What should I start with first? Python for beginners or applied data science?

I am new to data science and I would like to go deep into it.

I decided to start working with advanced competitions on kaggle, yet I should refine my knowledge in python and ML.

I am an engineer, with 4 years of programming (basically php vanilla JS, and javascript based frameworks)

I took a coirse on Udemy about ML with python. As I am already a programmer, I know most of the basics, I just need to familiarise with the script and how its written in python.

I am desperate for a help from someone, who can help me with if I should take the Python for everyone or simply just dig in directly into Applies data science with Python from University of Michigan.

I know sometimes I struggle writing a code with python but with a basic google search I can find an answer or figure out one of my own.

I know that I can skip lots of weeks on coursera as I already knows the basics. But still confused.

Topic coursera python machine-learning

Category Data Science

1
answered at

TLDR: No, it is not required for someone with your background to take "Python for everyone" beforehand.

I have taken the "Applied Data Science with Python Specialization" by UofM and it provided a very soft start in terms of Python for Data Science. Also, see the description of the first course of the specialization "Introduction to Data Science in Python":

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

As they say, the course walks you through the important libraries, such as Numpy and Pandas, and covers some key methods. Moreover, the second course of the specialization "Applied Plotting, Charting & Data Representation in Python" provides an intro to Matplotlib. So this one is covered too.

Finally, if you would like to go for competitions I highly recommend the course "How to Win a Data Science Competition: Learn from Top Kagglers" (as a next step). It is more advanced than the UofM specialization and provides great insights in the mechanics of and approaches for competitions.

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