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  • Book Overview & Buying Principles of Data Science
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Principles of Data Science

Principles of Data Science - Second Edition

By : Sinan Ozdemir, Kakade, Tibaldeschi
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Principles of Data Science

Principles of Data Science

By: Sinan Ozdemir, Kakade, Tibaldeschi
Buy this Book

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.
Table of Contents (17 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
1. How to Sound Like a Data Scientist
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1. How to Sound Like a Data Scientist
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What is data science?
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The data science Venn diagram
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Why Python?
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Some more terminology
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Data science case studies
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Summary
2
2. Types of Data
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2. Types of Data
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Flavors of data
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Why look at these distinctions?
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Structured versus unstructured data
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Quantitative versus qualitative data
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The road thus far
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The four levels of data
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Data is in the eye of the beholder
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Summary
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3. The Five Steps of Data Science
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3. The Five Steps of Data Science
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Introduction to data science
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Overview of the five steps
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Exploring the data
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Summary
4
4. Basic Mathematics
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4. Basic Mathematics
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Mathematics as a discipline
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Basic symbols and terminology
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Linear algebra
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Summary
5
5. Impossible or Improbable - A Gentle Introduction to Probability
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5. Impossible or Improbable - A Gentle Introduction to Probability
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Basic definitions
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Probability
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Bayesian versus Frequentist
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Compound events
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Conditional probability
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The rules of probability
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A bit deeper
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Summary
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6. Advanced Probability
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6. Advanced Probability
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Collectively exhaustive events
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Bayesian ideas revisited
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Random variables
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Summary
7
7. Basic Statistics
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7. Basic Statistics
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What are statistics?
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How do we obtain and sample data?
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8. Advanced Statistics
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8. Advanced Statistics
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Point estimates
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Sampling distributions
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Confidence intervals
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Hypothesis tests
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Summary
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9. Communicating Data
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9. Communicating Data
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Why does communication matter?
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Identifying effective and ineffective visualizations
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When graphs and statistics lie
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Verbal communication
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The why/how/what strategy of presenting
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Summary
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10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
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10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
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What is machine learning?
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Machine learning isn't perfect
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How does machine learning work?
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Types of machine learning
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How does statistical modeling fit into all of this?
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Linear regression
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Logistic regression
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Probability, odds, and log odds
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Dummy variables
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Summary
11
11. Predictions Don't Grow on Trees - or Do They?
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11. Predictions Don't Grow on Trees - or Do They?
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Naive Bayes classification
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Decision trees
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Unsupervised learning
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k-means clustering
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Choosing an optimal number for K and cluster validation
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Summary
12
12. Beyond the Essentials
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12. Beyond the Essentials
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The bias/variance trade-off
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K folds cross-validation
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Grid searching
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Ensembling techniques
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Neural networks
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Summary
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13. Case Studies
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13. Case Studies
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Case study 1 – Predicting stock prices based on social media
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Case study 2 – Why do some people cheat on their spouses?
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Case study 3 – Using TensorFlow
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Summary
14
14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service
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14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service
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Technical requirements
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Technologies for machine learning projects
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Configuring Azure Databricks
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Training a text classifier with Azure Databricks
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Azure Machine Learning
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Summary
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Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review – let other readers know what you think
16
Index
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Index

Unsupervised learning

It's time to look at some examples of unsupervised learning, given that we spend the majority of this book on supervised learning models.

When to use unsupervised learning

There are many times when unsupervised learning can be appropriate. Some very common examples include the following:

  • There is no clear response variable. There is nothing that we are explicitly trying to predict or correlate to other variables.
  • To extract structure from data where no apparent structure/patterns exist (can be a supervised learning problem).
  • When an unsupervised concept called feature extraction is used. Feature extraction is the process of creating new features from existing ones. These new features can be even stronger than the original features.

The first tends to be the most common reason that data scientists choose to use unsupervised learning. This case arises frequently when we are working with data and we are not explicitly trying to predict any of the columns, and we merely...

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Tech Concepts
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Programming languages
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