Chapter 1: Introduction

Overview

Data is everywhere - collected by our phones, websites, smart devices, and every click or scroll we make. In this first chapter, you'll learn what data science is, why it matters, and what it takes to do it well.

Data science is defined as the process of turning messy, real-world data into useful insights. You’ll explore what makes data science unique from just statistics or computer science, and how it’s used in everything from targeted ads to political campaigns. Through a humorous case study involving a fictional platform called DataSciencester, you'll also see what problems a data scientist might solve - like finding social influencers or predicting behavior based on network data.

This chapter sets the tone for the entire course: we’ll be learning by building tools from scratch, not relying on pre-built libraries. Why? Because creating our own tools forces us to deeply understand how algorithms work, how data structures behave, and what’s going on “under the hood” of data science platforms.

By the end of this chapter, you’ll understand the broad scope of data science, what skills it requires, and why it’s a powerful and in-demand field that touches almost every industry.

🔹 Key Concepts from Chapter 1

Data Science Defined
The art of extracting insight from messy, noisy, real-world data using programming, math, and domain expertise.
What Data Scientists Do
They analyze behavior, make predictions, personalize recommendations, conduct experiments, and build data-driven tools and models.
Real-World Examples
- OkCupid predicts relationship compatibility based on answers to strange but revealing questions.
- Facebook tracks your location to study migration and team fandom.
- Target analyzes purchases to predict pregnancy before customers tell them.
- Political campaigns (e.g., Obama 2012, Trump 2016) mine data to sway voters more effectively.
Data Scientist Case Study
You are hired to build data science tools for a social network for data scientists. Throughout the book, you'll solve challenges like identifying key influencers or designing experiments - building each tool yourself to understand how it works.

🧠 Key Terms to Know (Chapter 1)

Data Science: Extracting knowledge or insight from data.
Insight: Actionable understanding derived from analysis.
Connector: A highly networked or influential individual in a dataset (e.g., top influencers).
Reproducibility: Ensuring others can rerun your code and get the same results.
DataSciencester: A fictional social platform used to introduce common data science challenges in this course.

📖 Required Reading

Read: Chapter 1 – Introduction
From: Data Science from Scratch: First Principles with Python (2nd ed.) by Joel Grus (O’Reilly Media, 2019)