Summary

In this introductory chapter, we covered data analysis frameworks or process model, such as KDD, SEMMA, CRISP-DM, and standard process for data analysis. The job responsibilities and skill sets of data scientists, data engineers, ML engineers, data analysts, and NLP engineers were then covered. Next, we installed the packages that we will use throughout this book: NumPy, SciPy, Pandas, Matplotlib, IPython, Jupyter Notebook, Anaconda, Jupyter Lab, PyCharm, and VS Code. Installing Anaconda or Jupyter Lab, which comes with NumPy, Pandas, SciPy, and Scikit-learn integrated in, is a better option than installing all those modules. Next, we successfully implemented a vector addition application and discovered that NumPy performs better than the other libraries. We looked through the available documentation and learning resources on the nternet. We also talked about Pycharm, VS Code, Databricks, Jupyter Notebook, Jupyter Lab, and their features.

In the next chapter, Chapter 2, NumPy...