Data science for dummies / Lillian Pierson ; foreword by Jake Porway.

Format:

Book

Author/Creator:

Pierson, Lillian, author.

Contributor:

Porway, Jake, writer of foreword.

Series:

--For dummies.

For Dummies

Language:

English

Subjects (All):

Information retrieval.

Data mining.

Information technology.

Physical Description:

1 online resource (387 pages) : color illustrations.

Edition:

Second edition.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., 2017.

Summary:

Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.

Contents:

Getting Started With Data Science

Wrapping Your Head around Data Science

Exploring Data Engineering Pipelines and Infrastructure

Applying Data-Driven Insights to Business and Industry

Using Data Science to Extract Meaning from Your Data

Machine Learning: Learning from Data with your Machine

Math, Probability, and Statistical Modeling

Using Clustering to Subdivide Data

Modeling with Instances

Building models that Operate Internet-of-Things Devices

Creating Data Visualizations that Clearly Communicate Meaning

Following the Principles of Data Visualization Design

Using D3.js for Data Visualization

Web-Based Applications for Visualization Design

Exploring Best Practices in Dashboard Design

Making Maps from Spatial Data

Computing for Data Science

Using Python for Data Science

Using Open Source R for Data Science

Using SQL in Data Science

Doing Data Science with Excel and Knime

Applying Domain Expertise to Solve Real-World Problems Using Data Science

Data Science in Journalism: Nailing Down the Five Ws (and an H)

Delving into Environmental Data Science

Data Science for Driving Growth in E-Commerce

Using Data Science to Describe and Predict Criminal Activity

The Part of Tens

Ten Phenomenal Resources for Open Data

Ten Free Data Science Tools and Applications.

Notes:

Includes index.

Description based on print version record.

ISBN:

1-119-32764-4