Data Visualization with Python Literacy (Beginner Level)

Topics covered

• Compare categorical data by category against continuous values using bar charts
• create a histogram to visualize the frequency counts of data in bins using bars
• create and configure simple graphs with lines and markers using the Matplotlib data visualization library
• create various basic line charts visualizing random data using Matplotlib and pyplot
• Create various special histograms, such as a histogram visualizing multiple columns
• describe what Seaborn is and how it relates to other data science libraries in Python
• generate histograms and pie charts to analyze distributions and create scatter plots to plot the relationship between two variables in a dataset
• list libraries that can be used in Python to implement data visualization
• load and explore the dataset used for visualization
• plot pie charts, box plots, and scatter plots using Pandas
• use Matplotlib to create box-and-whisker plots to display various statistics, such as the median, upper and lower quartiles and outliers
• use Matplotlib to use correlation heatmaps to visually represent covariate relationships
• use Matplotlib to visualize the relationship between two continuous variables using scatter plots