Key components of data science
Posted: Fri Jul 12, 2024 11:19 am
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.
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.