Python is an incredibly versatile language, and it has a huge amount of support in data science, machine learning, and statistics. Not only that, but you can also do things like build web apps, automate tasks, scrape the web, create GUIs, build a blockchain, and create games.
Because Python can do so many things, I think it should be the language you choose. Ultimately, it doesn’t matter that much which language you choose for data science since you’ll find many jobs looking for either. So why not pick the language that can do almost anything?
However, learning R is also very useful in the long run since many statistics/ML textbooks use R for examples and exercises. In fact, both books I mentioned at the beginning use R, and unless someone translates everything to Python and posts it to Github, you won’t get the full benefit of the book. Once you learn Python, you’ll be able to learn R pretty easily.
Check out this StackExchange answer for a great breakdown of how the two languages differ in machine learning.
Are certificates worth it?
One big difference between Udemy and other platforms—like edX, Coursera, and Metis—is that the latter platforms offer certificates upon completion and are usually taught by instructors from universities.
Some certificates, like those from edX and Metis, even carry continuing education credits. Other than that, many real benefits, like accessing graded homework and tests, are only accessible if you upgrade. If you need to stay motivated to complete the entire course, committing to a certificate also puts money on the line, so you’ll be less likely to quit. There’s definitely personal value in certificates, but, unfortunately, not many employers value them that much.
Coursera and edX vs. Udemy
Udemy does not currently have a way to offer certificates, so I generally find Udemy courses to be good for more applied learning material. In contrast, Coursera and edX are usually better for theory and foundational material.
Whenever I’m looking for a course about a specific tool, whether Spark, Hadoop, Postgres, or Flask web apps, I search Udemy first since the courses favor an actionable, applied approach. Conversely, when I need an intuitive understanding of a subject, like NLP, Deep Learning, or Bayesian Statistics, I’ll search edX and Coursera first.
Data science is a vast, interesting, and rewarding field to study and be a part of. You’ll need many skills, a wide range of knowledge, and a passion for data to become an effective data scientist that companies want to hire, and it’ll take longer than the hyped-up YouTube videos claim.
If you’re more interested in the machine learning side of data science, check out the Top 5 Machine Learning Courses for 2022 as a supplement to this article. Also, if you're just starting with Python programming, check out Best Python Courses According to Data Analysis.
If you have any questions or suggestions, feel free to leave them in the comments below.
Thanks for reading, and have fun learning!