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.

Apache Kafka is more suitable for real-time data processing. It is a distributed streaming platform that can handle high-throughput, fault-tolerant, and real-time data streams, making it a popular choice for real-time data processing and analysis.

Semantic segmentation is an advanced computer vision technique that involves classifying each pixel in an image into a specific class or category. It's used for tasks like identifying object boundaries and segmenting objects within an image.

The role of a hidden layer in a neural network is to extract and transform features from the input data. Hidden layers learn to represent the data in a way that makes it easier for the network to make predictions or classifications. They are essential for capturing the underlying patterns and relationships in the data.

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.

The process of transforming skewed data into a more Gaussian-like distribution is called "standardization." It involves shifting the data's distribution to have a mean of 0 and a standard deviation of 1, making it more amenable to certain statistical techniques.