In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features were observed. This course provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthrough in Nonlinear Time series Analysis (NLTS) helps address this problem. Nonlinear Time Series Analysis is a collection of empirical tools designed to aid practitioners detect whether observed complexity is driven by stochastic or deterministic dynamics. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science and the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomenon investigated in several fields have been shown to exhibit deterministic nonlinear structures. The aim of the course is to help non-mathematicians – with limited knowledge of nonlinear dynamics – to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the course makes participants active learners with hands-on computer experiments using computer language R directing them through NLTS methods and helping them understand the underlying logic.