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Guidance and results for the data-rich, yet information-poor.

MAKING DATA SCIENCE PAY

How to Set a Vision, Culture and

Environment for Data-Driven Returns

Classroom: One Day
Online: Two Half-Day Live Sessions
$995 USD
INFORMS Professional Development Units: 9


ABOUT THIS SEMINAR

Data Science is a big deal. But if you were to ask a hundred people what Data Science is – and more importantly, to state its value – you’d probably get a hundred different answers. Data Science is too important to be so elusive.  This seminar remedies that by defining the value and explaining the technology behind it.  The purpose is to cut through the market buzz surrounding data science and boil it down to its practical concepts and applications.

Participants will learn the real-world usage and ROI of data science including why projects typically succeed or fail. The course simplifies the technology and the essential tasks of the data scientist. It peels away the complexities surrounding data science, boiling it down to its essence, presented in a style that all can understand.

This seminar is a non-biased, coherent, and often entertaining integration of facts and figures, explanations and real-world usage of data science — translating its technology into value, and its value into strategic competitive advantage. It is taught by a 30-year veteran of analytics, with the reason and measured judgment that can only come from that experience. Her perspective is both passionate and impartial — a rare find in the data science-crazed marketplace.


WHO SHOULD ATTEND

    • Executives, directors and managers struggling to understand the reality, measure the value, overcome the challenges, and realize the rewards of data science
    • Business Intelligence leaders seeking the rationalization for data science initiatives
    • Analytic professionals trying to understand the differences in data analysis and data science
    • Data analysts, statisticians, engineers, and computer scientists who aspire to become data scientists
    • The curious who are tired of being bombarded by the Data Science market buzz and frustrated at not understanding it sufficiently to make reasoned decisions about its use

KEY SKILLS YOU’LL TAKE AWAY IMMEDIATELY

    • A delineation of what’s real and what’s not – rhetoric vs. reality – of data science
    • Real-world case studies – successes and failures
    • A comprehensive understanding of organizational challenges and strategic rewards of data science initiatives
    • A working understanding of Data Science tools and technology
    • A firm grasp of the current reality and likely future of data science, advanced analytics and predictive modeling

THE ORGANIZATIONAL CHALLENGE FOR DATA SCIENCE

Data Science is purported to have substantial organizational value. But the reality is that most people don’t know how to realize that value. This course illuminates and clarifies data science’s strategic potential.

Executive interviews recently conducted by the author of this course revealed:

    • 100% of executives have heard the term “data science”.
        • 69% think that the term is mostly buzz, but that there is also organizational value to gained from it.
        • The remaining 31% feel that the technology industry has not sufficiently built a case for investing in data science.
    • Major obstacles in data science adoption are:
        • High cost of development – 100% of respondents
        • Lack of a compelling project case – 82% of respondents
        • Confusion around the varying technologies and associated risks – 68% of respondents

Even companies that are early adopters of data science and have successfully shown isolated value with a project or two are challenged by issues related to a) integrating it into organizational processes and culture, and b) scaling initial successes into enterprise-wide strategic advantage.

This course gives a high-level, yet comprehensive overview of data science and associated analytics, and methodically addresses each of these issues from a strategic, value-focused perspective.


WHAT MAKES THIS SEMINAR UNIQUE

The developer of this seminar is a highly-seasoned practitioner and active strategic consultant of all things data, including advanced analytics. Sandra Hendren has been immersed in the evolution from “small data” and “analyses and reporting” in the ’80s to big data, data science, and the advanced analytics applied in today’s complex environments.

Sandra’s experience spans the full range from the deeply technical to the fully strategic. She still writes code and develops predictive models and machine learning algorithms. She engages with management — in charge of multiple analytic development teams. And Sandra is fluent in conversing at the executive level — most recently as Chief Data and Analytics Strategist for UnitedHealth Group, a Fortune 12 company. Trained as a statistician, she is now known as a Strategic Data Scientist, and is fully effective in explaining the differences.

Ms. Hendren is known as an energetic speaker, bridging the gap between technology and value in her lucid explanations of their relationship. She has held multiple adjunct faculty positions, sometimes teaching technology courses, but as often teaching management courses. Most recently she was Senior Lecturer of Strategic Management for Harvard University.

Frustrated at the muddled content and too-often biased representation of data science, coupled with the total lack of vendor-neutral education, Ms. Hendren joined The Modeling Agency specifically to add this seminar to the curriculum. She believes all managers need a working knowledge of the latest trends in technology in order to be effective. Likewise, data science professionals must seek a better understanding of the strategic value of the technology for which they are responsible. Thus, this course was developed to strike that balance, and to be equally useful for both leadership and practitioners.


TOPIC COVERAGE

What is Data Science?
    • The Official Definition
    • The Unofficial Definition
    • Some Executives’ Definitions
    • The “Real” Definition
    • A Strategic Definition
    • My Working Definition
What is the Organizational Value of Data Science?
    • Two High-Value Use Cases
    • Deriving Value from Analytics
    • Analytic Stages and ROI
    • The Relationship Between Data Science and High ROI Analytics
    • Top Three Sources of High ROI
How is Data Science Different from Data Analytics
    • A Short History of Analytics
    • Three Types of Analytics
        • Descriptive Analytics
        • Predictive Analytics
        • Discovery
    • Data Science Analytic Methods, the Same but Different
        • Statistics
        • Data Mining
        • Machine Learning
    • Comparison and Cautions of Data Science Analytics vs. Regular Analytics
What are the Risks of Data Science?
    • Data Issues
    • The Truth about Social Media Data
    • People Issues
    • Technology Issues
    • The Top 5 Risks of Data Science
What are Data Science Technologies? A Layman’s View
    • Data and Analytics Technology – Old Rules
    • Data and Analytics Technology – New Rules
    • Hadoop and Big Data Realities
    • Data Science Tools Realities
    • Total Cost of Ownership of Data Science
    • How to Decide: The Data Part
    • How to Decide: The Science Part
What are the Skills Needed for Data Science?
    • Data Science Professionals
        • Data Architect
        • Data Engineer
        • Data Scientist
        • Subject Matter Expert
    • What Does a Data Scientist Do All Day?
        • Data Scientist Fundamental Skills
        • Characteristics of Data Scientists
How Do You Organize Data Science in Your Organization?
    • Historic Data and Analytics Organization
    • Data Science Organizational Paradox
    • 5 Types of Organizational Structures
The Future of Data Science and Advanced Analytics
    • From Rhetoric to Reality
    • Market Facts and Figures – Reality
    • Biggest Driver of Analytic Innovation
        • Continually Improving Productivity and Profitability
        • Predicting Problems Before They Happen Becomes the New Norm
        • Changing Ever More Operational Models
    • What’s Next in Data Science?
Picking Through the Rhetoric to Define Your Organization’s Data Science Reality
    • A High Level Data Science Plan
Prologue
    • My Top Rhetorics (and Associated Realities) Summarized

The Modeling Agency, LLC, is registered with INFORMS (the INstitute For Operations Research and the Management Sciences) as a Recognized Analytics Continuing Educational Provider for the CAP® (Certified Analytics Professional) program. The CAP® credential provides analytics professionals with a means to distinguish themselves and demonstrate to employers, colleagues, and the public that they are knowledgeable analytics professionals. Courses provided by The Modeling Agency, LLC, are automatically accepted by INFORMS when claimed by credential holders as evidence of continuing education. For more information about the CAP® program, including requirements, eligibility, benefits, preparation, and exam dates, please visit the INFORMS website at www.informs.org/certification.

SCHEDULE

June 27 & 28, 2017
12:00p – 5p US EDT
August 2, 2017
October 18, 2017
November 1 & 2, 2017
12:00p – 5p US EDT

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Attendee Comments

“Big Data and Analytics are not merely about scalability and stats, but the ability to understand the context of the problems, to ask the right questions, and to establish the performance metrics that are relevant and crucial to answer these questions. It requires a service design mentality, and that’s what this class delivers”

Ting-Shuo Yo
Data Scientist
ASUSTeK Computer inc.

“The instructor is also a consultant. She fully understands real world scenarios, and a project approach to effective execution.”

Jean M. Cotis
Application Development Manager
UPS Freight

“I look forward to applying what we learned during this seminar to identify and prioritize new projects. We will be more organized, efficient and effective by applying the concepts delivered in this class.”

Sean Schmitt
Analyst
US Customs and Border Patrol

“No fluff at all. The instructor consistently provided specific, practical and understandable advice about complex ideas and processes. She explains things very clearly, backs up her answers with real-world examples and is patient with questions.”

-Anonymous
Partner Conference Production

“As someone without a formal degree, big data and analytics can be daunting.This course is approached in a very consumable way. It is practical and applicable, which is exactly what is needed in order to support organizational objectives.”

Carmen Schwesinger
Analyst
Government of Alberta