Data Science For Dummies / Pierson, Lillian.

Format:

Book

Author/Creator:

Pierson, Lillian, author.

Series:

For dummies

Language:

English

Subjects (All):

Information technology.

Databases.

Data mining.

Physical Description:

1 online resource (408 pages)

Edition:

1st edition

Place of Publication:

For Dummies, 2015.

System Details:

text file

Summary:

Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there - let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

Contents:

Part I, Getting started with data science: Wrapping your head around data science

Exploring data engineering pipelines and infrastructure

Applying data science to business and industry

Part II, Using data science to extract meaning from your data: Introducing probability and statistics

Clustering and classification

Clustering and classification with nearest neighbor algorithms

Mathematical modeling in data science

Modeling spatial data with statistics

Part III, Creating data visualizations that clearly communicate: 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

Part IV, Computing for data science: Using python for data science

Using open source R for data science

Using SQL in data science

Software applications for data science

Part V, Applying domain expertise to solve real-world problems: Using data science in journalism

Delving into environmental data science

Data science for driving growth in e-commerce

Using data science to describe and predict criminal activity

Part VI, The part of tens: Ten phenomenal resources for open data

Ten (or so) free data science tools and applications.

Notes:

Online resource; Title from title page (viewed March 9, 2015)

Includes index.

ISBN:

9781118841525

1118841522

OCLC:

910165517