Data Analysis with Python

We live in a world surrounded by data. But how do we observe and extract value from this data? How do we explore and begin to understand and visualize large data sets? Are there relationships in the data or unusual observations we can uncover? This course will help students learn how to perform data analysis using Python. We will acquire data, examine it, clean it up, visualize it, and begin to infer conclusions from it. We will walk through the basics of data analysis using the Python toolchain. These popular tools are open source and very popular among data scientists and analysts in both academia and industry. They include the Jupyter Notebook, pandas, plotting with matplotlib and seaborn, and some basics of machine learning using scikit-learn. We will explore categorical data that provides labels such as the make of a car or the web browser of a visitor to a website. We will also discuss charts, tables, and correlations and explore some color theory. Finally, we will dive into numerical data, including how to create, interpret, and plot data with multiple dimensions. Students will leave the course able to analyze a data set from start to finish, providing graphical and numerical summaries, correlations, and outliers.

Matt Harrison
Principal Consultant and Corporate Trainer, MetaSnake

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