The tutorial starts by explaining the importance of linear algebra and statistics in data science and software engineering. It then introduces Python as a tool to apply statistical concepts computationally, specifically using NumPy and SciPy. The guide covers vectors, matrices, and NumPy's support for these data structures, including how they can be used to manipulate images and text data. The tutorial also touches on sentiment analysis using machine learning algorithms from scikit-learn, which relies on SciPy's data structures. Throughout the tutorial, Jupyter Notebooks are emphasized as an ideal environment to interact with code, and basic operations such as saving, adding, and running cells are reviewed.