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In this chapter, we are going to learn how to define variables in Python and what are the main data types.
In this chapter, we are going to look at programming concepts, such as working with loops, writing custom functions, and dealing with errors.
We will introduce two main packages for working with data. We are going to see how we can use NumPy to perform linear algebra calculations and how to perform data manipulations on tabular data using Pandas.
Making plots from the raw data is a powerful and somewhat underestimated tool that could give you a better understanding of what is going on in your data and some interesting insights. In this chapter we will look at the main plot types and how to create them in Python using Matplotlib and Seaborn packages.
Now that we know how to work with data it's time to learn how to actually import the data from external sources and files into Python. We will see how to load files of different formats, such as txt, csv, mat, and how to create API requests.
We will look at how to perform statistical analysis such as ANOVA or Chi-squared test using mainly scipy.stats module and pingouin package.
Miscellaneous examples of other awesome stuff you could do with Python to deal with "real-world" problems.
