What are the most common data partitioning techniques for data analysis?

Simple random sampling is a technique in data analysis where you randomly select data points from a larger dataset. It's like putting all your data into a virtual hat and picking out pieces without any particular order or bias. This method ensures that every data point has an equal chance of being chosen, which helps in creating a representative subset for analysis. It's a fair and effective way to avoid skewing your results and get an unbiased sample from your data pool.

When it comes to data partitioning techniques for data analysis, there are a few common approaches. The first is called "horizontal partitioning," which involves splitting the data into multiple smaller datasets based on a particular attribute, such as a date or country. The second approach is called "vertical partitioning," which involves splitting the data into multiple smaller datasets based on a specific column or field in the data. Finally, there's "random partitioning," which involves randomly dividing the data into smaller datasets.

SRS is a computationally efficient sampling technique. This is because it does not require any prior knowledge of the data, such as the distribution of the data or the presence of outliers. This makes it a good choice for large datasets.

In essence, stratified sampling allows you to focus your sampling efforts where they matter most, improving the representativeness of your sample and the reliability of your conclusions, especially when dealing with populations with diverse characteristics or attributes.

SS is one the best in business! it helps in maintaining the balance & representation of subgroups within the dataset, ensuring that the analysis is both accurate & generalizable, while minimizing the potential for bias & error, particularly while working with imbalanced data. Scenarios where given dataset is stratified, the respective analysis and models are more likely to generalize well to the entire population from which the data is drawn. While dealing with imbalanced classes or subgroups, we can use stratified sampling while splitting the data into training & testing sets, to improve the model's ability to make accurate predictions, especially in case where data have rare events or minority classes.

Based on hands-on experiences, I would say CS is a useful especially while dealing with large populations or spatially dispersed elements. It offers efficiency and cost savings, however, I must say, applicants should be mindful of the potential clustering effect. CS is useful in scenarios where the elements of the population are geographically or logistically grouped. E.g. for national survey, one might randomly select a few cities (clusters) and then survey individuals within those cities. Here, CS can simplify the logistics pertaining to data collection. one of the challenges of CS is that it can introduce additional variability within clusters. Data points within same cluster may be more identical to each other as to other clusters

Train-test split is a technique in data analysis where you divide your dataset into two separate groups: the 'training' set and the 'testing' set. The training set is similar to a practice field where you teach your model to learn patterns and make predictions. The testing set is reserved for evaluating how well your model performs on unseen data. It's a way to ensure that your analysis is both rigorous and practical, much like studying and then taking a real test to see how well you've learned.

The train-test split may not be optimal for all datasets. For example, if the dataset is small or imbalanced, TTS may lead to biased results.

We know that K-fold cross-validation is useful for machine learning model evaluation & selection. and to dissect further, It helps in addressing issues related to model performance, hyperparameter tuning, and provides a robust and unbiased assessment of a model's capabilities to enable generalizability. Another aspect is that K-fold helps tor reduce data wastage. E.g. usual train-test splits could lead to a significant portion of the data being unused for testing. However, K-fold cross-validation utilizes the entire dataset for both training & testing across multiple iterations, in turn maximizing overall data utility.