You're reading from Principles of Data Science Mathematical techniques and theory to succeed in data-driven industries

Product type Paperback

Published in Dec 2016

Publisher Packt

ISBN-13 9781785887918

Length 388 pages

Edition 1st Edition

Languages

Python

Concepts

Data Science

Author (1):

Sinan Ozdemir

View More author details

Table of Contents (15) Chapters

Preface

What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support

1. How to Sound Like a Data Scientist FREE CHAPTER

What is data science?
The data science Venn diagram
Some more terminology
Data science case studies
Summary

2. Types of Data

Flavors of data
Why look at these distinctions?
Structured versus unstructured data
Quantitative versus qualitative data
The road thus far…
The four levels of data
Data is in the eye of the beholder
Summary

3. The Five Steps of Data Science

Introduction to data science
Overview of the five steps
Explore the data
Summary

4. Basic Mathematics

Mathematics as a discipline
Basic symbols and terminology
Linear algebra
Summary

5. Impossible or Improbable – A Gentle Introduction to Probability

Basic definitions
Probability
Bayesian versus Frequentist
Compound events
Conditional probability
The rules of probability
A bit deeper
Summary

6. Advanced Probability

Collectively exhaustive events
Bayesian ideas revisited
Random variables
Summary

7. Basic Statistics

What are statistics?
How do we obtain and sample data?
Sampling data
How do we measure statistics?
The Empirical rule
Summary

8. Advanced Statistics

Point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Summary

9. Communicating Data

Why does communication matter?
Identifying effective and ineffective visualizations
When graphs and statistics lie
Verbal communication
The why/how/what strategy of presenting
Summary

10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials

What is machine learning?
Machine learning isn't perfect
How does machine learning work?
Types of machine learning
How does statistical modeling fit into all of this?
Linear regression
Logistic regression
Probability, odds, and log odds
Dummy variables
Summary

11. Predictions Don't Grow on Trees – or Do They?

Naïve Bayes classification
Decision trees
Unsupervised learning
K-means clustering
Choosing an optimal number for K and cluster validation
Feature extraction and principal component analysis
Summary

12. Beyond the Essentials

The bias variance tradeoff
K folds cross-validation
Grid searching
Ensembling techniques
Neural networks
Summary

13. Case Studies

Case study 1 – predicting stock prices based on social media
Case study 2 – why do some people cheat on their spouses?
Case study 3 – using tensorflow
Summary

Index

Chapter 13. Case Studies

In this chapter, we will take a look at a few case studies to help you develop a better understanding of the topics we've seen so far.

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Chapter 13. Case Studies

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Table of Contents (15) Chapters