The path from no experience to a first data analyst job, in plain language, for absolute beginners. This 22-part series starts with what the job really is, then builds the working skills in order: spreadsheets, SQL, cleaning and exploring data, statistics you can trust, charts and dashboards, pandas in Python, the metrics that matter, and finally the portfolio and interview prep that land the role. Every term is defined the first time it appears, and each part assumes only the ones before it.
Series in progress · 14 of 22 published
Phase 1 · Foundations
- 01What a Data Analyst Really Does, vs Data Scientist and Data Engineer
- 02The Analyst Toolkit and a Skills Roadmap
- 03How to Think in Data: Questions, Metrics, Hypotheses
- 04Spreadsheets That Do Real Work: Formulas and Pivot Tables
- 05How Data Is Stored: Tables, Types, CSVs and Databases
Phase 2 · SQL and wrangling data
- 06SQL Part 1: SELECT, WHERE, ORDER BY
- 07SQL Part 2: Joining Tables
- 08SQL Part 3: GROUP BY, Aggregations and Window Functions
- 09Cleaning Messy Data
- 10Exploratory Data Analysis: Finding the Story
Phase 3 · Statistics and visuals
- 11Statistics an Analyst Actually Uses
- 12Correlation, Causation and the Traps Between
- 13Choosing the Right Chart and Avoiding Misleading Ones
- 14Your First Dashboard: Power BI or Looker Studio
Phase 4 · Python and metrics
- 15Python for Analysts: pandas Essentials
- 16Plotting in Python
- 17Metrics and KPIs: Defining What Matters
- 18A/B Tests and Experiments Explained Simply
Phase 5 · Communication and career
- 19Telling the Story: Presenting to Stakeholders
- 20Data Modeling Basics: How Analytics Data Is Organized
- 21Ethics, Privacy and Bias in Analysis
- 22Building a Portfolio and Landing the First Role
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