Skill Path: Data Analysis & Visualization with Python
Data analysis and data visualization are essential skills for data professionals. Python offers some great tools for analyzing data and creating different kinds of visuals to recognize patterns in data and create insights. This path introduces pandas and NumPy, the two most popular open-source Python libraries for data analysis. We'll also learn the fundamentals of data visualization in Python by exploring graph interpretation (statistics) and tooling (Matplotlib and Seaborn).
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Learn Data Analysis & Visualization with Python
Here's what you'll learn to do.
Dataquest Skill Paths teach you job-ready skills that can be immediately applied to your current or future data roles and projects.
- How to use the Python libraries, pandas, and NumPy for data analysis
- The most common data visualization techniques to explore data
- Using data visualization to convey insights and tell a story
Course Structure:
Data Analysis & Visualization with Python
Learn the fundamentals of Python programming and data science.
Learn how to use Jupyter Notebook and build a portfolio project.
Learn important Python data science skills, how to analyze text data, how to work with dates and times, and object-oriented programming.
Learn how vectorized operations make working with data faster, selecting data by value using boolean indexing and using pandas and NumPy methods to analyze data.
Learn how to visualize time series data with line plots, visualizing frequency distributions with bar plots and histograms, and how to speed up your exploratory data visualization workflow using Pandas.
Learn how to better create graphs for an audience by using information design principles, creating visual patterns using Gestalt principles, and using Matplotlib built-in styles — case study on the FiveThirtyEight style.
