Problem Identification and Solution Design for Data Scientists
1h 25mIntermediate2024-09-23
Authors
Keith McCormick
Data Miner, Trainer, Speaker, Author
Course details
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.
Learning objectives
Identify the tasks associated with the business understanding phase of the CRoss Industry Standard Process for Data Mining (CRISP-DM).
Translate business goals into technical goals that can be achieved with data science techniques.
Identify project success metrics that align with a project sponsor’s business goals.
Diagnose possible causes of the challenges that a project seeks to overcome.
Explain options and technical considerations that a project sponsor might have to consider in business terms.
Learning objectives
Identify the tasks associated with the business understanding phase of the CRoss Industry Standard Process for Data Mining (CRISP-DM).
Translate business goals into technical goals that can be achieved with data science techniques.
Identify project success metrics that align with a project sponsor’s business goals.
Diagnose possible causes of the challenges that a project seeks to overcome.
Explain options and technical considerations that a project sponsor might have to consider in business terms.
Skills covered
Data GovernanceTeams and CollaborationCommunicationPersonaData AnalysisData ScienceProfessional DevelopmentBusiness Analysis and StrategyLeadership and ManagementBusiness Software and Tools
Concepts
Introduction
- Welcome
- Intended Audience
Meet the Project Customer
- Looking at the project from the sponsor's point of view
- Reading the project description critically
- Preparing for the first meeting as an internal resource
- Preparing for the first meeting as an external resource
What to Expect in the Initial Meeting
- What to expect in the initial meeting
- Inference vs. prediction
- Prediction vs. forecasting
- Avoiding confusion with other analytic project types
- Diving deeper into predictive analytics
- Identifying ROI
- Some questions that always apply
The Business Understanding Phase of CRISP-DM
- Business understanding phase overview
- The four tasks
- Advice on how much detail to share
Meeting with IT
- Pre-meeting requests and preparation
- Ensuring that you leave with the critical info
- The provenance of the data
- Leveraging IT experience and expertise
Meeting with SMEs and the Frontline Team
- Leave these interviews for last
- Assessing culture
- Focusing on inclusion, not exclusion
Avoid Delays
- Metadata vs. data
- Estimates of accuracy
- Beware too much prototyping
Writing Up Your Results
- Documentation advice
- A simple visuatlization for setting priorities
- Striking the optimal amount of detail
Conclusion
- What's next in problem identification and solution design for data scientists