Descriptive-Statistics

Statistical Foundations for Data Science and Analytics Data science blends math with real world problems. Statistical thinking helps you turn numbers into reliable knowledge. By focusing on uncertainty, you can avoid overclaiming results and design better experiments. This guide covers core ideas that apply across fields, from business analytics to research and product work. Descriptive statistics summarize data quickly: mean, median, and mode describe central tendency; standard deviation and interquartile range describe spread. A simple example: monthly sales data: 8, 12, 9, 11, 14. The mean is about 10.8 and the spread hints at variability. Visuals like histograms support interpretation, but the numbers themselves give a first read. In practice, you will often report these numbers alongside a chart. ...

Data Science for Everyday Decision Making Data science is not just for researchers. It can help people make better choices with small, honest data. You don’t need fancy software to start. The goal is to replace guesswork with evidence you can trust, even for everyday tasks. Key steps to apply data science at home: Define the goal and choose a metric that matters (time saved, money kept, energy reduced). Collect a small amount of data you can track for a week (minutes spent on tasks, expenses, weather days). Use simple summaries such as averages, best/worst values, and a basic comparison between options. Watch for bias—make sure you measure like-for-like and update data when things change. Simple tools you can use: ...