All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
Enter the value with up to 1 decimal. If the found value is 40.123, enter only 40.1.
Enter the value with up to 1 decimal. If the found value is 40.123, enter only 40.1.
Enter the value with up to 1 decimal. If the found value is 40.123, enter only 40.1.
Use the series temp_max and temp_min to create a new series temp_range that contains the difference in temperature between the Max and the Min for each day.
The humidity range is important for understanding the variability in moisture levels throughout the day. It helps in assessing how much the humidity fluctuates, which can impact comfort and weather patterns.
It's calculated using the formula:
Humidity Range = Humidity_Max - Humidity_Min
Where:
Humidity_Max is the maximum humidity of the day as a percentage.Humidity_Min is the minimum humidity of the day as a percentage.This formula provides a simple measure of the daily range in humidity levels.
Create the new series humidity_range by combining the series humidity_max and humidity_min.
The wind speed range is important for understanding the variability in wind conditions throughout the day. It helps in assessing how much the wind speed fluctuates, which can impact weather conditions and perceived temperature.
It's calculated using the formula:
Wind Speed Range = Wind_Speed_Max - Wind_Speed_Min
Where:
Wind_Speed_Max is the maximum wind speed of the day in kilometers per hour.Wind_Speed_Min is the minimum wind speed of the day in kilometers per hour.This formula provides a simple measure of the daily range in wind speeds.
Create the new series wind_speed_range by combining the series wind_speed_max and wind_speed_min.
Normalization is a common preprocessing step in data science. Normalize the chlorophyll series by subtracting the mean of the series and dividing by the standard deviation. The resulting series should be chlorophyll_normalized.
The formula to normalize it is:
chlorophyll_normalized = (chlorophyll - AVG(chlorophyll)) / STD DEV(chlorophyll)
The "density_anomaly" is an important concept in oceanography for understanding how variations in seawater density affect ocean circulation and stratification.
Typically, density is calculated using a complex formula involving temperature, salinity, and pressure. For this exercise, we will approximate the density anomaly using a simplified formula:
density_anomaly = 1000 - (0.2 * sea_temperature) + (0.8 * salinity)
This formula subtracts a weighted combination of sea_temperature and salinity from a baseline density value of 1000 kg/m³. This approximation helps in grasping the basic concept of how temperature and salinity influence seawater density.
The wind chill index is important for understanding the perceived temperature on the human body under cold and windy conditions. It takes into account the effect of wind speed in making the air feel colder than the actual temperature.
It's calculated using the formula:
Wind Chill = 13.12 + 0.6215 * Temp_Min - 11.37 * (Wind_Speed_Max ^ 0.16) + 0.3965 * Temp_Min * (Wind_Speed_Max ^ 0.16)
Where:
Temp_Min is the minimum temperature of the day in degrees Celsius.Wind_Speed_Max is the maximum wind speed of the day in kilometers per hour.Create the new series wind_chill_index by combining the series temp_min and wind_speed_max.
The ocean nutrient index is important for assessing the availability of key nutrients that support phytoplankton growth, which forms the base of the marine food web.
It's calculated using the formula:
Ocean Nutrient Index = (Phosphate + Silicate + Nitrito+Nitrato) / 3
Where:
Phosphate is the concentration of phosphate in µM.Silicate is the concentration of silicate in µM.Nitrito+Nitrato is the combined concentration of nitrite and nitrate in µM.This index provides a simplified measure of the overall nutrient availability in the ocean.
Create the new series ocean_nutrient_index by combining the series phosphate, silicate, and nitrite_nitrate.
The humidity comfort index is important for understanding how the combination of temperature and humidity affects human comfort. High humidity combined with high temperatures can make conditions feel much hotter and more uncomfortable.
It's calculated using the formula:
Humidity Comfort Index = Temp_Avg - (0.55 - 0.0055 * Humidity_Avg) * (Temp_Avg - 14.5)
Where:
Temp_Avg is the average temperature in degrees Celsius.Humidity_Avg is the average humidity as a percentage.This index gives an adjusted temperature value that reflects how the temperature feels when accounting for humidity.
Create the new series humidity_comfort_index by combining the series humidity_avg and temp_avg.
The storm potential index is important for evaluating the likelihood of storm conditions by combining key meteorological factors. High wind speeds, humidity, and temperature are often associated with stormy weather.
It's calculated using the formula:
Storm Potential Index = (Wind_Speed_Max * Humidity_Max * Temp_Max) / 1000
Where:
Wind_Speed_Max is the maximum wind speed of the day in kilometers per hour.Humidity_Max is the maximum humidity of the day as a percentage.Temp_Max is the maximum temperature of the day in degrees Celsius.This formula provides a scaled index that reflects the potential for stormy conditions by considering the interaction of these factors.
Create the new series storm_potential_index by combining the series wind_speed_max, humidity_max, and temp_max.
The comfort_temp_diff_ratio is important for understanding how fluctuations in daily temperature impact perceived human comfort, as indicated by the humidity comfort index.
It's calculated using the formula:
Comfort Temp Diff Ratio = Humidity_Comfort_Index / temp_range
Where:
Humidity_Comfort_Index reflects perceived comfort based on average temperature and humidity.temp_range is the difference between the maximum and minimum temperature for each day.This ratio helps assess how daily temperature changes influence overall comfort.
Create the new series comfort_temp_diff_ratio by combining the series humidity_comfort_index and temp_range.
The storm_wind_correlation is important for exploring how variability in wind speed might amplify or dampen the potential for storm conditions.
It's calculated using the formula:
Storm Wind Correlation = Storm_Potential_Index * Wind_Speed_Range
Where:
Storm_Potential_Index is a scaled index reflecting the potential for storm conditions based on maximum wind speed, humidity, and temperature.Wind_Speed_Range is the difference between the maximum and minimum wind speeds for each day.This product helps assess the relationship between wind variability and storm potential.
Create the new series storm_wind_correlation by combining the series storm_potential_index and wind_speed_range.
