Key components of data science

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Key components of data science include:

Data Collection: Gathering structured and unstructured data from various sources, such as databases, sensors, social media, etc.

Data Cleaning and Preprocessing: Ensuring data quality by handling missing values, removing duplicates, and transforming data into a suitable format for analysis.

Exploratory Data Analysis (EDA): Analyzing data through statistical summaries and visualizations to understand patterns, trends, and relationships.

Model Building and Machine Learning: Developing algorithms and statistical models to make predictions or decisions based on data. This often involves techniques like regression, classification, clustering, and deep learning.

Data Visualization and Communication: Presenting findings through visualizations (charts, graphs) and effectively communicating insights to stakeholders.

Deployment and Maintenance: Implementing models into production systems and continuously monitoring and updating them to ensure they remain accurate and relevant.