Principles of Data Science

Learn the techniques and math you need to start making sense of your data

Principles of Data Science

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Sinan Ozdemir

4 customer reviews
Learn the techniques and math you need to start making sense of your data
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Book Details

ISBN 139781785887918
Paperback388 pages

Book Description

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.

With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

Table of Contents

Chapter 1: How to Sound Like a Data Scientist
What is data science?
The data science Venn diagram
Some more terminology
Data science case studies
Summary
Chapter 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
Chapter 3: The Five Steps of Data Science
Introduction to data science
Overview of the five steps
Explore the data
Summary
Chapter 4: Basic Mathematics
Mathematics as a discipline
Basic symbols and terminology
Linear algebra
Summary
Chapter 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
Chapter 6: Advanced Probability
Collectively exhaustive events
Bayesian ideas revisited
Random variables
Summary
Chapter 7: Basic Statistics
What are statistics?
How do we obtain and sample data?
Sampling data
How do we measure statistics?
The Empirical rule
Summary
Chapter 8: Advanced Statistics
Point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Summary
Chapter 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
Chapter 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
Chapter 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
Chapter 12: Beyond the Essentials
The bias variance tradeoff
K folds cross-validation
Grid searching
Ensembling techniques
Neural networks
Summary
Chapter 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

What You Will Learn

  • Get to know the five most important steps of data science
  • Use your data intelligently and learn how to handle it with care
  • Bridge the gap between mathematics and programming
  • Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
  • Build and evaluate baseline machine learning models
  • Explore the most effective metrics to determine the success of your machine learning models
  • Create data visualizations that communicate actionable insights
  • Read and apply machine learning concepts to your problems and make actual predictions

Authors

Table of Contents

Chapter 1: How to Sound Like a Data Scientist
What is data science?
The data science Venn diagram
Some more terminology
Data science case studies
Summary
Chapter 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
Chapter 3: The Five Steps of Data Science
Introduction to data science
Overview of the five steps
Explore the data
Summary
Chapter 4: Basic Mathematics
Mathematics as a discipline
Basic symbols and terminology
Linear algebra
Summary
Chapter 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
Chapter 6: Advanced Probability
Collectively exhaustive events
Bayesian ideas revisited
Random variables
Summary
Chapter 7: Basic Statistics
What are statistics?
How do we obtain and sample data?
Sampling data
How do we measure statistics?
The Empirical rule
Summary
Chapter 8: Advanced Statistics
Point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Summary
Chapter 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
Chapter 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
Chapter 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
Chapter 12: Beyond the Essentials
The bias variance tradeoff
K folds cross-validation
Grid searching
Ensembling techniques
Neural networks
Summary
Chapter 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

Book Details

ISBN 139781785887918
Paperback388 pages
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From 4 reviews

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