Data Science vs Data Analytics.

    Data science and data analytics are both fields that work with data to gain insights, but they have different focuses and objectives:
    Data science
    A broad field that includes data analytics and other areas like machine learning and data engineering. Data scientists use statistical and computational methods to build models that can predict future outcomes. They also develop algorithms, create data structures, and build systems to improve how data is collected and analyzed.
    Data analytics
    A more focused field that's often part of the larger data science process. Data analysts examine large datasets to identify trends, develop charts, and create visual presentations. They use tools like SQL, R, Power BI, and Tableau to organize and clean data, and share their findings.
    Data scientists and analysts both work with data, but each role requires a slightly different set of skills and tools. Many skills involved in data science build on those used by data analysts.
    Data analysts typically need at least a bachelor's degree in a field like mathematics, statistics, computer science, or finance. Data scientists typically have a master's or doctoral degree in data science, information technology, mathematics, or statistics.

---