Data Analytics vs Data science

I was watching a video to improve my domain knowledge, and it was really helpful for me. So, I decided to share a summary of it with you for a quick overview.

Data science and data analytics are distinct fields; data science encompasses broader tasks, while data analytics focuses on interpreting existing data.

• Data science and data analytics are often confused but are not the same.
• Data science is an umbrella term for tasks like data mining and machine learning.
• Data analytics specializes in querying, interpreting, and visualizing datasets.
• Data science follows a lifecycle with seven phases, including data cleaning and predictive modeling.
• Data scientists need skills in machine learning, coding, and big data platforms.
• Data analysts use various analytics types to derive actionable insights from data.
• Data science is broader, while data analytics answers specific questions with existing data.

Understanding the difference between data science and data analytics is essential for those considering a career in either field. This clarity helps in choosing the right career path.

Data science acts as an overarching framework that includes various methodologies, highlighting its versatility in handling complex data problems.

While data analytics is a specialization within data science, it emphasizes the importance of interpreting and visualizing data for decision-making.

The data science lifecycle is iterative, promoting continuous improvement through hypothesis testing and experimentation, which is crucial for accurate outcomes.

Data scientists require a robust skill set, including programming in languages like Python and R, as well as familiarity with big data technologies, making it a technical role.

Data analysts focus on specific analytics techniques, such as predictive and prescriptive analytics, to extract meaningful insights from data, making it more application-oriented.

Data science and analytics both play vital roles in business operations, ensuring efficient inventory management and informed decision-making, ultimately benefiting the organization.