Part 1 | Exploratory Data Analysis
// start by understanding your data //Making decisions with data involves understanding what your variables actually contain. Before exploring relationships you need to know whether you're working with categories, counts, or measurements, and what patterns, outliers, and limitations exist in your data. Data is easiest to understand when visualized appropriately and the appropriate data visualization depends on the data type. Part 1 indroduces approaches for summarizing data with figures and tables using python and spreadsheets, stills that will save you countless hours.
Part 1.0 ~ Data Types
Classify variables as categorical (binary, nominal, ordinal) or numerical (discrete, continuous). Choose appropriate summary methods and visualizations based on variable type.
Concept 1.0 // Variable Types and Data Structures
Variables are either categorical or numerical
Exercise 1.0 // Categorical & Numerical Variables
Synthetic data and code to summarize categorical and numerical variables
Part 1.1 ~ Categorical Variables
Use Bar charts to show all three types of categorical variables. Use Pie charts rarely and generally only for binary categorical variables.
Exercise 1.1 // Coffee Shop Locations
The locations of a small (fictional) coffee shop chain
Part 1.2 ~ Numerical Variables
Use Histograms to show distributions. Use Boxplots+Stripplots to show quartiles and precise values.
Concept 1.2 (pt. 1) // Numerical Variables with Histograms
Summarizing numerical variables with Histograms
Exercise 1.2 (pt. 1) // Starbucks Customers
Create Histograms of Starbucks customers (from Kaggle)
Concept 1.2 (pt. 2) // Numerical Variables with Boxplots+Stripplots
Summarizing numerical variables
Exercise 1.2 (pt. 2) // Create a Boxplot+Stripplot of coffee consumption per capita
Data on coffee consumption per capita in 2019
Part 1.3 ~ Timeseries
Match the visualization choice to the data structure.
Concept 1.3 pt.1 // Timeseries and Linegraphs
Visualizing relationships between data points
Exercise 1.3 pt.1 // Timeseries and Linegraphs
Three univariate data structures: cross-sectional, timeseries, and panel
Concept + Exercise 1.3 pt.2 // Seasonality and Boxplots
Visualizing subtrends by season
Homework 1.3
Due on Friday September 12 at 5PM on Gradescope
Part 1.4 ~ Panel Data
Long vs wide format; grouped box plots compare across categories; scatterplots for changes over time.
Concept 1.4 pt.1 // Panel Data in Long Format
Visualizing continuous variables by category
Exercise 1.4 // Transactions by Shop Location
Coffee shop transaction data by location from Kaggle
Concept 1.4 pt.2 // Panel Data in Wide Format
Visualizing continuous variables by category
Concept 1.4 pt.2 cont. // Panel Data in Wide Format
Visualizing continuous variables by category
Exercise 1.4 // Coffee Consumption by Country Through Time
How has coffee consumption changed since 1999?
Homework 1.4
Due on Friday September 12 at 5PM on Gradescope
Part 1.5 ~ Bivariate Relationships
Use scatter plots to visualize relationships between two numerical variables.
Exercise 1.5 // Coffee Production and GDP
Data on countries' coffee production and GDP
Part 1.6 ~ Geographic Data and Wrap Up
Use maps to show geographic differences.
Exercise 1.6 // US Cities and Temperature
US cities on the map and their average temperatures
MiniExam
MiniExam 1 will be held on September 22 and covers everything in Part 1 (except Part 1.6 Geographic Data). You will begin to learn that if you understand the concepts and do the work in the examples and homework, you're going to be in good shape on the MiniExam.
MiniExams focuses on practical application of the concepts we've developed. Practice with Homework and Exercises to prepare effectively.