#####Big limitations in data science education####
- learning DS libraries before learning coding basics
- learning ML algorithms before learning how to preprocess your data
- learning Deep Learning before Machine Learning
- Learning data viz before understanding the basics of statistical inference This is where most people may spend months trying to read a book, finish a MOOC, or learn a topic on YouTube with little retention of the applied topic.
####best ways to deal with data Science####
Anytime you’re learning a new concept you can’t neglect the fundamentals & always keep the big picture in mind because:
1. You have to know coding basics before you can even debug the implementations of your DS/ML Libraries.
2. You have to know how to preprocess your data before applying hashtag#machinelearning methods accurately.
3. You have to know statistical inference before you make sense of your visualization. Remember to don’t just jump into a course without taking the time to ask yourself “why” and “how” is this being used. It’s only when you start asking yourself questions about the problems where you begin to connect the dots more. And by having a good foundation of the basics, those dots that you connect with new concepts will be retained a lot longer.
Yes you are right.
Many times unfortunately people want to gain quick expertise and they put the "cart before the horse" so to speak.
I would recommend that before any discussion on Machine Learning etc..., to check first on the Foundations of Data Science that is in the field of Math ....statistics and probability. Many learners have vague or no background in statistics and probability ... not to go even further and ask about algebra (matrix operations etc...).
So to do things right, I would start with the foundations, then dive into software development and only after hone on specifics.
But people are eager to "fast learn", .... that is as healthy as "fast food", ... hence probably not the best way.
My 2 cents on the topic.
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