Role of statistics in data science.

What is statistics? Data science is based on statistics. It is a mathematical framework that collects, analyzes and performs data. This framework plays a crucial role in turning raw information into useful insights. We have several fundamentals used to summarize and describe the basic features of a dataset namely: Descriptive statistics; is used to describe the basic features of a dataset Probability theory; provides mathematical rules for handling uncertainty, which is essential for predictive modelling. Inferential statistics; This is used to make predictions, generalizations or draw conclusions about a broader population based on a smaller sample. Hypothesis testing; this is a structured method for making data-driven decisions and validating assumptions. Correlation & Regression analysis; They are techniques used to quantify the relationships between variables. -> Correlation mainly measures the strength and direction of a linear relationship between two variables. -> Regression analysis is the relationship between a dependent variable and more independent variables to make predictions Hypothesis testing- This helps in knowing whether a new product feature improves user engagement. Predictive modelling- it models, predict future outcomes like forecasting stock prices or customer purchases. Risk analysis- In fields like finance and healthcare, statistics is used to asses risk and make informed decisions.