As you already know, Python has become one of the most popular, standard languages and is a complete package for data science-based operations. Python offers numerous libraries, such as NumPy, Pandas, SciPy, Scikit-Learn, Matplotlib, Seaborn, and Plotly. These libraries provide a complete ecosystem for data analysis that is used by data analysts, data scientists, and business analysts. Python also offers other features, such as flexibility, being easy to learn, faster development, a large active community, and the ability to work on complex numeric, scientific, and research applications. All these features make it the first choice for data analysis.
In this chapter, we will focus on various data analysis processes, such as KDD, SEMMA, and CRISP-DM. After this, we will provide a comparison between data analysis and data science, as well as the roles and different skillsets for data analysts and data scientists. Finally, we will shift our focus and start installing various Python libraries, IPython, Jupyter Lab, and Jupyter Notebook. We will also look at various advanced features of Jupyter Notebooks.
In this introductory chapter, we will cover the following topics:
- Understanding data analysis
- The standard process of data analysis
- The KDD process
- SEMMA
- CRISP-DM
- Comparing data analysis and data science
- The skillsets of data analysts and data scientists
- Installing Python 3
- Software used in this book
- Using IPython as a shell
- Using Jupyter Lab
- Using Jupyter Notebooks
- Advanced features of Jupyter Notebooks
Let's get started!
