Data-driven Decision Making

Start Date: 02/23/2020

Course Type: Common Course

Course Link:

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About Course

Welcome to Data-driven Decision Making. In this course you'll get an introduction to Data Analytics and its role in business decisions. You'll learn why data is important and how it has evolved. You'll be introduced to “Big Data” and how it is used. You'll also be introduced to a framework for conducting Data Analysis and what tools and techniques are commonly used. Finally, you'll have a chance to put your knowledge to work in a simulated business setting. This course was created by PricewaterhouseCoopers LLP with an address at 300 Madison Avenue, New York, New York, 10017.

Course Syllabus

In this module you'll learn the basics of data analytics and how businesses use to solve problems. You'll learn the value data analytics brings to business decision-making processes. We’ll introduce you to a framework for data analysis and tools used in data analytics. Finally, we’re going to talk about careers and roles in data analytics and data science.

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Course Introduction

Data-driven Decision Making Have you ever wondered how companies make their financial decisions? Do you know how to make a data-driven decision? This course will give you the tools to make informed data-driven decisions. We will learn how companies make decisions by combining the insights of a data team and the tools of a data analyst. You will understand the challenges and opportunities for making data-driven decisions; you will practice and leverage the decision tools and techniques that analysts and managers know how to use; you will evaluate the performance of different decision-making models; and you will compare and contrast different decision-making models. We will learn the ins and outs of data-driven decision making so that you can make informed data-driven decisions. Upon completing this course, you will be able to: 1. Make data-driven decisions 2. Assess performance of decisions 3. Use decision tools and techniques to analyze and choose among models 4. Compare and contrast different decision-making models 5. Use the decision tools and techniques to analyze and choose between models 6. Use decision tools and techniques to analyze and choose among models 7. Use the decision tools and techniques to

Course Tag

Data-Informed Decision-Making Big Data Data Analysis Data Visualization (DataViz)

Related Wiki Topic

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Data-driven instruction Swan, G., & Mazur, J. (2011). Examining data driven decision making via formative assessment: A confluence of technology, data interpretation heuristics and curricular policy. Gene, 1(1), 1.
Data-driven instruction Kennedy, B. L., & Datnow, A. (2011). Student Involvement and Data-Driven Decision Making Developing a New Typology. Youth & Society, 43(4), 1246–1271.
Data-driven instruction Moriarty, T. W. (2013). Data-driven decision making: Teachers’ use of data in the classroom (Ph.D.). University of San Diego, United States—California. Retrieved from
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Data-driven instruction Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use to improve instruction by looking at the information they have about their students. It takes place within the classroom, compared to data-driven decision making. Data-driven instruction works on two levels. One, it provides teachers the ability to be more responsive to students’ needs, and two, it allows students to be in charge of their own learning. Data-driven instruction can be understood through examination of its history, how it is used in the classroom, its attributes, and examples from teachers using this process.
EBay eBay uses a system that allows different departments in the company to check out data from their data mart into sandboxes for analysis. According to Goul, eBay has already experienced significant business successes through its data analytics. eBay employs 5,000 data analysts to enable data-driven decision making.
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Jeanne Woodford Woodford has more than 30 years’ experience in corrections and law enforcement as an administrator, author, and public speaker. In 2004, she was appointed by Governor Arnold Schwarzenegger as the Undersecretary of the CDCR, where she oversaw an eight billion dollar budget, brought accountability to the department through data-driven decision-making, and advocated for rehabilitation programs and a sentencing commission for California.
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Office of Social Innovation and Civic Participation Presidential Innovation Fellow,: Scott Hartley focused on Evidence-based Policy, data driven decision making, and competitive grant programs, helping the Office of Social Innovation, OMB, and other agencies consider Silicon Valley methodologies such as "Lean Startup" philosophy to drive staged decision making, faster or more iterative feedback loops, and risk mitigation without stifling innovation. On leave from Mohr Davidow Ventures, and on assignment from USAID’s Development Innovation Ventures, he managed agency workshops related to the President’s Management Agenda.
Collaborative decision-making software Most decision-making and discussion surrounding business processes occurs outside organizational BI platforms, opening a gap between human insight and the business data itself. Business decisions should be made alongside business data to ensure steadfast, fact-based decision-making.
Collaborative decision-making software In the 1960s, scientists deliberately started examining the utilization of automated quantitative models to help with basic decision making and planning. Automated decision support systems have become more of real time scenarios with the advancement of minicomputers, timeshare working frameworks and distributed computing. The historical backdrop of the execution of such frameworks starts in the mid-1960s. In a technology field as assorted as DSS, chronicling history is neither slick nor direct. Diverse individuals see the field of decision Support Systems from different vantage focuses and report distinctive records of what happened and what was important. As technology emerged new automated decision support applications were created and worked upon. Scientists utilized multiple frameworks to create and comprehend these applications. Today one can arrange the historical backdrop of DSS into the five expansive DSS classes,including: communications-driven, data-driven, document driven, knowledge-driven and model-driven decision support systems. Model-driven spatial decision support system (SDSS) was developed in the late 1980s and by 1995 the SDSS idea had turned out to be recognized in the literature. Data driven spatial DSS are also quite regular. All in all, a data-driven DSS stresses access to and control of a time-series of internal organization information and sometimes external and current data. Executive Information Systems are cases of data driven DSS.The very first cases of these frameworks were called data-oriented DSS, analysis Information Systems and recovery. Communications-driven DSS utilize networks and communications technologies to facilitate decision-relevant collaboration and communication. In these frameworks, communications technologies are the overwhelming design segment. Devices utilized incorporate groupware, video conferencing and computer-based bulletin boards.
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