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The starter track is free — read all lessons and practice for free. Full access to every track (current and future) is $14.99/month. Cancel anytime.

How long does each track take?

Each track is designed as a 30-day challenge — one lesson per day, about 15 minutes each. Go at your own pace, but the structure is built around daily consistency.

What's the lesson format?

Each lesson is a student-teacher dialogue with code examples, followed by a hands-on code challenge in an in-browser editor. You read, you understand, then you write real code.

Do I need prior coding experience?

Our beginner track starts from absolute zero — no prior experience needed. Advanced tracks build on earlier ones, and the platform tells you exactly where to start.

How is zuzu.codes different from freeCodeCamp or Codecademy?

zuzu.codes uses a structured 30-day track format with dialogue-based teaching, an in-browser code editor, and gamification (XP, streaks, progress tracking). The format builds genuine understanding through daily practice.

Week 1: First Steps for Researchers — Python For Researchers

Day 2 · ~11 min●

Reviewer 2 wants the mean age by treatment group recalculated with a stricter eligibility cutoff. You open SPSS. How long before the revised table is in your inbox?

If I remember the syntax — maybe an hour. If I have to re-click the menus, probably a weekend.

In Python, those values are variables. A named slot for each number, a formatted string for the table row, a comparison for the eligibility rule. Change the cutoff, rerun the script — the revised table generates itself.

So by Friday I can write code that spits out a formatted methods-section line without touching SPSS?

Exactly. Five short functions, each adding one layer. Day 3 formats one respondent label. Day 4 normalises a messy treatment-group name from the SurveyMonkey export. Day 5 builds the full statistics line with N and mean. Day 6 checks whether a respondent meets the pre-registered age minimum. Day 7 categorises an outcome score into three tiers. By Friday you have the raw pieces of the reproducible pipeline you'll finish in Week 4.

This week

Day 3 — store respondent ID and age as variables; format them as "Respondent R_042 — Age: 29.00"
Day 4 — clean a raw SurveyMonkey treatment-group label with strip(), lower(), and replace()
Day 5 — build a full stats line with N and mean outcome using f"{mean:.2f}"
Day 6 — compare age to the pre-registered minimum and return a bool flag
Day 7 — categorise an outcome score as "below threshold", "at threshold", or "above threshold" with if/elif/else

Goal: by Friday you can format one respondent row entirely in code.

Week 1

7 lessons this week

Day 2 · ~11 min●

Reviewer 2 wants the mean age by treatment group recalculated with a stricter eligibility cutoff. You open SPSS. How long before the revised table is in your inbox?

If I remember the syntax — maybe an hour. If I have to re-click the menus, probably a weekend.

So by Friday I can write code that spits out a formatted methods-section line without touching SPSS?

This week

Day 3 — store respondent ID and age as variables; format them as "Respondent R_042 — Age: 29.00"
Day 4 — clean a raw SurveyMonkey treatment-group label with strip(), lower(), and replace()
Day 5 — build a full stats line with N and mean outcome using f"{mean:.2f}"
Day 6 — compare age to the pre-registered minimum and return a bool flag
Day 7 — categorise an outcome score as "below threshold", "at threshold", or "above threshold" with if/elif/else

Goal: by Friday you can format one respondent row entirely in code.

This week

Day 3 — store respondent ID and age as variables; format them as "Respondent R_042 — Age: 29.00"

Day 4 — clean a raw SurveyMonkey treatment-group label with strip(), lower(), and replace()

Day 5 — build a full stats line with N and mean outcome using f"{mean:.2f}"

Day 6 — compare age to the pre-registered minimum and return a bool flag

Day 7 — categorise an outcome score as "below threshold", "at threshold", or "above threshold" with if/elif/else

Goal: by Friday you can format one respondent row entirely in code.

This week

Day 3 — store respondent ID and age as variables; format them as "Respondent R_042 — Age: 29.00"

Day 4 — clean a raw SurveyMonkey treatment-group label with strip(), lower(), and replace()

Day 5 — build a full stats line with N and mean outcome using f"{mean:.2f}"

Day 6 — compare age to the pre-registered minimum and return a bool flag

Day 7 — categorise an outcome score as "below threshold", "at threshold", or "above threshold" with if/elif/else

Goal: by Friday you can format one respondent row entirely in code.