if you’re interested, we have even more comparisons of the two e-learning platforms, including a breakdown of a Udacity Nanodegree vs. a Coursera Specialization.