Three Data Science Projects in Three Days

A primer to get your portfolio started.

Working on our portfolios under a funky avocado sky.

A data science portfolio should contain a variety of projects. It deserves your time and attention to build one that showcases your unique talents and perspectives. There are several ways to code interesting models that can fill out your portfolio. Here are three simple ones to get the ball rolling.

Day One: Regression

Fit a simple linear regression model using the CO2 dataset in R:

# Load the CO2 dataset
data(CO2)

# Fit a linear regression model
model <- lm(conc ~ uptake, data = CO2)

# Print the model summary
summary(model)

In this example, we are fitting a simple linear regression model using the lm() function in R, with conc as the response variable and uptake as the predictor variable from the CO2 dataset. The data parameter specifies the dataset to use for the model. The summary() function is then used to print the model summary, which includes information such as coefficients, standard errors, t-statistics, p-values, and goodness-of-fit measures like R-squared.

The CO2 dataset contains measurements of CO2 uptake by a plant at different levels of light, temperature, and CO2 concentration. By fitting a linear regression model to this data, we can explore the…