Corporate Training By Zack Academy - Making Data Science Pay

In the Making Data Science Pay seminar, students 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.

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.

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 this Making Data Science Pay Seminar?

• Executives and directors
• Analysts
• Statisticians
• Engineers
• Project leaders

Topics to be Covered:

• What is Data Science?
• What is the Organizational Value of Data Science?
• How is Data Science Different from Data Analytics?
• What are the Risks of Data Science?
• What are Data Science Technologies?
• What are the Skills Needed for Data Science
• The Future of Data Science and Advanced Analytics

Upon successful completion, students will earn 9 INFORMS Professional Development Units. This seminar has no prerequisites- sign up today to enhance your business practice career!

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.

Syllabus

• The Official Definition
• The Unofficial Definition
• Some Executives’ Definitions
• The “Real” Definition
• A Strategic Definition
• My Working Definition

• 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

• 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

• Data Issues
• The Truth about Social Media Data
• People Issues
• Technology Issues
• The Top 5 Risks of Data Science

• 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

• Data Science Professionals
• Data Architect
• Data Engineer
• Data Scientist
• Subject Matter Expert

• Data Scientist Fundamental Skills
• Characteristics of Data Scientists

• 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

• Picking Through the Rhetoric to Define Your Organization’s Data Science Reality
• A High Level Data Science Plan

• My Top Rhetorics (and Associated Realities) Summarized