The sky in X-rays is incredibly dynamic. Black holes and neutron stars vary on time scales ranging from milli-seconds to decades, their brightness occasionally changing by several orders of magnitude within seconds or minutes. Studying this variability is one of the best ways to understand key physical processes that are unobservable on Earth: general relativity in strong gravity, extremely dense matter and the strongest magnetic fields known to us are just a few examples. In this talk, I will give an overview of the state-of-the-art of time series analysis in high-energy astronomy. I will present key statistical methods and machine learning models we have been developing recently as well as point out the opportunities and challenges of the spectral-timing revolution we are moving toward with data from current and future space missions. At the same time, this talk also chronicles my path from conventional astronomical research into data science and data-intensive astronomy, and thus touches upon some of the occasionally surprising turns that path has taken.