Data Science Literacy

Sketch and Solve is a computing paradigm used in the field of Big Data to efficiently process large-scale datasets. The basic idea behind this approach is to first create a compact "sketch" of the dataset, which is a compressed representation of the data that preserves important statistical properties such as frequencies, correlations, and patterns. The sketch is then used to solve various computational problems on the dataset, such as querying, clustering, classification, and regression. The sketching process involves mapping the original data into a lower-dimensional space, where the data can be represented by fewer dimensions or features, without losing too much information. This reduces the memory and computational requirements of subsequent analyses. There are various types of sketches that can be used depending on the nature of the data and the problem at hand, such as random projections, feature hashing, sketching matrices, count-min sketches, and Bloom filters. The so