Python and Data Science Training – Complete Tour

To work effectively with Python, you must understand how to manage independent modules. Installing and importing a module is like buying a new box of Legos: it gives you more building blocks. This allows you to extend Python’s capabilities — for example, adding the ability to process CSV files or spreadsheets.

Python also needs to interact with your computer. This means learning how to manage the operating system: setting the working directory from which data will be imported, for instance. Imported data — whether from files or from the web — is rarely perfect. It must be cleaned, prepared, and corrected. In a spreadsheet, you would use features like Find and Replace. In Python, you can achieve this and much more with the **regex** module.

In many industries, time is a central dimension of data (time series). The **datetime** module is essential and also introduces the foundations of data science, since time-based datasets are often the basis of forecasting models (machine learning). In fact, as soon as you begin cleaning and transforming data, you are already entering the world of data science, particularly with the **NumPy** and **Pandas** modules.

Additional topics are covered in the appendix, including an introduction to object-oriented programming to accelerate computations, **doctest** for documenting source code, and **MicroPython** for microcontrollers (a part of the Internet of Things). These will be reviewed briefly.