What is difference between Data Science and Big Data?

Data Science:​

1. Definition: Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
2. Scope : Encompasses a broad range of data-related activities, including data analysis, predictive modeling, machine learning, and data visualization.
3. Focus : Primarily concerned with extracting meaningful information and insights from data to inform decision-making, solve problems, and create predictive models.
4. Tools and Techniques : Utilizes statistical analysis, machine learning algorithms, data mining, and visualization tools. Common languages and tools include Python, R, SQL, and libraries like TensorFlow, Pandas, and Scikit-Learn.
5. Outcome : Generates actionable insights, predictive models, and visualizations to support business strategies and decisions.

Big Data:​

1. Definition : Big Data refers to large, complex datasets that are difficult to process using traditional data processing tools and methods due to their volume, velocity, and variety.
2. Scope : Focuses on the technologies and techniques required to store, process, and analyze massive amounts of data. This includes data that is continuously generated in real-time from various sources such as social media, sensors, transactions, and logs.
3. Focus : Concerned with handling and processing large-scale data efficiently and effectively, often involving the infrastructure and architecture needed to manage such data.
4. Tools and Techniques : Utilizes distributed computing frameworks and storage solutions like Hadoop, Apache Spark, NoSQL databases (eg, MongoDB, Cassandra), and cloud services (eg, AWS, Google Cloud, Azure).
5. Outcome : Enables the processing and analysis of massive datasets to uncover trends, patterns, and insights that can drive business decisions and operational performances.