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Python for Researchers — How Far Have You Come? — Python For Researchers

Day 30 · ~11 min●

Thirty days ago you rated yourself on six skills. Today you rate yourself again — same questions, same scale. Then you see the gap.

I ran the capstone this morning. One function: took a list of respondent dicts, filtered to the pre-registered subset, computed stats per treatment group. Reviewer 2 can ask for any specification they want.

That function pulled in everything — f-strings for the output, loops for the wave iteration, dicts for the codebook lookup, sorted key for the ranking, try/except for the messy missing-data rows. Thirty days of small wins composed into one reproducible pipeline.

I want to see the delta against Day 1.

Same six rows. Answer honestly — the chart shows both ratings side by side. That gap is yours to cite in a methods section: "Analysis was conducted using a reproducible Python pipeline."

I didn't think I'd be saying that 30 days ago.

What you built

30 days, 20 code lessons, 1 reproducible analysis pipeline.

From first variable to full pipeline

Week 1 — Variables, f-strings, booleans, if/elif — labelling and filtering a single respondent
Week 2 — Lists, for loops, dicts, nested structures — iterating 300 respondents across 5 waves
Week 3 — Functions, defaults, CSV/JSON parsing — turning a one-off into a reusable co-author-friendly script
Week 4 — Comprehensions, sorted key, error handling, capstone — the full analysis pipeline that regenerates every table

What the chart shows

Column	Meaning
Left bar	Day 1 self-rating
Right bar	Today's rating
Gap	Real, measurable growth

Ahead: automation-for-researchers starts where this track ends. The first thing you meet there is a live Gmail connection — your code reads real messages from your inbox.

Self-Assessment

0/6

Not at allSlightlyModeratelyVeryExtremely

1.Now I can write a Python script that computes and prints summary statistics from a list of respondent dicts.

2.Now I can read a collaborator's Python analysis script and suggest improvements or spot bugs.

3.Now I can find and fix bugs in a data pipeline using error messages and test cases.

4.Now I can load a CSV of survey responses, filter by treatment group, and compute descriptive stats.

5.Now I can automate a repetitive analysis task so the next wave of data runs through the same pipeline without manual steps.

6.Now I can explain a Python analysis function to a non-technical co-author or a methods reviewer.

Day 30 · ~11 min●

Thirty days ago you rated yourself on six skills. Today you rate yourself again — same questions, same scale. Then you see the gap.

I want to see the delta against Day 1.

Same six rows. Answer honestly — the chart shows both ratings side by side. That gap is yours to cite in a methods section: "Analysis was conducted using a reproducible Python pipeline."

I didn't think I'd be saying that 30 days ago.

What you built

30 days, 20 code lessons, 1 reproducible analysis pipeline.

From first variable to full pipeline

Week 1 — Variables, f-strings, booleans, if/elif — labelling and filtering a single respondent
Week 2 — Lists, for loops, dicts, nested structures — iterating 300 respondents across 5 waves
Week 3 — Functions, defaults, CSV/JSON parsing — turning a one-off into a reusable co-author-friendly script
Week 4 — Comprehensions, sorted key, error handling, capstone — the full analysis pipeline that regenerates every table

What the chart shows

Column	Meaning
Left bar	Day 1 self-rating
Right bar	Today's rating
Gap	Real, measurable growth

Ahead: automation-for-researchers starts where this track ends. The first thing you meet there is a live Gmail connection — your code reads real messages from your inbox.

What you built

30 days, 20 code lessons, 1 reproducible analysis pipeline.

From first variable to full pipeline

Week 1 — Variables, f-strings, booleans, if/elif — labelling and filtering a single respondent

Week 2 — Lists, for loops, dicts, nested structures — iterating 300 respondents across 5 waves

Week 3 — Functions, defaults, CSV/JSON parsing — turning a one-off into a reusable co-author-friendly script

Week 4 — Comprehensions, sorted key, error handling, capstone — the full analysis pipeline that regenerates every table

What the chart shows

Column	Meaning
Left bar	Day 1 self-rating
Right bar	Today's rating
Gap	Real, measurable growth

Ahead: automation-for-researchers starts where this track ends. The first thing you meet there is a live Gmail connection — your code reads real messages from your inbox.

What you built

30 days, 20 code lessons, 1 reproducible analysis pipeline.

From first variable to full pipeline

Week 1 — Variables, f-strings, booleans, if/elif — labelling and filtering a single respondent

Week 2 — Lists, for loops, dicts, nested structures — iterating 300 respondents across 5 waves

Week 3 — Functions, defaults, CSV/JSON parsing — turning a one-off into a reusable co-author-friendly script

Week 4 — Comprehensions, sorted key, error handling, capstone — the full analysis pipeline that regenerates every table

What the chart shows

Column	Meaning
Left bar	Day 1 self-rating
Right bar	Today's rating
Gap	Real, measurable growth

Ahead: automation-for-researchers starts where this track ends. The first thing you meet there is a live Gmail connection — your code reads real messages from your inbox.