Data Science vs. Data Analytics

What's the Difference? Let's Break It Down!

1. The Basics

– Data Science: The big picture! It's about collecting, cleaning, and analyzing data to find patterns and predict the future. – Data Analytics: The detective work! It focuses on looking at data to find answers to specific questions.

2. Key Focus Areas

– Data Science: - Uses machine learning - Builds models to predict what might happen next. - Works with big, messy data! – Data Analytics: -Looks at trends and patterns. - Helps businesses solve problems. - Works with clean, organized data.

3. Skills Needed

– Data Scientists: - Coding (Python, R). - Math and stats knowledge. - Storytelling with data! – Data Analysts: - Excel, SQL (for databases). - Creating charts and dashboards. - Problem-solving.

4. Goal

– Data Science: Think long-term. It's about why and what's next? – Data Analytics: Think short-term. It's about what happened and how to fix it?

In Short

– Data Science = Inventing the future! – Data Analytics = Solving today’s puzzles!

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