Data Analysis Foundations

What you will be able to do

• Run any analysis through a repeatable process, from framing the question to sharing the insight.
• Turn a vague business request into a sharp, answerable question with clear success measures.
• Classify data by type and measurement scale (nominal, ordinal, interval, ratio) and pick analysis that fits.
• Tell structured, semi-structured, and unstructured data apart and explain what each is good for.
• Compare first-party, second-party, third-party, and open data sources and weigh their trade-offs.
• Judge a dataset against core data-quality dimensions and catch sampling or collection bias before it misleads you.
• Read descriptive statistics for center and spread to describe the shape of a dataset.
• Package findings into a clear, honest recommendation a non-technical stakeholder can act on.

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   The Data Analysis Process

   Good analysis is a process, not a lucky guess. This module maps the full journey from a business question to a shared insight, and shows exactly where the analyst adds value at each step of the ask, prepare, analyze, share, and act lifecycle. You will also learn to frame a question sharply enough that data can actually answer it, and to define what success looks like before you touch a single row.

   2 lessons · 5 quiz questions

2. 02

   Knowing Your Data

   You cannot analyze what you cannot categorize. This module hands you the vocabulary every analyst uses to size up a dataset before touching it. You will learn to tell qualitative from quantitative data and place any variable on one of the four measurement scales, then read the difference between structured, semi-structured, and unstructured formats such as tables, CSV, JSON, and free text. Get this right and you will know which analysis is valid and which is nonsense.

   2 lessons · 5 quiz questions · assignment

3. 03

   Where Data Comes From

   Data never arrives clean or neutral. This module surveys the sources an analyst pulls from, including databases, spreadsheets, APIs, surveys, and open data, and how each one shapes what you can honestly conclude. You will learn to check a dataset for quality and to catch bias before it quietly steers your results.

   2 lessons · 5 quiz questions

4. 04

   From Raw Data to Decisions

   This is where the analyst's job gets done: a messy export becomes a decision someone can act on. You will clean and explore a realistically messy dataset, describe its center and spread with the statistics that fit its shape, and find the story hiding in the numbers. Then you will turn that story into a clear, honest recommendation and communicate it so stakeholders move.

   2 lessons · 5 quiz questions · assignment