Data Science Fundamentals

Statistical sampling is defined as a technique used to select a representative subset (portion) of items from a larger population, allowing conclusions to be drawn about the entire population based on the characteristics observed in the sample, with each item having a known probability of selection. This is distinct from data mining clustering algorithms (which group objects based on similarity) and is not merely a refinement of a prior sample.

Business analysis, as defined in the BABOK Guide, is the practice of enabling change within an enterprise by identifying needs and recommending solutions that deliver value to stakeholders, and this work is carried out within the context of business projects or initiatives. Data science, on the other hand, is defined by its reliance on programmatic, algorithm-driven methods — including statistical modeling, machine learning, and code-based data processing — to extract insights from data. This is the core, well-established distinction between the two disciplines, as opposed to an oversimplified past-versus-future framing or a specific-versus-general framing, neither of which accurately or completely characterizes how the two fields differ.

Statistical modeling is fundamentally about representing data patterns and relationships in a way that communicates the outcomes of analysis—such as trends, predictions, and conclusions—to support understanding and decision-making. While techniques like sampling and correlation may be part of the modeling process, the overriding goal is to convey analysis results and conclusions in a clear, interpretable manner.

Moore's Law is the observation, originally made by Intel co-founder Gordon Moore, that the number of transistors on an integrated circuit—and consequently overall computing power—doubles approximately every two years. This trend reflects continuous advances in semiconductor manufacturing that allow more transistors to be packed into chips, increasing processing capability roughly twofold every two years.

Classification is a supervised learning method where data is sorted into categories that have already been defined (labels), whereas clustering is an unsupervised method that groups similar data points together based on inherent characteristics without relying on predefined categories. This supervised-vs-unsupervised distinction, and the presence or absence of predetermined groupings, is the fundamental difference between the two techniques.