This path is for BI analysts or data scientists looking to build job-ready Python skills and master the most popular libraries for data analysis and visualization.

We'll start by mastering the core building blocks of Python for analytics, including data types, properties, and foundational tools like variables, numeric and string operators, conditional logic, loops and functions.

Next we'll dive into NumPy & Pandas, two of the most popular Python packages for data analysis. We'll introduce arrays and array properties, common operations like indexing, slicing, filtering and sorting, and powerful methods for exploring, analyzing, aggregating and transforming dataframes.

From there we'll explore data visualization methods using Matplotlib & Seaborn. We'll introduce data visualization frameworks and best practices, review tools and techniques for building and customizing basic charts, then explore advanced formatting options and custom visuals.

Last but not least we'll use Plotly & Dash to build and deploy interactive visuals, dashboards, and web applications.

This path is designed to help you learn job-ready skills, solve real business problems, and build a project portfolio to showcase your skills to peers and employers.

WHO SHOULD TAKE THIS PATH?

• Data Analysts or BI professionals looking to build expert-level Python skills

• Aspiring data scientists who want to learn Python for data analysis and visualization

• Students looking for a hands-on, project-based learning experience

WHAT ARE THE PATH REQUIREMENTS?

• Jupyter Notebooks (free download, we'll walk through the install)

• No advance preparation is required (basic familiarity with programming is a plus, but not a prerequisite)