The Growing Adoption of Python by Business Analysts
As data becomes increasingly vital to organizational success, Python is transitioning from being primarily a tool for data professionals to a valuable resource for business analysts and other non-technical users. The rise of data-driven decision-making has prompted professionals in various roles to seek more robust analytical capabilities beyond the limitations of traditional business intelligence (BI) tools. This shift presents a unique opportunity for those relatively new to Python to harness its power for more advanced analytics.
The demand for skilled data professionals has surged in many organizations, often outpacing the available resources and capabilities of existing data teams. Business analysts, who are tasked with interpreting and utilizing data to inform strategy, frequently encounter constraints in their analytical tools. By integrating Python into their skill set, these analysts can perform complex analyses, create custom data visualizations, and leverage machine learning techniques without solely relying on the data science team.
Python’s popularity has skyrocketed in recent years, making it one of the most sought-after programming languages. According to a recent survey of over 20,000 developers, Python ranked just behind JavaScript in terms of usage, gaining 3.3 million new users within a six-month period to reach a total of 15.7 million worldwide. This growing community contributes to the language’s evolving ecosystem, providing ample resources for learners and practitioners alike.
In addition to its accessibility, Python’s extensive library ecosystem offers tools specifically designed to simplify advanced analytics for business analysts. Libraries like NumPy enable efficient numerical computations, while Prophet provides straightforward methods for time series forecasting. Furthermore, geospatial analysis has been enhanced by projects like H3, originally developed by Uber, allowing analysts to manipulate location-based data with ease. These resources empower analysts to tackle a broader range of data challenges, ultimately driving better business outcomes.