Goals and Organization of the Book

This book constitutes an introduction to data science as we have conceptualized it at the Rockefeller College of Public Affairs and Policy, University at Albany. The College has a long tradition teaching data science and modeling in our Masters in Public Administration, and many smart colleagues have influenced our current class on data science that we call “Data, Models and Decisions.” It would be very difficult to mention all people influencing this curriculum through the years, but I can say that the main structure of this book is most recently influenced by myself, and my colleagues Erika Martin and Mikhail Ivonchyk.

The book goals are consistent to the goals in our class, which are to prepare students to manage quantitative data and develop computer-based decision models to make evidence-based decisions in the public and nonprofit sectors. This overall goal involves three major areas:

1. Knowledge of data science—Students will learn about the data production cycle, data models, issues that influence data quality and usability, the role of public policy analysis in decision-making, and the conceptual framework underlying different types of policy analysis techniques.
2. Skills of a data scientists—Students will be able to use Excel and Access to organize, manage, and analyze data for decision-making including: probabilistic decision trees, making decisions with multiple criteria, optimization, database construction, and sensitivity analyses.
3. Traits of a data scientists —Assignments and course activities will help students develop important traits necessary to making evidence-based decisions and informing policy debates: examining complex policy problems from multiple perspectives, presenting results from decision models to a variety of audiences, team work, creativity, curiosity, attention to detail, and becoming critical data consumers.

Understanding how to use data for decision making and policy analysis is a core goal of data science applications in management and public policy, and although most current approaches emphasize on data exploration and discovery, we find much value in approaches that start with a model that is used as a framework to organize and understand the data. In this way, the book includes two main parts. The first part, including the first 7 chapters and tutorials is related to data exploration, visualization and management. The second part, including 6 more chapters, is a primer of decision models and their application in public policy and management. Decision modeling tools included in the second part of the book include Decision Trees, Multi-Atribute Decision Models, Optimization and Simulation Techniques.

More specifically, the first chapter is a quick introduction to think about data for decision making within organizations. As we reflect on how data can be used to produce knowledge and understanding, we also discuss some important concepts of data quality and data management. The next two chapters of the book are included as prerequisite skills to our class, material that we usually cover in the orientations to our MPA program, and some times go very quickly over the first week of classes. If you have no previous experience with Excel, these two chapters are a way of getting you started. Chapters 4, 5 and 6 introduce more advanced skills using Excel, skills that are integrated through tutorial 1, in which we use an Open Dataset to practice several skills in a process of exploring a set of questions about differences in pricing of medical procedures. Chapter 7 and tutorial 2 are tools to explore and learn about data modeling and databases using Microsoft® Access®.

Chapters 8 through 13 constitute our modeling section of the book. Again, Chapter 8 is an introduction to key concepts of modeling, what is modeling and what types of models are used for decision and policy making. Chapter 9 is an introduction to basic concepts of probability and Chapter 10 applies probability concepts as we use decision trees to support decision making under uncertainty. Multi-Atrubute models are the main topic of Chapter 11, providing tools to compare a set of alternatives using multiple criteria such as cost, quality or efficiency. Chapter 12 introduces linear programming as a tool to find optimal solutions within constrains of limited resources, a common problem of decision and policy makers. The final chapter of the book introduces simulation as a tool for policy analysis.

Software Applications used throughout the Book

We have traditionally used Microsoft® Excel® to teach these basic concepts, so it seemed natural to use an existing Excel OER as the basis for this book. Microsoft® Excel® is a tool that can be used in virtually all careers and is valuable in both professional and personal settings. Whether you need to keep track of medications in inventory for a hospital or create a financial plan for your retirement, Excel enables you to do these activities efficiently and accurately. The core Excel Chapters are coming from the book Excel for Decision Making, which in turn, is an adaptation from materials by The Saylor Foundation.

This core text provides students with the skills needed to execute many personal and professional activities. It also prepares them to go on to more advanced data science skills using the Excel software. The text takes the approach of making decisions and exploring policy using Excel. Personal decisions introduced include important purchases, such as homes and automobiles, savings for retirement, and personal budgets. Professional decisions include budgets for managing expenses, merchandise items to mark down or discontinue, and inventory management. Students are given clear, easy-to-follow instructions for each skill presented and are also provided with opportunities to learn additional skills related to the personal or professional objectives presented. This text also places an emphasis on “what-if” scenarios so students gain an appreciation for the computational power of the Excel application. In addition, students learn how Excel is used with Microsoft® Word® and Microsoft® PowerPoint® to accomplish a variety of personal and professional objectives. This current version of the book includes a chapter and a tutorial on data management using Microsoft® Access®.

Screenshots that appeared in How to Use Microsoft Excel: The Careers in Practice Series, adapted by The Saylor Foundation, were used with permission from Microsoft Corporation, which owns their copyright. How to Use Microsoft® Excel®: The Careers in Practice Series is an independent publication and is not affiliated with, nor has it been authorized, sponsored, or otherwise approved by Microsoft Corporation. Our adapted work uses all Microsoft Excel screenshots under fair use. If you plan to redistribute our book, please consider whether your use is also fair use.

Attribution

Adapted by Luis F. Luna-Reyes from How to Use Microsoft Excel: The Careers in Practice Series, adapted by The Saylor Foundation without attribution as requested by the work’s original creator or licensee, and licensed under CC BY-NC-SA 3.0.