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Basic Python skill, statistics and probability concepts.
Understand why visualization is important in the practice of data analysis and know what distinguishes exploratory analysis from Explanatory analysis and the role of data visualization in each.
Interpret features in terms of level of measurement and know different encodings that can be used to depict data in visualizations.
Use bar charts to depict distributions of categorical variables.
Use scatterplots to depict relationships between numeric variables.
Use encodings like size, shape and color to encode values of a third variable in a visualization.
Understand what it means to tell a compelling story with data and choose the best plot type, encodings and annotations to polish your plots.
Apply your knowledge of data visualization to a dataset involving the characteristics of diamonds and their prices.
Real-world data rarely comes clean. Using Python, you’ll gather data from a variety of sources, assess its quality and tidiness, then clean it. You’ll document your wrangling efforts in a Jupyter Notebook, plus showcase them through analyses and visualizations using Python and SQL.
Data Scientist at Nerd Wallet
Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
Data Analyst Instructor
Mike is a content developer with a multidisciplinary academic background, including math, statistics, physics, and psychology. Previously, he worked on Udacity's Data Analyst Nanodegree program as a support lead.
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A well-prepared student should: