Learn how to use data analytics in Python to derive insights from data.

Zero to Hero in Python for Data Science

This module will give us an introduction to Python-based data analytics. We will learn the inner workings of Python to the point where we can confidently find and manipulate data sources and use a Jupyter environment to derive insights from our data. By the end of this module, we will be able to find and clean data sources, plot datasets, and use Python functionalities to represent our data.

Data Analytics in Python (Projects)

There are two and a half things we’ll need to access the API successfully:

* BLS endpoint addresses matching the specific data series you need
* Python code to launch the request
* A `BLS API key` to unlock higher request rates (in case we need them)

To work with Python, as shown in the figure, we need to access the API.



## Getting the series endpoint addresses

For getting the series endpoint addresses, we may need to do some digging around in the BLS website, as shown in the figure. The following image shows what that looks like, including the endpoint codes - like `LNS11000000` for the Civilian Labor Force set.

# Getting the series endpoint addresses

For getting the series endpoint addresses, we may need to do some digging around in the BLS website, as shown in the figure. The following image shows what that looks like, including the endpoint codes - like `LNS11000000` for the Civilian Labor Force set.

Learn how to work with API and how to use the BLS library.

Comparing Wages With Consumer Price Index Data

Wages and CPI: Reality Check

Working With Major US Storm Data

Property Rights and Economic Development

How Representative Is Your Government?

Does Wealth Influence The Prevalence Of Mental Illness?

Do Birthdays Make Elite Athletes?

Does Literacy Impact The Income of People

Conclusion

Working with API

Things we need

Getting the series endpoint addresses