Cloud computing enhances data science workflows by providing cost efficiency, scalability, and security. It eliminates the need for expensive on-premise servers, allowing teams to pay only for the resources they use. Additionally, it streamlines workflows by consolidating databases and workloads, which reduces errors and improves productivity.

Cloud computing offers significant scalability advantages. Unlike traditional setups that require purchasing and installing additional servers, cloud solutions allow teams to quickly increase or decrease their capacity based on demand. This flexibility ensures that organizations can efficiently manage their resources as data volumes grow, which are projected to exceed 180 zettabytes by 2025.

Despite concerns about cybersecurity, cloud computing can enhance security for data science applications. Many cloud providers offer advanced security features that may be too costly for in-house implementation, such as automated compliance and encrypted backups. Additionally, most security breaches stem from human error rather than technical flaws in the cloud, making it a reliable option for data protection.