Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

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 (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Some more terminology


This is a good time to define some more vocabulary. By this point, you're probably excitedly looking up a lot of data science material and seeing words and phrases I haven't used yet. Here are some common terminologies you are likely to come across:

  • Machine learning: This refers to giving computers the ability to learn from data without explicit "rules" being given by a programmer.

    We have seen the concept of machine learning earlier in this chapter as the union of someone who has both coding and math skills. Here, we are attempting to formalize this definition. Machine learning combines the power of computers with intelligent learning algorithms in order to automate the discovery of relationships in data and create of powerful data models. Speaking of data models, we will concern ourselves with the following two basic types of data models:

  • Probabilistic model: This refers to using probability to find a relationship between elements that includes a degree of randomness.

  • Statistical model: This refers to taking advantage of statistical theorems to formalize relationships between data elements in a (usually) simple mathematical formula.

While both the statistical and probabilistic models can be run on computers and might be considered machine learning in that regard, we will keep these definitions separate as machine learning algorithms generally attempt to learn relationships in different ways.

We will take a look at the statistical and probabilistic models in the later chapters.

  • Exploratory data analysis (EDA) refers to preparing data in order to standardize results and gain quick insights.

    EDA is concerned with data visualization and preparation. This is where we turn unorganized data into organized data and also clean up missing/incorrect data points. During EDA, we will create many types of plots and use these plots to identify key features and relationships to exploit in our data models.

  • Data mining is the process of finding relationships between elements of data.

    Data mining is the part of data science where we try to find relationships between variables (think spawn-recruit model).

  • I tried pretty hard not to use the term big data up until now. This is because I think this term is misused, a lot. While the definition of this word varies from person, big data. Big Data is data that is too large to be processed by a single machine (if your laptop crashed, it might be suffering from a case of big data).

    The state of data science (so far). This diagram is incomplete and is meant for visualization purposes only.