data science vs data analytics ?

Data science is a broad field using advanced techniques (like ML/AI) to predict future outcomes and build models, answering "what will happen next?", while data analytics focuses on interpreting historical data to understand past trends, answering "what happened and why?" to support immediate business decisions, with analytics often being a subset of science, using tools like SQL/Excel for analysis versus science's focus on complex algorithms and coding. 

Data Analytics

• Goal: Understand past performance, identify trends, and answer specific business questions.

• Focus: Descriptive and diagnostic analysis of structured, historical data.

• Methods:

  Reporting, data visualization, querying (SQL), business intelligence tools (Tableau, Power BI)

  .

• Key Questions: "What happened?" or "Why did it happen?". 

Data Science

• Goal: Predict future outcomes, build new models, and drive innovation.

• Focus: Predictive and prescriptive analysis, often using complex algorithms on larger, unstructured datasets.

• Methods: Machine learning, deep learning, statistical modelling, programming (Python/R).

• Key Questions: "What will happen next?" or "What can we do about it?". 

Key Differences Summarized

• Scope: Science is broader (umbrella), analytics is a specific function within it.

• Techniques: Science uses ML/AI; analytics uses BI/reporting tools.

• Data: Science often handles unstructured; analytics focuses on structured data.

• Career: Data Science is typically more advanced, technical, and commands higher salaries, requiring deeper statistical and programming skills. 

In essence, a data scientist builds the predictive engine, while a data analyst uses that engine (or other tools) to tell the business what happened and what it means now, making analytics foundational to science.