Sign In Start Free Trial

Book Overview & Buying
Table Of Contents

Principles of Data Science - Second Edition

By : Sinan Ozdemir, Kakade, Tibaldeschi

Principles of Data Science

By: Sinan Ozdemir, Kakade, Tibaldeschi

Overview of this book

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas. Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

1. How to Sound Like a Data Scientist

1. How to Sound Like a Data Scientist

What is data science?

The data science Venn diagram

Why Python?

Some more terminology

Data science case studies

Summary

2. Types of Data

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

3. The Five Steps of Data Science

Introduction to data science

Overview of the five steps

Exploring the data

Summary

4. Basic Mathematics

4. Basic Mathematics

Mathematics as a discipline

Basic symbols and terminology

Linear algebra

Summary

5. Impossible or Improbable - A Gentle Introduction to Probability

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

6. Advanced Probability

Collectively exhaustive events

Bayesian ideas revisited

Random variables

Summary

7. Basic Statistics

7. Basic Statistics

What are statistics?

How do we obtain and sample data?

8. Advanced Statistics

8. Advanced Statistics

Point estimates

Sampling distributions

Confidence intervals

Hypothesis tests

Summary

9. Communicating Data

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

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?

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

Naive Bayes classification

Decision trees

Unsupervised learning

k-means clustering

Choosing an optimal number for K and cluster validation

Summary

12. Beyond the Essentials

12. Beyond the Essentials

The bias/variance trade-off

K folds cross-validation

Grid searching

Ensembling techniques

Neural networks

Summary

13. Case Studies

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

14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service

14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service

Technical requirements

Technologies for machine learning projects

Configuring Azure Databricks

Training a text classifier with Azure Databricks

Azure Machine Learning

Summary

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review – let other readers know what you think

Index

Index

Case study 3 – Using TensorFlow

I would like to finish off our time together by looking at a somewhat more modern machine learning module called TensorFlow.

TensorFlow is an open source machine learning module that is used primarily for its simplified deep learning and neural network abilities. I would like to take some time to introduce the module and solve a few quick problems using TensorFlow. The syntax for TensorFlow (like PyBrain in Chapter 12, Beyond the Essentials) is a bit different than our normal scikit-learn syntax, so I will be going over it step by step. Let's start with some imports:

from sklearn import datasets, metrics 
import tensorflow as tf 
import numpy as np 
from sklearn.cross_validation import train_test_split 
%matplotlib inline

Our imports from sklearn include train_test_split, datasets, and metrics. We will be utilizing our train test splits to reduce overfitting, we will use datasets in order to import our iris classification data, and we'll use...

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

Tech Tools

Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

50+ new titles added per month and exclusive early access to books as they are being written.

Principles of Data Science

Search

Your notes and bookmarks