This course provides business leaders and managers with strategies and guidelines for addressing the human capital, technological, and management challenges associated with incorporating data science into their operations. Students will learn how to identify data science opportunities across many functional areas of the business, as well as how to prioritise and execute on those opportunities as part of a data science initiative.
Course: AI for Business Leaders
Introduction to Data Science
- • Classify data science projects in terms of Area, Approach, and Type of Model
- • For a given Area, Approach, and Model Type, provide one example project from your business
- • Given the particulars of a data science project, identify areas of concern that might lead to the projects failing.
- • Given the particulars of a data science project, identify steps that could be taken to help ensure the project succeeds
Business Case for Data Science
- • Define an organization’s data science roadmap
- • Identify the best projects(s) to start with
- • Detail strategies for successfully launching data science initiatives
- • Determine a starting point -- the most appropriate first project (or suite of projects) to capture the most promising opportunities and launch the data science function with adequate momentum to ensure its long-term operation within the organization.
- • Work with fellow executives to set and manage reasonable expectations of success for data science projects
- • Given a set of candidate data science projects, determine the relative strategic importance, cost, complexity of implementation, risk, the likelihood of value capture, and magnitude of benefit for each of the five projects
- • For any data science project, identify strategies for meeting three key factors of success (executive sponsorship; strategic alignment with core business interests; scope conditions)
Human Capital of Data Science
- • Use the Data Science Heat Map as a tool for specifying roles within the Data Science organization
- • Manage Data Science operations using structured processes for work and communication
- • Given the particulars of a company’s strategic and operating contexts, identify the data science organizational model best suited for that company.
- • Given a data science strategy, identify and prioritize the mix of roles you would pursue to build out the data science organization.
- • Describe the project and product management strategies best suited for a given company’s data science organizations
- • Given a broad business challenge, describe how you would approach the development of a data science strategy using the Structured Problem Solving Method.
- • Given a business context, identify strategies for promoting a data-driven culture throughout that business, particularly,
- • around guiding employees on how to think through breaking down problems of identifying data consumers, data needs/ use cases, data sources, and related necessary pipeline/ transformations that need to happen
Data and Machine Learning Infrastructure Strategy
- • Given a particular business context, prepare a detailed Data and Data Architecture strategy
- • Given a particular business context, detail how a Machine Learning Architecture strategy fits into its Data and Data Architecture strategy.
- • Identify the strengths and weaknesses of a given business’s Data and Data Architecture strategy
Capstone Project: 100-Day Data Science Plan
It is common for an executive to be asked to prepare a plan for their first 100 days in a new leadership role within a company (whether from an internal move or joining the company for the first time). The Capstone Project requires students to create a 100-day data science plan for a company of their choice; this could be the student's current company, another existing company, or a fictitious business context.
1- The student will construct/create the following as part of this project:
- • Their data science organization's Human Capital Plan
- • Their data science organization's technical plan
- Data Architecture and Data Strategy
- Architecture for Machine Learning
2- Six data science opportunities for the organisation have been identified.
- Evaluation of these opportunities on a rack and stack basis
- Describe the risks, challenges, and critical success factors for each of these opportunities.
3- A plan for implementing these six data science opportunities.
This Capstone project's deliverable will be a detailed presentation to the CEO outlining your strategy and the reasoning behind your decisions.