Many people were asking me if Python is useful if they are not a data scientist or not working with data projects, and my answer is YES, it is extremely useful and can help analysts do the work that were time-consuming or limited before.
1. Replace Excel: If you spend hours working with Excel, doing repetitive analysis, waiting for your data to open, or the file easily crushed by doing some lookups, then Python will be your best friend. Python can quickly replace Excel, as one of the most popular languages for business analytics, it can be learned easily. Excel isn’t scalable for modern business, it was built for a world where datasets were small and real-time information wasn’t needed.
2. Data visualization: Python can also be used to describe and categorize the data, such as exploratory data analysis, which includes profiling the data and visualizing results.
3. Prediction: Excel has limited build-in functions to do predictive analysis. For example, Excel’s solver and Data Analysis can only do few selected models where data need to be labeled exactly the way the function is built. Python has no such limitations and you can easily create models such as Bayesian networks, decision trees, and much more.