In the 21st century, data is vital for decision-making and developing long-term strategic plans. Python provides numerous libraries and built-in features that make it easy to support data analysis and processing. Making business decisions, forecasting weather, studying protein structures in biology, and designing a marketing campaign are all examples that require collecting data and then cleaning, processing, and visualizing it.
There are five main steps for data science processing.
- 1.
Data acquisition is where ...
