Book DetailAuthor/Editor(s): Lillian Pierson
Publication Date: March 9, 2015
Publisher: For Dummies
Size: 7.62 MB
Book DescriptionDiscover 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.
Excellent overview and how-to guide to data science (and some data engineering topics). A number of analytical techniques are discussed ranging from basic statistics and techniques of information visualization, to more specific and advanced methodologies for analyzing structured, semi-structured and unstructured data sets of various sizes and data velocities. Specific analytical techniques such as clustering, classification and K nearest neighbors are discussed. Programming languages and techniques are documented sufficiently to begin coding and select techniques which require further documentation. Data manipulation with Python and R, as well as Excel are described and some detailed examples are given. Sql data manipulation is also documented as are tools for Information Visualization. Finally applications ranging from environmental modeling to criminal activity prediction. In all cases as little jargon as possible is used and these complex topics are described in a way that an intelligent layman can at least understand there application, and those with some background in college statistics or information technology could actually implement solutions using these tools (if supplemented by their help files and documentation). A clear and respectable introduction to data science and some aspects of data engineering.
--Ira Laefsky, MS Engineering/MBA formerly on the Senior Consulting Staff of Arthur D. Little, Inc. and Digital Equipment Corporation