Time Series Forecasting and Survival Analysis Fundamentals

Many real-world data problems involve predicting when an event will occur or how a metric will change over time, requiring specialized techniques beyond standard regression. Understanding how to handle temporal dependencies and incomplete data is essential for accurate forecasting and risk assessment. This course provides a solid foundation in two critical areas of specialized machine learning: time series analysis and survival analysis. You will move from basic data concepts to understanding how to model patterns over time and handle censored data where outcomes are not yet fully observed. By the end of this program, you will be able to interpret temporal patterns and apply specialized models to predict future outcomes with confidence. What you'll learn: - Understand the fundamental components of time series data, including seasonality, trends, and noise. - Apply statistical models to forecast future values based on historical patterns. - Master survival analysis concepts to predict the time until a specific event occurs. - Handle censored data effectively to ensure accurate outcome inference in real-world scenarios. - Verify model assumptions using modern validation techniques and diagnostic tests. - Practice data preparation and modeling using current industry-standard libraries and workflows. The curriculum begins with core terminology and statistical foundations before progressing through specific modeling techniques for both forecasting and event-time analysis. You will explore practical applications through written explanations and code-based exercises designed to reinforce theoretical concepts. This course is designed for beginners in data science and machine learning who want to expand their toolkit; no prior experience with time-dependent data is required. Start building your expertise in specialized data modeling today.