“Data Computer Science” is not a standard term or field in the realm of computer science or data science. However, it’s possible that you may be referring to a combination of computer science and data science, where the two disciplines intersect. Here are some key points related to data and computer science:

1. Data Science and Computer Science Intersection: Data Science and Computer Science are closely related fields that often overlap in practice. Data Science involves extracting insights and knowledge from data using various techniques, while Computer Science encompasses the study of algorithms, data structures, software development, and computational theory.

2. Data-Driven Software Development: In modern software development, data plays a crucial role. Computer scientists often work on developing applications that involve data collection, processing, storage, and analysis. These applications may include data-driven web services, mobile apps, and more.

3. Machine Learning and AI: The intersection of computer science and data science is particularly pronounced in the fields of machine learning and artificial intelligence (AI). Computer scientists develop algorithms and models that are used in machine learning and AI applications for tasks such as image recognition, natural language processing, and recommendation systems.

4. Big Data: The management and processing of large datasets, often referred to as “big data,” require expertise in both computer science and data science. Computer scientists may work on designing distributed computing systems, while data scientists focus on extracting insights from the data.

5. Data Engineering: Data engineering is a field that bridges the gap between data science and computer science. Data engineers are responsible for building and maintaining data pipelines, databases, and data infrastructure, ensuring that data is available and accessible for analysis.

6. Data Visualization: Computer scientists often work on developing data visualization tools and techniques that help data scientists and analysts present data in a comprehensible and interactive manner.

7. Software for Data Analysis: Computer scientists may create software tools and platforms used by data scientists for data analysis and modeling. These tools facilitate data manipulation, statistical analysis, and machine learning.

8. Research and Academia: In academia, researchers often explore the intersection of computer science and data science, conducting studies and publishing papers on topics like data mining, data analytics, and computational methods for handling data.

9. Data Privacy and Security: Both computer scientists and data scientists are concerned with data privacy and security. They collaborate to develop secure data storage and transmission solutions and to address privacy concerns in data-driven applications.