Beginner or an advanced learner, if you are interested in time series analysis and forecasting only few reading materials should meet your 95% needs.
In Python, statsmodels is a good place to start with.
statsmodels is a Python module for statistical analysis and has some good time series and forecasting examples. You should also read Machine Learning Mastery blogs. Jason Brownlee is an absolute master of time series in python. A lot of theories and application you’ll find in his blog series.
If you do not want to go through a steep learning curve, you have an one stop solution for all time series and forecasting needs. That’s in R. Just review Rob Hyndman’s) wonderful, easy to follow book Forecasting Principles and Practice. This covers everything you need to learn from basic time series analysis to advanced forecasting techniques. One of the most amazing thing is you can do almost any forecasting with only few lines of codes.
Last words on Python vs R: the main difference is that Python community doesn’t have one Rob Hyndman yet. This is a gap, but at the same time an opportunity for someone to be famous! Everything you do in R, you can do in Python; but because things are scattered all over, you really have to burn a lot of your energy to find the right tools at the right time.