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Learn Data Analysis

Turn raw tables into clear answers people can act on.

Free to start. Built from a single prompt. No credit card.

Data analysis is the work of answering a specific question with data: which products sell best by region, why sign-ups dropped last month, whether a change actually helped. The process runs from framing the question, to finding and cleaning the data, to summarizing and comparing groups, to charting the result and writing a recommendation. The core tools are spreadsheets, SQL and often Python or a BI tool like Tableau or Power BI. Learnr builds a personalized data analysis course from one prompt and teaches each stage through short interactive lessons grounded in realistic business datasets.

How to learn Data Analysis, step by step

Practice framing the question first. Rewrite requests like "look into churn" as "what share of customers who joined in March cancelled within 60 days, by plan type?" A precise question tells you which data, filters and comparison you need.
Get strong at spreadsheets. Learn sorting, filtering, SUMIFS, COUNTIFS, lookups and pivot tables in Excel or Google Sheets. Spreadsheets are where most analysts start and where many quick questions are still answered fastest.
Learn SQL for real datasets. Master SELECT, WHERE, GROUP BY, HAVING, JOINs, CASE expressions and then window functions like ROW_NUMBER and running totals. Practice on a sample database with several related tables so joins and duplicates become familiar.
Clean data like a skeptic. Check row counts before and after every join, look for duplicates, nulls, impossible values and inconsistent category labels, and write down every cleaning decision. Small cleaning errors quietly change conclusions.
Compare groups with the right summaries. Use medians when data is skewed, report counts alongside rates, and watch for Simpson's paradox, where a trend in every subgroup reverses when the groups are combined. Short Learnr lessons help these statistical traps stick.
Present findings for a busy reader. Lead with the answer, support it with one or two clean charts, and state limitations plainly. Build a small portfolio of written analyses on public data, each answering one clear question.

What you'll learn

Turn a vague request into a precise, answerable analysis question
Clean and reshape data in spreadsheets, SQL or pandas
Summarize and compare groups with aggregates and descriptive statistics
Choose charts that make one finding obvious
Write a short findings summary with a clear recommendation and caveats

What's inside your Data Analysis course

A sample path Learnr builds for Data Analysis. Your real course adapts to your goal and level.

1. The analysis process from question to answer
2. Spreadsheets for cleaning and quick summaries
3. SQL for querying and joining tables
4. Descriptive statistics and comparing groups
5. Visualizing and presenting findings
6. A complete analysis project from start to finish

How long does it take to learn Data Analysis?

Core skills in spreadsheets, SQL, basic statistics and charting take about 2 to 5 months at 5 to 8 hours a week. Becoming job-ready with a small portfolio typically takes 4 to 9 months, faster if you already work with numbers in your current role.

What you need before you start

Basic arithmetic, percentages and comfort using a computer. No coding experience is needed, since spreadsheets and SQL are taught from the beginning.

Common mistakes to avoid

Starting to query before defining the question, which produces many charts and no answer.
Joining tables without checking for duplicate keys, so totals silently double or triple. Compare row counts before and after each join.
Reporting an average change without the sample size or spread, leaving readers unable to judge whether the difference is meaningful.

How Learnr builds your course

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Data Analysis questions, answered

Do I need to learn Python for data analysis?

Not at first. Spreadsheets and SQL handle a large share of analyst work. Python with pandas becomes valuable for automation, larger files and more advanced statistics once the basics are solid.

What is the difference between data analysis and data science?

Data analysis focuses on answering questions about what happened and why, mostly with SQL, spreadsheets, statistics and dashboards. Data science overlaps but leans further into predictive modeling, experimentation and programming.