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.
Table of Contents (17 chapters)
16
Index

Summary

Between decision trees, Naïve Bayes classification, feature extraction, and k-means clustering, we have seen that machine learning goes way beyond the simplicity of linear and logistic regression, and can solve many types of complicated problems.

We also saw examples of both supervised and unsupervised learning and, in doing so, became familiar with many types of data science related problems.

In the next chapter, we will be looking at even more complicated learning algorithms, including artificial neural networks and ensembling techniques. We will also see and understand more complicated concepts in data science, including the bias-variance trade-off, as well as the concept of overfitting.