Language : English
Published : 2019-07-15
Pages : 448
Illustrated Microsoft Office 365 and Excel 2019 Comprehensive
Now students can master the nuances of Microsoft® Office quickly with ILLUSTRATED MICROSOFT® OFFICE 365 & EXCEL 2019 COMPREHENSIVE, part of today’s popular Illustrated Series. This focused, user-friendly approach uses a proven two-page layout that allows students to work through an entire task without turning the page. Clear Learning Outcomes outline the skills for each lesson, while large full-color screen images reflect exactly what students see on their own computers. Each module begins with a brief overview of the principles covered in the lesson and introduces a real-world case scenario to engage students and reinforce critical skills to make them successful in their educational and professional careers. In addition, MindTap and updated SAM (Skills Assessment Manager) online resources are available to guide additional study and ensure successful results.
The world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. With this new edition, Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. They walk you through the detailed steps of designing, developing, and deploying a data warehousing/business intelligence system. With substantial new and updated content, this second edition again sets the standard in data warehousing for the next decade.
About the Author
The authors’ professional careers have followed remarkably similar paths. Each author has focused on data warehousing and business intelligence (DW/BI) consulting and education for more than fifteen years. Most worked together at Metaphor Computer Systems, a pioneering decision support vendor, in the 1980s. All the authors are members of the Kimball Group and teach for KimballUniversity. They contribute regularly to Intelligent Enterprise magazine and other industry publications; most have previously written books in the Toolkit series. Ralph Kimball founded the Kimball Group. Since the mid 1980s, he has been the DW/BI industry’s thought leader on the dimensional approach and trained more than 10,000 IT professionals. Ralph has his Ph.D. in Electrical Engineering from Stanford University. Margy Ross is President of the Kimball Group. She has focused exclusively on DW/BI since 1982 with an emphasis on business requirements analysis and dimensional modeling. Margy graduated with a BS in Industrial Engineering from Northwestern University. Warren Thornthwaite began his DW/BI career in 1980. After managing Metaphor’s consulting organization, he worked for Stanford University and WebTV. Warren holds a BAin Communications Studies from the University of Michigan and anMBA from the University of Pennsylvania’sWharton School. JoyMundy has focused onDW/BIsystems since 1992 with stints at Stanford, Web TV, and Microsoft’s SQL Server product development organization. Joy graduated from Tufts University with a BA in Economics, and from Stanford University with an MS in Engineering Economic Systems. Bob Becker has helped clients across a variety of industries with their DW/BI challenges and solutions since 1989, including extensive work with health care organizations. Bob has a BSB in Marketing from the University of Minnesota’s School of Business.
For undergraduate or graduate courses in IT Strategy or Management. Using IT to deliver business value. IT Strategy: Issues and Practices provides a critical issues perspective that shows students how to use and manage IT to deliver business value. This edition has been overhauled in order to reflect the most important issues facing IT managers today.
- Chapter 1. Is There a Security Problem in Computing?
- Chapter 2. Elementary Cryptography
- Chapter 4. Protection in General-Purpose Operating Systems
- Chapter 5. Designing Trusted Operating Systems
- Chapter 7. Security in Networks
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.