It's crucial for modern companies to have clean, easy-to-understand data to inform business direction and measure outcomes. Data literacy and organization allow for better decision-making, faster interpretation, and more widespread comprehension throughout an organization. Being able to collect, manage, understand, contemplate, and communicate with data will separate those who experience change to those who drive it.
In this course, you will learn how to harness the power of Python to gain highly coveted skill in data analysis and visualization. We’ll cover how to use standard packages for the organization, analysis, and visualization of data, such as Numpy, Scipy, Matplotlib, and Scikit-Learn. You’ll get to apply these skills on a daily basis and, at the end, produce a substantial project showcasing your new abilities as a data analyst.
At the end of this course students will:
- Be familiar with the standard data analysis tools of Python.
- Know how to visualize their data, whether processed or not, so that they can communicate its relevance to those with and without the appropriate domain knowledge.
- Understand how to clean data and prune for quality without losing depth of meaning (or at least be able to adequately explain information loss) such that any analysis using that data isn't hampered by regular or irregular artifacts.
- Be equipped to attack small to mid-size data sets with one of the most popular modern programming languages, empowering them with the agency to handle mass amount of data that the world generates daily.
- Be able to back up claims based in data with solid reporting and data-driven analyses, adding to the legitimacy of their work and the credibility of their decisions.
- Complete a deep dive and thorough analysis of a publicly-available data set. This will be done in class as a group project. It should include at least three meaningful figures describing their data and relevant to their analysis, along with the hypotheses (if there were any) that drove them to that analysis, and any insightful conclusions they may have gained from their analysis (even if it was a null result).
Students enrolling in this coures should:
- Know arithmetic and basic algebra
- Have seen a mathematical matrix (even if it's not yet understood)
- Understand Git and GitHub
- Have a basic understanding of Python:
- Built-in Python data structures and functions (list, tuple, dict, int, float, string, len, sum, min, max)
- Performing basic mathematical operations in Python (with and without math from the standard library)
- Writing custom functions and classes
- Importing packages
- Running scripts
- Reading/writing files
- Installing new packages