In both academic and business circles, you will hear people talking about the tools and technologies they use in their work. Depending on the environment and conditions, you might need to work with specific technologies. For example, if your company has already invested in SAS, you will need to carry out your project in the SAS development environment suited to your problem.
However, one of the advantages of NumPy is that it's open source, and it costs nothing for you to utilize it in your project. If you have already coded in Python, it's super easy to learn. If performance is your concern, you can easily embed C or Fortran code. Moreover, it will introduce you to a whole other set of libraries such as SciPy and Scikit-learn, which you can use to solve almost any problem.
Since data mining and predictive analytics became really important recently, roles like Data Scientist and Data Analyst are mentioned as the hottest jobs of the 21st century in many business journals such as Forbes, Bloomberg, and so on. People who need to work with data and do analysis, modeling, or forecasting should become familiar with NumPy's usage and its capabilities, as it will help you quickly prototype and test your ideas. If you are a working professional, your firm most probably wants to use data analysis methods in order to move one step ahead of its competitors. If they can better understand the data they have, they can understand the business better, and this will lead them to make better decisions. NumPy plays a critical role here as it is capable of performing wide range of operations and making your projects timewise efficient.
