Introduction to Data Science · Lesson 1 of 1

Introduction to Data Science

Understand the data-science lifecycle, problem framing, tools, and responsible communication.

Lesson

1. Introduction

Data science combines domain questions, data, computation, and statistical reasoning to produce useful evidence. It is not simply machine learning: careful collection, definitions, analysis, and communication often matter more than a complex model.

2. Core concepts

A project moves from framing and acquisition to preparation, exploration, modeling when appropriate, evaluation, and delivery. Identify the unit of analysis and intended user early. A result should state its assumptions, data period, and limits so a reader can decide how to use it.

3. Topics covered in this module

What data science includes and how it differs from analytics
Lifecycle, stakeholder questions, data roles, and collaboration
VS Code, Jupyter, PyCharm, notebooks, scripts, and handbooks
Privacy, bias, reproducibility, and communication of uncertainty

4. Syntax and how to read it

question = 'Which factors are associated with late delivery?'
unit = 'one shipment'
measure = 'days_between_promised_and_actual'

Naming the question, unit, and measure before coding prevents a project from drifting into unrelated exploration.

5. Practical workflow

Turn a broad request into a measurable question, define the records needed and their grain, then write down how a successful answer changes a decision. Explore only approved data and retain the steps needed to reproduce the result.

6. Common mistakes

Starting with a favorite algorithm before clarifying the real question.
Presenting a sample result as a universal fact without describing its population.

7. Best practices

Keep a short decision brief, data dictionary, and reproducible analysis log.
Protect sensitive records and communicate uncertainty in plain language.

8. Practice task

A team asks you to “predict customer success.” Name two questions you must clarify before choosing data or a model.

9. One possible solution

Define what success means and over what time horizon; also identify the decision the prediction will support and which customers are in scope.

10. Summary

Understand the data-science lifecycle, problem framing, tools, and responsible communication. Keep a short decision brief, data dictionary, and reproducible analysis log. Protect sensitive records and communicate uncertainty in plain language.

11. What comes next

The next module, Understanding the Conda Environment, builds on this foundation and applies it to a new part of the analytics workflow.

Visual guide

A reliable Introduction to Data Science workflow

Step 01 Frame
Define the question, input scope, and intended output.
Step 02 Work
Apply the method and validate assumptions as you go.
Step 03 Check
Compare results with known cases and communicate limitations.

Code examples

Define an analysis contract

PYTHON

analysis = {
    'population': 'shipments delivered in 2026',
    'unit': 'shipment',
    'outcome': 'late_days',
}
print(analysis['unit'])

Expected output

shipment

Separate observation from explanation

TEXT

Observation: 12% of sampled shipments were late.\nInterpretation: lateness was more common in the winter sample.\nNext check: compare routes and sample sizes.