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.
Statistics is a vital tool in the field of data science, revealing patterns, trends, and relationships among large, complex rows
Statistics are also integral in preprocessing data, hypothesis testing, and model validation.
In the ability to make accurate inferences, predictions and provide actionable insights, statistics and data science have a symbiotic partnership.
There are both essential to provide the best information to a more insightful future.

fundamentals of statistical methods in data analysis.

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

Application of statistics in data science.

• 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.