More and more organizations rely heavily on data for growth and everyday operations. Companies are investing in analytics projects to drive business decisions, develop new strategies and more. The demand for skilled data scientists is high in nearly every industry. However, the field is still comparatively young. Therefore, there's a significant shortage that struggles to meet the demand.

For that reason, many companies are upskilling existing employees, turning them into citizen data scientists with great tools and Python packages. But what are citizen data scientists, and what are Python packages?

Citizen Data Scientists: A Vital Role in a Data-Heavy World

A citizen data scientist completes data science work for an organization without the official title of "data scientist." These individuals don't have a formal background in analytics, statistics or any other related field. Therefore, they can't replace an official data scientist.

However, citizen data scientists augment the work of data scientists, allowing companies to expand their analytics capabilities. They are more knowledgeable about advanced analytics programs than other employees. As a result, citizen data scientists can often run machine learning algorithms, create models, employ statistical analysis techniques and more.

Businesses invest substantial time and money in creating citizen data scientists. While this role doesn't require a degree, there is some training and considerable education involved. Some companies will pay for boot camps, courses and certifications. Others will look for candidates with those qualifications outright, making them an attractive addition to one's resume.

What are Python Packages?

Data scientists must be proficient in Python, a high-level programming language for data analysis. Typically, an expert scientist will learn everything they need to know about Python during their years of formal education. But what about a citizen data scientist?

A Python package is a collection of tools that helps users initiate code. Packages typically contain scripts and modules. The purpose of using a package is to ensure code reusability. Instead of learning to write new Python code, you can use packages to maximize efficiency. It modularizes code, making it more accessible.

For a citizen data scientist, Python packages are essential to getting up to speed and augmenting the work of expert scientists.

Author Resource:-

Emily Clarke writes about business software and services like spreadsheets that automatically generate Python code and transform your data with AI etc. You can find her thoughts at Python data manipulation blog.