Learn to clean, analyze and present data with Excel, Google Sheets, SQL, Power BI, Looker Studio and Python, using one realistic sales dataset from your first formula to a portfolio project.
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Every lesson starts by telling you what you’ll be able to do.
Real-life cases from U.S. households and small businesses.
Check what you learned with immediate feedback.
Practice first, then compare with the solution.
4 levels · 16 lessons · 3h 23min of content
The four types of analytics, a data analyst's day, analyst vs. data scientist vs. engineer, the core toolkit and BLS career data.
Ask, prepare, process, analyze, share and act, applied step by step to a real question about a December sales spike.
Categorical vs. numerical data, data types in Excel, SQL and pandas, tidy data, wide vs. long tables and data dictionaries.
Tables, filters, SUMIFS, COUNTIFS, AVERAGEIFS, XLOOKUP, UNIQUE and FILTER, conditional formatting and Excel vs. Google Sheets.
Profile a raw file, remove duplicates, handle blanks, fix inconsistent text and dates, validate totals and keep a cleaning log.
Mean vs. median, skewed data, standard deviation, quartiles, IQR outliers and percentiles, with Excel formulas.
Build pivot tables in Excel and Google Sheets, cross-tabs, % of total, date grouping, slicers and common pivot mistakes.
Set up SQLite, import the practice data and write queries with filters, sorting, LIMIT, DISTINCT, IN, LIKE and calculated columns.
Aggregate functions, GROUP BY, WHERE vs. HAVING, query execution order, INNER and LEFT JOINs and gross profit by category.
Match the chart to the question, design rules for bar and line charts, color, takeaway titles and chart mistakes to avoid.
KPIs and layout, step-by-step builds in Power BI (with DAX measures) and Looker Studio, filters, sharing and a design checklist.
The big idea, what / so what / now what, headline titles, audience, a five-slide storyline and honest presentation of uncertainty.
Pearson correlation, why correlation is not causation, confounders, trend lines, moving averages and how to read an A/B test.
Load, clean, group, pivot, merge and chart the practice data with tested pandas code in Google Colab or Jupyter.
A complete year-end sales review: questions, cleaning log, analysis, dashboard, recommendations, README and a grading rubric.
Job titles, portfolio, résumé bullets that show results, LinkedIn, interview stages, SQL and STAR questions and a 90-day plan.
A practical data analytics course for beginners who want to think and work like a data analyst. Instead of toy examples, every lesson uses the same realistic dataset: 200 orders from a fictional U.S. retailer in 2025, with regions, states, products, channels and revenue in U.S. dollars. You start with the analysis process and the types of data, and by the end you have a complete project you can show to employers.
You'll learn to work in Excel and Google Sheets (SUMIFS, XLOOKUP, pivot tables), clean messy data (duplicates, blanks, inconsistent text and dates), calculate descriptive statistics, write SQL queries with GROUP BY, HAVING and JOINs, choose the right chart, build dashboards in Power BI and Looker Studio, present findings with data storytelling, read correlations and A/B tests, and repeat the whole workflow in Python with pandas. The final lessons walk you through a portfolio project and the steps to land your first data analyst job: résumé, portfolio and interviews. There are 16 lessons in four levels, with interactive quizzes and exercises with solutions, and every number in the lessons is calculated from the practice file.
Includes downloadable resources: a sales practice workbook (.xlsx) with raw data containing deliberate errors, a cleaned answer key, a product table for lookups and JOINs, a summary sheet with ready-made formulas and a data dictionary, plus CSV versions for SQL and Python.
Templates and practice data you’ll use throughout the course. Free, no sign-up.
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