You're reading from Principles of Data Science A beginner's guide to essential math and coding skills for data fluency and machine learning

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781837636303

Length 326 pages

Edition 3rd Edition

Languages

Python

Tools

MLOps

Concepts

Data Science

Author (1):

Sinan Ozdemir

View More author details

Table of Contents (18) Chapters

Preface

1. Chapter 1: Data Science Terminology

2. Chapter 2: Types of Data FREE CHAPTER

3. Chapter 3: The Five Steps of Data Science

4. Chapter 4: Basic Mathematics

5. Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability

6. Chapter 6: Advanced Probability

7. Chapter 7: What Are the Chances? An Introduction to Statistics

8. Chapter 8: Advanced Statistics

9. Chapter 9: Communicating Data

10. Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials

11. Chapter 11: Predictions Don’t Grow on Trees, or Do They?

12. Chapter 12: Introduction to Transfer Learning and Pre-Trained Models

13. Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift

14. Chapter 14: AI Governance

15. Chapter 15: Navigating Real-World Data Science Case Studies in Action

16. Index

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17. Other Books You May Enjoy

Identifying effective visualizations

The main goal of data visualization is to have the reader quickly digest the data, including possible trends, relationships, and more. Ideally, a reader will not have to spend more than 5-6 seconds digesting a single visualization. For this reason, we must take visuals very seriously and ensure that we are making a visual as effective as possible. Let’s look at five basic types of graphs: scatter plots, line graphs, bar charts, histograms, and box plots.

Scatter plots

A scatter plot is probably one of the simplest graphs to create. It is made by creating two quantitative axes and using data points to represent observations. The main goal of a scatter plot is to highlight relationships between two variables and, if possible, reveal a correlation.

For example, we can look at two variables: the average hours of TV watched in a day and a 0-100 scale of work performance (0 being very poor performance and 100 being excellent performance...