In this chapter, we have discussed various data analysis processes, including KDD, SEMMA, and CRISP-DM. We then discussed the roles and skillsets of data analysts and data scientists. After that, we installed NumPy, SciPy, Pandas, Matplotlib, IPython, Jupyter Notebook, Anaconda, and Jupyter Lab, all of which we will be using in this book. Instead of installing all those modules, you can install Anaconda or Jupyter Lab, which has NumPy, Pandas, SciPy, and Scikit-learn built-in.
Then, we got a vector addition program working and learned how NumPy offers superior performance compared to the other libraries. We explored the available documentation and online resources. In addition, we discussed Jupyter Lab, Jupyter Notebook, and their features.
In the next chapter, Chapter 2, NumPy and Pandas, we will take a look at NumPy and Pandas under the hood and explore some of the fundamental concepts surrounding arrays and DataFrames.
