Descriptive-Stats

Statistical Methods for Data Analysis Data analysis uses a toolbox of methods to turn raw numbers into understanding. Good methods help you describe what happened, compare patterns, and judge what might be true beyond the observed data. A clear plan, based on a few core ideas, keeps results honest and useful for decision making. Descriptive statistics give quick summaries. You can report the mean and median to know the center, and the range or standard deviation to see spread. Visuals like histograms or box plots help spot skewness or outliers, and they summarize data at a glance. ...

Basics of Data Science and Statistics You Should Know Data science blends math, statistics, and computing to turn raw data into actionable insights. It helps teams answer questions, improve products, and tell clear stories with numbers. Statistics gives tools to summarize data and judge uncertainty. Data science adds steps to collect, clean, and model data at scale, so decisions are based on evidence rather than guesswork. Foundations of Statistics Descriptive statistics summarize a dataset with simple numbers. They include the mean, median, and mode, plus the spread measures like range or standard deviation. Visuals such as histograms and box plots help show where the data lie. ...

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. ...

Exploratory Data Analysis: Techniques for Beginners Exploratory Data Analysis (EDA) is the first look at your data after you collect it. It helps you understand what the numbers say, find mistakes, and plan the next steps. This guide covers simple techniques that work for most datasets and all kinds of tools. What is Exploratory Data Analysis? EDA is a mindset as much as a set of tricks. You learn the shape of the data, check data types, and spot patterns. You look for missing values, unusual values, and surprising relationships. The goal is to describe the data clearly and prepare it for any modeling or reporting. ...