Beginner Level (Building Basic Skills)

Exercise 1: Basic Visualization Practice

  • Create a line plot showing temperature (tmpf) over time
  • Make a histogram of wind speeds (sknt)
  • Create a box plot comparing temperatures between weekdays and weekends
  • Bonus: Add proper titles, axis labels, and legends to your plots

Exercise 2: Temperature Analysis

  • Find the hottest and coldest days in the dataset
  • Calculate the average temperature for each day of the week
  • Create a bar chart showing average temperature by day of week
  • Question: Are weekends significantly warmer or cooler than weekdays?

Exercise 3: Simple Filtering Practice

  • Determine if any observations occur during nighttime.
  • Find all observations where it was both clear (skyc1 == 'CLR') AND windy (sknt > 15)
  • Count how many observations had temperatures above 70°F. What percentage of observations is this? What percentage of days contain a temperature above 70°F?
  • Challenge: Find the windiest clear day in the dataset

Intermediate Level (Data Analysis Skills)

Exercise 4: Seasonal Patterns

  • Extract the month from the valid_ET column and analyze seasonal patterns
  • Create a monthly breakdown of precipitation and cloud cover
  • Plot how average temperature changes month by month
  • Research Question: Is there a month that’s particularly cloudy or clear?

Exercise 5: Weather Condition Deep Dive

  • Analyze all sky condition codes (skyc1, skyc2, skyc3, skyc4)
  • Create a “cloudiness index” that combines all four sky condition layers
  • Compare your cloudiness index between weekdays and weekends

Exercise 6: Wind Analysis

  • Create a scatter plot of wind speed vs temperature
  • Analyze wind direction patterns (drct) - are certain directions more common?
  • Find correlations between wind speed and precipitation

Exercise 7: Time-Based Analysis

  • Analyze hourly patterns - extract hour from valid_ET
  • Are certain times of day more likely to be clear?
  • Do weather patterns differ between morning and afternoon observations?
  • Statistical Question: Is there a statistically significant difference?

Advanced Level (Research Projects)

Exercise 8: Pressure and Weather Prediction

  • Analyze the relationship between atmospheric pressure (alti, mslp) and weather conditions
  • Can you predict rain based on pressure changes?
  • Create a “pressure trend” by calculating differences between consecutive observations
  • Research Goal: Build a simple weather prediction model

Exercise 9: Comprehensive Weekend Analysis

  • Define your own “good weather” criteria (temperature range, clear skies, low wind, etc.)
  • Calculate percentage of “good weather days” by day of week
  • Perform a statistical test (chi-square) to determine if weekend differences are significant
  • Extension: How would changing your “good weather” definition affect results?

Exercise 10: Data Quality Investigation

  • Identify missing data patterns - are certain weather parameters missing more often?
  • Find potential data quality issues (impossible values, outliers)
  • Create a data quality report with recommendations
  • Professional Skill: Learn to critically evaluate data sources

Exercise 11: Comparative Analysis

  • Research weather data from a nearby location and compare patterns
  • Do other airports in Virginia show the same weekend effect?
  • Analyze whether this pattern holds for different seasons
  • Scientific Method: Attempt to replicate the findings