Univariate Then Bivariate
From single columns to relationships.
Univariate Then Bivariate is a free Data Science Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Data Science Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
One Column, Then Two
Good EDA has an order. First understand each column alone, then study how pairs move together. Start univariate, finish bivariate. 📊
What Univariate Means
Looking at a single column on its own is univariate analysis. You ask what is typical and how spread out the values are.
Histograms Show Shape
For one numeric column, a histogram reveals its shape: bell, skewed, or lumpy. Shape hints at how to model it later.
df["age"].hist()Bar Charts for Categories
For a single label column, a bar chart of value_counts shows which categories dominate and which are rare.
df["plan"].value_counts().plot.bar()Center and Spread
Each numeric column has a center, like the mean, and a spread, like the standard deviation. Together they summarize it in two numbers.
Now Compare Two Columns
Once columns make sense alone, study pairs. Bivariate analysis asks whether two variables rise and fall together.
Number vs Number
For two numeric columns, a scatter plot shows whether they trend together, oppose, or scatter with no link at all.
df.plot.scatter(x="tenure", y="charges")Category vs Number
To compare a number across groups, group then summarize. A grouped mean shows how charges differ by plan in one line.
df.groupby("plan")["charges"].mean()Category vs Category
For two label columns, a crosstab counts every combination, so you can see which pairs appear together often.
pd.crosstab(df["plan"], df["churn"])Quantify the Link
A scatter hints, but a number confirms. The correlation between two numeric columns measures how tightly they move together.
df["tenure"].corr(df["charges"])Always Build Up in Order
Jumping to relationships before knowing each column hides traps. The univariate-first order keeps your conclusions trustworthy.
Quick Check
You want to see if two numeric columns move together.
Recap: Single Then Paired
You explore one column with histograms and bars, then pairs with scatters, groupings, and correlation. Order keeps insights honest. ✅
Frequently asked questions
Is the “Univariate Then Bivariate” lesson free?
Yes — the full text of “Univariate Then Bivariate” is free to read here on the web, and the Data Science Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Data Science Academy course, upgrade to CoddyKit PRO.
What will I learn in “Univariate Then Bivariate”?
From single columns to relationships. You practise Data Science Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Data Science Academy?
No prior experience is required. Data Science Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Univariate Then Bivariate” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Data Science Academy lesson?
Yes. Every Data Science Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Frame the Questions First
- Profile Every Column
- Univariate Then Bivariate
- Write Down What You Found