Data Preprocessing is the process of cleaning, transforming, and organizing raw data to prepare it for modeling. It includes tasks such as handling missing values, feature scaling, and encoding categorical variables. This step is crucial in Data Science to ensure the quality of data used for analysis and modeling.

When working with large datasets that do not fit into memory, the Python library "Dask" is a useful tool for efficient computations. Dask provides parallel and distributed computing capabilities, enabling data scientists to handle larger-than-memory datasets using familiar Python tools.

Convolutional Layers in neural networks are responsible for feature extraction and learning spatial hierarchies, making them crucial in tasks such as image recognition. They apply filters to the input data, capturing different features.

In time-series analysis, creating lag features means using previous time steps (observations) as new data points. This allows you to incorporate historical information into your model, which can be valuable for forecasting future values in time series data.

Residual Networks (ResNets) are known for their residual connections, which allow for easier training of very deep networks. ResNets have become a standard in deep learning due to their ability to mitigate the vanishing gradient problem, enabling the training of much deeper architectures.

A Box Plot is a commonly used visualization for detecting outliers in a single variable. It displays the distribution of data and identifies potential outliers based on the interquartile range (IQR). Data points outside the whiskers of the box plot are often considered outliers. Box plots are useful for identifying data anomalies.

Data Preprocessing is the step in the Data Science Life Cycle that involves cleaning the data, handling missing values, and preparing it for analysis. This step is crucial for ensuring the quality and reliability of the data used in subsequent analysis.

The mean, also known as the average, is a common measure of central tendency. It's calculated by adding up all the values in the dataset and then dividing by the number of data points. The mean provides a sense of the "typical" value in the dataset.

The True Positive Rate, also known as Sensitivity or Recall, calculates the ratio of true positives to the sum of true positives and false negatives. It measures the model's ability to correctly identify positive cases. It is an important metric in binary classification evaluation.

k-NN imputation is a technique that uses the similarity of data points to impute missing values. It finds records with similar characteristics to the one with missing data and replaces the missing value with the imputed value from its nearest neighbors. Other options are simpler imputation methods.