I have completed the following through courses at coursera:
R-Programming, Getting and cleaning data – John Hopkins University
Introduction to SQL – University of Michigan
Managing Big Data with MySQL and TERADATA – Duke University
Data Visualization and Communication with Tableau – Duke University
I am currently working on python courses. So i think i have got the basics covered.
I have two questions:
1) what next to study? I am having a hard time to decide. just need a direction
2) given that i have no experience, can i work as a basic freelancer to develop and practice these tools i have learned or i should learn more in this field and then i can execute?
For the basics of data science, we will notice a good overlap between R and Python, so, in that case, you'd better stick with whichever better suits you and master it. You have learned about a good set of tools before you get to know other tools, master one or few of these ones that you're already working on it.
I would say but I'm not, to go into Machine Learning and/or Deep Learning once you took your Python courses, HOWEVER, ML and DL might, unfortunately, make you uninterested in other (basic) foundations of data science. Therefore, I would recommend you any good inferential statistics course:
https://www.coursera.org/learn/inferential-statistics-intro taught in R, Duke University
Do you have any good material, books suggestion for Exploratory Data Analysis and how to handle missing values.??