Data Science Fundamentals

Continuous data types can take on any value within a range, such as height or weight.

Discrete data types can only take on specific values, such as gender or occupation.

Data collection is the process of gathering data from various sources, such as surveys, experiments, or online databases.

Data preprocessing is the process of cleaning and preparing data for analysis, which includes removing errors, handling missing values, and normalizing data.

Data analysis is the process of extracting meaningful information from data, which includes using statistical methods, machine learning algorithms, and data visualization techniques.

Data visualization is the process of presenting data in a visual format, such as charts, graphs, and maps, to make it easier to understand and interpret.

Histogram, scatter plot, and bar chart are all common data visualization techniques used to present data in a visual format.

Machine learning is the process of using data to train algorithms to make predictions or decisions, without being explicitly programmed to do so.

Linear regression, decision tree, and support vector machine are all common machine learning algorithms used for various tasks such as prediction, classification, and regression.

Model evaluation is the process of assessing the performance of a machine learning model using metrics such as accuracy, precision, and recall.

Accuracy, precision, and recall are all common model evaluation metrics used to assess the performance of a machine learning model.

Model deployment is the process of deploying a machine learning model into production, which involves making the model available to end-users or other systems.

Python, R, and Tableau are all common data science tools used for data analysis, data visualization, and machine learning.

Data storytelling is the process of communicating the results of a data science project in a clear and concise manner, often using data visualization and narrative techniques.

Data scientists are responsible for collecting and analyzing data, building and deploying machine learning models, and communicating the results of data science projects.