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Hey, I’m Conor. I’m a data scientist and writer living and working in New York.


Python for Data Science and Machine Learning Bootcamp Review

Introduction

I came into this summer with an initial goal of reviewing fundamental concepts related to using Python for data science. I decided that the best way to achieve this was by working through online courses and independent side-projects in parallel. In my experience, I’ve found that this set-up gives me the right amount of structure and flexibility to learn most effectively.

Luckily, many Data Scientists out there have already taken on the task of analyzing the pros and cons of various online courses, so all I had to do was review the information and decide which one would be best for me. Most notably, David Venturi has a couple excellent posts that are designed to do just this.

After some thought and a bit more research, I was finally able to narrow down my choice to a popular course on Udemy called Python for Data Science and Machine Learning Bootcamp. This course is broken down into short sections that touch on everything from data analysis to implementing machine learning algorithms to getting started with Spark and TensorFlow. Below you’ll find a more excitable, formal excerpt taken from the course overview:

Are you ready to start your path to becoming a Data Scientist!

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python!

On average, I’ve been going through about a section a day for the last few weeks and I’m finishing things up within the next couple days. While the experience is still fresh in my mind, I thought I’d write up a thorough review on the course for anyone else that’s looking into a Python-focused data science course.

The Good

First and foremost, I enjoyed the lectures throughout the course. They were about the perfect length and usually fairly concise, though I recommend watching on at least 1.25x speed since things can move a bit slow sometimes. The concepts were well-explained, especially in the earlier lectures on data analysis with NumPy and Pandas. Props to the instructor, Jose for that.

I also thought the structure of the course was excellent. Moving from theory to watching someone else apply the concepts to hands-on practice and finally to review and corrections seemed to be the way to go.

The breadth of topics covered was impressive as well, especially for a course targeted at beginner and intermediate Data Scientists. The course touched on just about everything you could imagine to some degree, from Python basics to NLP to deep learning.

Overview of topics covered

Lastly, the Jupyter notebook and solution overviews included with the course were easily my favorite part. I’ve found that it’s often hard to get motivated to do coursework assigned via an online course, but that wasn’t the case here. I enjoyed working through the practice problems and was eager to check the solution notebooks and review lectures afterwards.

The Bad

Despite all of this, I did have a few criticisms with the course. I found that despite spending half the course on machine learning algorithms, the content doesn’t go very deep whatsoever. To be fair, I believe that this is largely due the fact that the course is targeted for beginners— which was explicitly stated in the overview.

However, I found that the application of different algorithms with minimal theory got repetitive after the third or fourth ML section. For my purposes, I would’ve liked to see some of the same old scikit-learn calls replaced with more fundamental ML theory and concepts.

Lastly, it’s worth mentioning that the course costs about $10 out of pocket. This is extremely reasonable and I had no problem coughing up the spare change. With this being said, I also believe that you can find equal if not better quality content online for free from content creators like Siraj and Sentdex, among plenty of others.

The Ugly

This doesn’t have anything to do with this specific course, however it’s probably worth mentioning. Over the past few years, there has been some pushback on Udemy as an organization. Several posts and videos have gone into this topic, like this one from a former instructor on the platform and more recently, the video linked below from a popular content provider on Youtube.

Personally, this was my first experience on Udemy and I had no problem with the platform, but there are some interesting points brought up that are worth considering next time you enroll in a course.

Final Takeaways

Overall, this course does an excellent job doing what it sets out to do. There’s a reason that over 100,000+ students have enrolled in the course, despite the paywall. It serves as a strong starting point for beginners in data science or an effective quick review for more established practitioners.

However, if you really want a strong foundation in machine learning, I recommend pairing this course with a more theory-intensive course like Andrew Ng’s critically-acclaimed Machine Learning course on Coursera.

Finally, in respect to the paywall, I’m extremely confident that there is equal, if not better material available online for no cost if you look hard enough. When you choose to enroll in this course, you are effectively paying for the structure and accountability that comes with buying an online course. For me, that choice made sense and in the end, this course turned out to be a solid investment of both time and money.

Final Rating: 4/5 Stars

Conor Dewey