Inferential-Stats

Stats-Driven Data Science: From Descriptive to Inferential Data science often begins with numbers, plots, and stories. Descriptive statistics give a clear snapshot of what happened, while inferential statistics let us ask what might be true beyond the observed data. This shift—from describing data to reasoning about populations—changes how we decide and communicate. Descriptive metrics show central tendency, spread, and shape. Mean and median reveal typical values; standard deviation and interquartile range show spread; histograms hint at distribution. These tools are essential for cleaning data, spotting anomalies, and guiding model choices. ...

Statistical Methods for Data Science Statistics is a core tool in data science. It helps turn raw numbers into understanding. This post highlights practical methods you can use in real projects, from describing data to building reliable models. You will find simple explanations and small examples you can try yourself. Foundations start with describing what you have. Descriptive statistics summarize a dataset: mean, median, mode, range, and spread. Visuals like histograms and box plots help too. For a quick demo, imagine five house prices: 200k, 250k, 275k, 300k, 350k. The average is 275k and the spread shows how far prices vary. Simple checks, like counting missing values, also guide your work. ...