The heights recorded as 500 cm are likely outliers in the dataset. Outliers are data points that significantly differ from the majority of the data and may indicate measurement errors or anomalies. It's important to identify and handle outliers appropriately during data analysis.

ARIMA (AutoRegressive Integrated Moving Average) captures both trend and seasonality in time series data. It combines autoregressive, differencing, and moving average components to model complex time series patterns, making it a powerful method for forecasting data with seasonal and trend components.

Seaborn is a Python library built on top of Matplotlib, designed for statistical data visualization. It provides a high-level interface for creating informative and attractive statistical graphics, making it a valuable tool for data analysis and visualization.

The Pooling Layer in a CNN is responsible for reducing the spatial dimensions of the input. This layer downsamples the feature maps, which helps in retaining important features and reducing computational complexity.

HDFS (Hadoop Distributed File System) is the primary component in the Hadoop ecosystem for distributed data storage. It is designed to store large files across multiple machines and provides data durability and fault tolerance.

Text data from social media platforms is typically unstructured. It doesn't have a fixed format or schema. It may include text, images, videos, and other content without a well-defined structure, making it unstructured data.

The "Model Deployment" step in the Data Science Life Cycle is essential for taking the data science model from development to production. It involves integrating the model into real-world applications, making it a crucial phase.

XML and JSON are examples of semi-structured data. Semi-structured data is characterized by a hierarchical structure and flexible schemas, making it a middle ground between structured and unstructured data. It is commonly used in various data exchange and storage scenarios.

Data Scientists are typically proficient in advanced statistical modeling. They use statistical techniques to analyze data and create predictive models. While Data Analysts may also have statistical skills, Data Scientists specialize in this area.

Ensemble methods, like Random Forest and Gradient Boosting, combine multiple models (decision trees in the case of Random Forest) to improve overall predictive performance. These models are trained independently and then aggregated to make predictions. The combination of models is what enhances the accuracy and robustness of the ensemble.