Book Image

Principles of Data Science - Second Edition

By : Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi
Book Image

Principles of Data Science - Second Edition

By: Sinan Ozdemir, Sunil Kakade, Marco 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.

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Principles of Data Science - Second Edition
Sinan Ozdemir | Sunil Kakade | Marco Tibaldeschi
Dec, 2018 | 424 pages
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Table of Contents (17 chapters)
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
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
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
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
4. Basic Mathematics
4. Basic Mathematics
Mathematics as a discipline
Basic symbols and terminology
Linear algebra
Summary
5
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
6. Advanced Probability
6. Advanced Probability
Collectively exhaustive events
Bayesian ideas revisited
Random variables
Summary
7
7. Basic Statistics
7. Basic Statistics
What are statistics?
How do we obtain and sample data?
8
8. Advanced Statistics
8. Advanced Statistics
Point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Summary
9
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
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
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
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
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
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
15
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review – let other readers know what you think
16
Index
Index

Point estimates

Recall that, in the previous chapter, we mentioned how difficult it is to obtain a population parameter; so, we had to use sample data to calculate a statistic that was an estimate of a parameter. When we make these estimates, we call them point estimates.

A point estimate is an estimate of a population parameter based on sample data.

We use point estimates to estimate population means, variances, and other statistics. To obtain these estimates, we simply apply the function that we wish to measure for our population to a sample of the data. For example, suppose there is a company of 9,000 employees and we are interested in ascertaining the average length of breaks taken by employees in a single day. As we probably cannot ask every single person, we will take a sample of the 9,000 people and take a mean of the sample. This sample mean will be our point estimate.

The following code is broken into three parts:

  • We will use the probability distribution, known as the Poisson distribution...
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