Lecture
This lecture delves into various data analysis techniques, starting with time series analysis and signal processing methods like Fourier series. It then shifts focus to summarizing data findings, discussing stochastic processes, random variables, and making aggregate statements. The lecture covers statistical concepts such as histograms, kernel density estimators, and cumulative distribution functions, emphasizing the importance of visualizing data distributions. Additionally, it explores hypothesis testing, outlier detection, and the impact of extreme values on statistical metrics. Practical examples and considerations for analyzing anomalous data periods and interpreting statistical significance are also discussed.