A data analyst turns messy data into clear answers that help a team decide what to do. Day to day you pull data with SQL, clean it, analyze it with Excel or Python, build charts and dashboards, and explain what you found to people who are not technical. The O*NET data for this field stresses math, critical thinking, writing and speaking, so the job is as much about asking the right question and communicating the answer as about code. You do not need a degree to start: a portfolio of real projects, strong SQL and a clear way of explaining results get you interviews. This roadmap moves you from
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Stage 1 of 4 · about 12 weeks
Learn spreadsheets (formulas, pivot tables, lookups), basic statistics (mean, median, standard deviation, distributions, correlation) and SQL SELECT, WHERE, GROUP BY and JOIN. Finish by answering 10 business questions on a public dataset and writing a one-paragraph finding for each.
Spreadsheets for analysis
Almost every business team lives in spreadsheets. Analysts use them to check data quickly, share results with non-technical people, and sanity-check what SQL or Python produces. Strong spreadsheet skills are tested in many entry-level interviews.
Basic statistics
Statistics is how you tell a real pattern from noise and describe data honestly. Every report you write uses averages, spreads and comparisons, and stakeholders trust analysts who explain them correctly.
SQL fundamentals
Company data lives in databases, not spreadsheets. SQL is the most requested skill in data analyst job postings, and almost every interview includes a SQL screen.
Turning questions into findings
Employers hire analysts to answer questions, not to run queries. The task list for this job includes identifying business problems, presenting results, and recommending actions. Writing a clear finding is the skill that makes your numbers matter.
Then 3 more stages (about 68 weeks in all) with lessons, drills, projects, 10 interview questions with strong answers and free resources.