Time Series Data Prep and Analysis with LSTM Models

Time series forecasting and sequential analysis are critical skills in modern data science, yet preparing this data for deep learning can be highly challenging. Working with Long Short-Term Memory (LSTM) models requires a precise approach to structuring, scaling, and splitting sequential datasets. In this text-only course, you will learn how to transform raw sequential data into model-ready formats, avoiding common pitfalls like data leakage while building a solid foundation in recurrent neural networks. What you'll learn: - Understand the foundational concepts of time series data and recurrent neural networks. - Prepare raw sequential datasets by handling missing values and structural anomalies. - Apply proper temporal splitting and scaling techniques to prevent data leakage. - Structure data into three-dimensional arrays required for LSTM input layers. - Build and train basic LSTM models to identify complex temporal patterns. - Evaluate model performance using modern loss functions and validation metrics. The course begins with core terminology and theoretical foundations of sequential data before guiding you through step-by-step data preprocessing pipelines and practical LSTM model implementation. This course is designed for aspiring data scientists, analysts, and developers who are new to deep learning and want to work with sequential datasets. No prior experience with LSTMs is required. Start reading today to unlock the power of temporal data analysis and deep learning.