Data Science

Data Science is the interdisciplinary field that combines tools, techniques, and algorithms to analyze data and extract meaningful insights.

A Data Scientist works by collecting, analyzing, and interpreting complex data to uncover insights and support decision-making.

Data refers to raw facts, figures, or information that can be analyzed to gain insights and support decision-making.

A database table is a structured collection of data organized into rows and columns, commonly used in Data Science for storing and managing data.

Variables are data elements that can hold different values, which may change during the execution of a program or experiment.

Data Science is the field that uses scientific methods, algorithms, and systems to analyze and interpret data, and Python is a popular programming language used to implement data analysis and machine learning techniques.

A Python DataFrame is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure, commonly used in Data Science for data manipulation and analysis.

Data Science functions are predefined operations or methods used to process, analyze, and manipulate data, often implemented through libraries like Pandas, Numpy, and Scikit-learn.

The max() function returns the highest value from a given set of data or a list of values.

Data preparation in Data Science involves cleaning, organizing, and transforming raw data into a structured format suitable for analysis and modeling.

Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and irrelevant information from raw data to ensure its quality and accuracy for analysis.

Remove blank rows refers to the process of identifying and deleting rows in a dataset that contain no data or only empty values.

Data categories refer to the classification of data based on its characteristics, such as numerical, categorical, or temporal, to facilitate analysis and interpretation.

Data types define the kind of value a variable can hold, such as integers, floating-point numbers, strings, or boolean values.

Analyzing the data involves examining, interpreting, and deriving meaningful insights or patterns from the dataset.

Linear functions in Data Science represent relationships between variables where the change in one variable is proportional to the change in another.

A linear function with one explanatory variable models the relationship between an independent variable (explanatory) and a dependent variable, with a constant rate of change.

Plotting linear functions in Data Science involves graphing the relationship between an independent and dependent variable to visually represent their correlation.

The line is not fully drawn down to the y-axis because the data starts from a specific value of the independent variable (x), and the line represents the relationship between the variables within that range.

Slope and intercept define the relationship in a linear function, where the slope indicates the rate of change, and the intercept represents the starting value when the independent variable is zero.

The intercept is the value of the dependent variable (y) when the independent variable (x) equals zero.

Define the mathematical function in Python by using the slope, intercept, and input variable to calculate the output.

Intro to Statistics in Data Science involves using statistical methods to collect, analyze, interpret, and present data to make informed decisions.

Percentiles are values that divide a data set into 100 equal parts, helping to understand the distribution of the data.

Standard deviation is a measure of the amount of variation or dispersion in a set of values.

Variance is a measure of how far each number in a data set is from the mean, and thus from every other number in the set.

Step 1 to calculate the variance: Find the mean (average) of the data set.

You can use Python's var() function from NumPy to find the variance of the health_data variable.

A correlation matrix is a table showing correlation coefficients between variables, helping to identify relationships between them.

A heatmap is a data visualization tool that uses color gradients to represent the values in a matrix, helping to highlight patterns and correlations in data.

Correlation quantifies the relationship between variables, while causality determines if one directly influences the other.

Data Science - Linear Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

Linear regression with one explanatory variable models the relationship between a dependent variable and a single independent variable using a straight line.

Data Science - Regression Table is a summary table that presents key statistical values from a regression analysis, such as coefficients, p-values, R-squared, and standard errors, to assess the relationship between variables.

Data Science - Regression Table - Info refers to the key details and statistics from a regression analysis, including model information, coefficients, p-values, R-squared, and standard errors, which summarize the relationship between variables.

Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables, often for prediction purposes.

Data Science - Regression Table: P-Value indicates the probability that the observed relationship between variables occurred by chance, helping assess the statistical significance of the regression coefficients.

Hypothesis Testing is a statistical method used to determine whether there is enough evidence to support or reject a hypothesis about a population parameter.

Data Science - Regression Table: R-Squared is a statistical measure that indicates the proportion of the variance in the dependent variable that is explained by the independent variables in the regression model.

A Visual Example of a High R-Squared Value (0.79) shows a strong fit between the regression line and the data points, indicating that 79% of the variance in the dependent variable is explained by the independent variable.

Data Science - Linear Regression Case refers to a scenario where linear regression is used to model the relationship between a dependent variable and one or more independent variables, often for prediction or analysis.

Define the Linear Regression Function in Python by using libraries like statsmodels or scikit-learn to model the relationship between dependent and independent variables, typically through Ordinary Least Squares (OLS) regression.

Adjusted R-Squared is a modified version of R-Squared that adjusts for the number of explanatory variables in the model, providing a more accurate measure of the model's goodness of fit.