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Problem Identification and Solution Design for Data Scientists

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Whether you’re working as a consultant or an employee, you need to be able to speak to nontechnical business leaders if you want to be successful as a data scientist. Becoming bilingual isn’t easy, though—so what’s the secret to mastering this coveted skill? In this course for aspiring data professionals, join instructor Keith McCormick as he outlines the fundamentals of problem identification and solution design for data scientists. Learn how to translate business needs into technical terms, follow the CRoss Industry Standard Process for Data Mining (CRISP-DM), conduct structured interviews with project leaders, and think about projects from a business leader’s point of view. Keith offers quick insights and easy-to-use tips for conveying technical concepts clearly with ease. Along the way, you’ll learn how to leverage business communication and documentation skills throughout every stage of a project.


01 - Introduction
  • 01 - Welcome
  • 02 - Intended Audience

  • 02 - 1. Meet the Project Customer
  • 01 - Looking at the project from the sponsors point of view
  • 02 - Reading the project description critically
  • 03 - Preparing for the first meeting as an internal resource
  • 04 - Preparing for the first meeting as an external resource

  • 03 - 2. What to Expect in the Initial Meeting
  • 01 - What to expect in the initial meeting
  • 02 - Inference vs. prediction
  • 03 - Prediction vs. forecasting
  • 04 - Avoiding confusion with other analytic project types
  • 05 - Diving deeper into predictive analytics
  • 06 - Identifying ROI
  • 07 - Some questions that always apply

  • 04 - 3. The Business Understanding Phase of CRISP-DM
  • 01 - Business understanding phase overview
  • 02 - The four tasks
  • 03 - Advice on how much detail to share

  • 05 - 4. Meeting with IT
  • 01 - Pre-meeting requests and preparation
  • 02 - Ensuring that you leave with the critical info
  • 03 - The provenance of the data
  • 04 - Leveraging IT experience and expertise

  • 06 - 5. Meeting with SMEs and the Frontline Team
  • 01 - Leave these interviews for last
  • 02 - Assessing culture
  • 03 - Focusing on inclusion, not exclusion

  • 07 - 6. Avoid Delays
  • 01 - Metadata vs. data
  • 02 - Estimates of accuracy
  • 03 - Beware too much prototyping

  • 08 - 7. Writing Up Your Results
  • 01 - Documentation advice
  • 02 - A simple visuatlization for setting priorities
  • 03 - Striking the optimal amount of detail

  • 09 - Conclusion
  • 01 - Whats next in problem identification and solution design for data scientists
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    تاریخ انتشار: ۲۷ دی ۱۴۰۳
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