Introduction¶
Welcome to the official EasyPass Coding Crashcourse!
Conceived in the late 1980s, Python has since become an essential tool for many programmers, engineers, researchers, and data scientists across academia and industry. As a business student, Python will most likely be helpful for extracting meaning from large astronomical datasets, scraping and munging data sources from the Web, or automating day-to-day research tasks.
The appeal of Python is in its simplicity and beauty, as well as the convenience of the large ecosystem of domain-specific tools that have been built on top of it. For example, most of the Python code in scientific computing and data science is built around a group of mature and useful packages:
- NumPy provides efficient storage and computation for multi-dimensional data arrays.
- SciPy contains a wide array of numerical tools such as numerical integration and interpolation.
- Pandas provides a DataFrame object along with a powerful set of methods to manipulate, filter, group, and transform data.
- Matplotlib provides a useful interface for creation of publication-quality plots and figures.
- Scikit-Learn provides a uniform toolkit for applying common machine learning algorithms to data.
- IPython/Jupyter provides an enhanced terminal and an interactive notebook environment that is useful for exploratory analysis, as well as creation of interactive, executable documents. For example, the manuscript for this report was composed entirely in Jupyter notebooks.
No less important are the numerous other tools and packages which accompany these: if there is a scientific or data analysis task you want to perform, chances are someone has written a package that will do it for you.
To tap into the power of this ecosystem, however, first requires familiarity with the Python language itself.
As such, this report in no way aims to be a comprehensive introduction to programming, or a full introduction to the Python language itself. We'll start of by building a solid foundation of Phyton's core syntax and semantics, after which we'll take a deep dive into the core principles of data science and machine learning.
With that, let's start our tour of the Python language.