Business & Analytics Integrator Skills - BA - Q3

Participation Prerequisites

This course is for MSc Business Analytics students only. Students from other MSc programmes may participate as guests (subject to availability of capacity) but cannot take the course for credit.

Course Content

This module covers the whole journey from building and managing a data science team, over setting up data science projects, to managing them and deploying their outputs. It further focuses on translating analytics and AI algorithms into innovative solutions and new business opportunities and vice versa. 

• Soft skills: Influencing people based on their personality type. We adapt soft skills training using intensive role plays (as there are similarly used internally at McKinsey & Partners) to figure out how to best influence stakeholders and communicate effectively with clients. Building and managing team in data science.
• Project generation: generating ideas from data or business processes. Creativity techniques commonly
• used in management consultancy. Design thinking and minimum viable products.
• Software architecture and software engineering
• Project management and innovation management. 
• Translating analytics and AI algorithms into innovative solutions and new business opportunities.
• Change management and deployment: Overcoming barriers in the organization to implement novel analytics and AI solutions. Metrics and project evaluation tools etc. Translating data science outcomes back into business outcomes. Monitoring projects and solution maintenance. Learnings for next project.

Intended Learning Outcomes and Competencies

• Students will have expert knowledge of business management, including project management and leadership approaches for analytical projects.
• Students will select and to some extent employ a wide range of analytics methods and tools in a variety of organizational and market contexts.
• Students will know the main functional dimensions of the management context within which they may work, and of current research in at least one of these areas.
• Students will effectively use a range of relevant computer software and tools. 
• Students will apply these skills with self-direction, originality, and an entrepreneurial mindset to a range of business problems, contexts, and market challenges.
• Students will be capable of developing and implementing business strategies and solutions considering diverse people and cultures.
• Students will communicate effectively between stakeholders on the business and on the analytics side
• in the context of a business challenge.
• Students conduct compelling oral presentations.
• Students produce business documents of a highly professional standard.
• Students learn how to translate analytics and AI algorithms into innovative solutions and new business opportunities.
• Students learn how to manage and lead cross-functional analytics and AI innovation teams.
• Students understand best practices, tools and methods to manage innovation and change, such as agile new product development (scrum etc.), stage-gate processes, metrics and project evaluation tools.

Instruction Type

In-person

Form of Examination

Literature

There is no core textbook for this course. Below are some optional references that are closely related to the course:

• Iansiti, M., Lakhani, K. (2020): Competing in the Age of AI. HBR (January-February 2020)
• Fountaine, T., McCarthy, B., Saleh, T. (2019): Building the AI-Powered Organization. HBR (July-August 2019)
• Kirill Dubovikov. Managing Data Science. 2019, Packt Publishing