Data Science Course

Data science is less about any one tool and more about a way of thinking: turning a pile of raw, messy numbers into an answer someone can actually act on. This course covers that thinking — the workflow, the statistics, and the judgment calls — using plain, dependency-free Python examples throughout.

What you'll learn

You'll start with what the field actually is and how the full workflow fits together, from a first question through to cleaning, exploring, and modeling data. From there you'll build real intuition for descriptive statistics, why the mean can quietly lie to you, how to pick an honest chart, and the difference between a correlation and a cause. The back half covers machine learning at a conceptual level — what "training" and "overfitting" actually mean, and why a model's accuracy number can be almost meaningless without more context — finishing with the most underrated skill in the field: explaining what you found to someone who wasn't in the room for the analysis.

This course deliberately stays library-agnostic. It's not a NumPy or pandas tutorial — it's the thinking those tools are built to support. Once these ideas are solid, picking up a specific library like the site's NumPy or Pandas courses becomes a much smaller step.

Lessons

  1. What Is Data Science?
  2. The Data Science Workflow
  3. Collecting & Cleaning Data
  4. Exploratory Data Analysis
  5. Descriptive Statistics
  6. Data Visualization Basics
  7. Correlation & Relationships
  8. Intro to Machine Learning Concepts
  9. Evaluating a Model
  10. Communicating Results
Where to start: begin with What Is Data Science? and go in order — the statistics lessons lean on the workflow covered early on, and the machine learning lessons near the end assume you're comfortable with the statistics before them.