Python for Time Series Data Analysis

Jose Portilla's "Python for Time Series Data Analysis" is a solid, beginner-to-intermediate applied forecasting course that holds a verified 4.5/5 from about 9,170 Udemy ratings (Bestseller / Highest Rated) with roughly 51K enrollments. Across about 15.5 hours and 95 lectures it walks through a clean, practical progression: NumPy/Pandas foundations, time-stamped data handling, statsmodels (ETS decomposition, EWMA, Holt-Winters), ACF/PACF, the ARIMA family (ARIMA, SARIMA, and SARIMAX with exogenous variables), a short RNN/LSTM deep-learning section, and Facebook Prophet. Its biggest strength is hands-on, copy-ready Jupyter notebooks and Portilla's clear, structured delivery; its biggest weaknesses, per a first-hand student review, are oversimplified model theory, roughly a third of runtime spent on basic Python, and notably thin Deep Learning and Prophet sections that mostly mirror documentation. It is a good fit for working Python developers new to forecasting who want a quick, practical on-ramp, but those who already know time-series fundamentals or need multivariate/state-of-the-art methods will find it shallow.

Strong, well-rated practical introduction with excellent runnable notebooks, but only worth the money if you are new to time-series forecasting and already comfortable with Python; experienced practitioners and anyone needing deep DL or multivariate forecasting should look elsewhere.

Best for: Working Python developers and data analysts who are comfortable with Python up to functions and basic Pandas, and who want a fast, hands-on introduction to classical time-series forecasting (ARIMA/SARIMA/SARIMAX) plus a first taste of Prophet and LSTM forecasting using ready-made notebooks they can adapt at work.

Skip if: Complete programming beginners (it assumes Python fundamentals), people who already understand time-series theory (much will be review), and anyone needing rigorous statistical depth, robust multivariate forecasting, or production-grade modern deep-learning forecasting, since the DL and Prophet coverage is thin and the focus is univariate.