Analytics Foundations & the Analyst's Mindset is a structured track of 12 lessons that build a complete, interview-ready understanding of the topic. Work through them in order, then use the quiz and flashcards in each lesson to revise.
What this course covers
- What Analytics Really Is: From Raw Data to a Decision - The data-to-decision chain, and why analytics is judged on decisions rather than dashboards.
- The Four Types of Analytics: Descriptive, Diagnostic, Predictive & Prescriptive - What question each type answers, with a worked example of all four on one dataset.
- Analytics vs Reporting vs Business Intelligence vs Data Science - Four terms used interchangeably in job posts, separated by what each actually delivers.
- The Data Roles Ecosystem: Analyst, Engineer, Scientist & Analytics Engineer - Who owns which part of the stack, and which role to target as a fresher.
- How a Data Team Actually Works: Requests, Sprints & Stakeholders - The weekly rhythm of an analytics team, and where a new analyst fits into it.
- Asking the Right Question Before Touching the Data - How to convert a vague business request into an answerable analytical question.
- Where Data Comes From: Events, Transactions, Surveys & Third-Party Sources - Each data source, what it is good for, and the bias each one carries.
- Data Types & Measurement Scales, and Why They Decide Your Test - Nominal to ratio scales, and how the scale limits which analysis is valid.
- Data Cleaning Fundamentals: Missing Values, Outliers & Duplicates - A repeatable cleaning sequence, and the decisions to document as you go.
- Exploratory Data Analysis: A Repeatable First Pass - The first twenty minutes with any dataset, done in a fixed order.
- Common Analytical Mistakes That Cost Analysts Credibility - The errors that get an analysis rejected, each with the check that prevents it.
- 100 Must-Know Analytics Terms - The Complete Interview Glossary - One hundred analytics terms defined in a line each, AI-era vocabulary included.