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And the best part-- it's under the table 💲
Community Builder
Do you prefer Python or R for your analyses?
Chief
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Bowl Leader
Bowl Leader
Bowl Leader
Pro
And the best part-- it's under the table 💲
Community Builder
Do you prefer Python or R for your analyses?
Chief
Mentor
The techniques are helpful, but I've typically seen R used more in academia and Python in industry.
And, as above, that's not a long time to practically understand the concepts in detail, but maybe a good opportunity for you to understand what you want to pursue further.
New people don’t need to worry about this stupid R vs Python debate. They’re both great languages with a ton of benefits. Once you know one it’s not too hard to be able to use the other.
Learn techniques first, the language is just the implementation of that technique.
Conversation Starter
Man I find it funny how you spend just a week on these topics when there are some courses entirely dedicated to them.
To answer your question though, yes, I've seen those methods used in the wild. Will one week of learning be sufficient though to prep you for interviews and jobs? I think you would have to continue modeling or hone in on one method to get the most value out of this class. Just my two cents
Completely agree on this. I spent an entire quarter just learning good old linear regression and another learning GLM (Logistic and other generalized linear models). The topics shared by OP would be very high level overviews and you will have to spend a lot of time reading these in detail outside the course work
Yep looks good...specially if it has a project at the end
Topics are worthwhile. However, I would highly recommend taking a similar course in a CS or Stats department. They might cover less in a semester but the depth will make you a better data scientist
Coach
It’s a decent survey of common methods, the order seems a little weird to be though. Worth taking an entire course in regression and it’s extensions. It will help expose you to a few techniques and you can see if you actually like it before further study.
You can tell it’s also different from the order of the book used for the course.
Thank you all!!!
The concepts are helpful to know and understand, I’d also try to see if you can gain some practical experience in using that in your day-to-day or doing some outside work to practice.
I’d also recommend learning Python as it’s used more and more (and in some companies almost exclusively) in data science roles.
I would also look at some data scientist roles that you’re specifically interested in and from the job listing see 1) what tool is used mostly, and 2) what concepts are especially used that the role calls out, that can also prob help you figure out what especially to spend more time on initially.
But even so, get EDA and visualization down pat (including data cleansing) bc in many roles that will be the majority of what you’ll be doing. Lots of great free datasets out there to practice on, too!
Looks like a great overview of common topics in DS. Would probably be enough for you to understand when you’d apply each topic in practice, which goes a long way, but you’d probably need to dive deeper to get a real understanding.
Also would 100% recommend python over R unless you really want to work in life sciences. But even so there’s an increasing amount of python there
Echoing the others’ thoughts in that this course seems worthwhile for you to get a high level overview of what they each do.
Don’t worry about the R/Python debate for now. Focus on the concepts because that’ll set a solid base for you. As you go through the concepts, try to think about how they apply to your everyday life. The R/Python skills will follow with practice. Good luck!!