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Making Data Science Pay
Duration
1 Day
Course
BAPD-105
Available Formats
Classroom Training, Online Training
Course Description
Overview
Making Data Science Pay focuses on How to Set a Vision, Culture and Environment for Data-Driven Returns.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, who possesses the reason and measured judgment that can only come from the experience of being immersed in the evolution of all things data, 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. Combining a perspective which is both passionate and impartial — a rare find in the data science-crazed marketplace.
Objectives
Upon completion of the Making Data Science Pay seminar, attendees will take away:
- 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
Audience
Making Data Science Pay is intended for:
- 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
Prerequisites
- INFORMS Professional Development Units: 9
Topics
- 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
- What Does a Data Scientist Do All Day?
- Data Scientist Fundamental Skills
- Characteristics of Data Scientists
- Historic data and analytics organization
- Data Science organizational paradox
- 5 types of organizational structures
- 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?
- A high level data science plan
- My top rhetoric (and associated realities) summarized
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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