AI and ML Data Strategy Competency (Intermediate Level)

Topics covered

• compare the three options for building a data science team
• describe the role of managers in driving AI adoption and change management
• distinguish between centralized, decentralized, and hybrid data team structures and the benefits and challenges of each structure type
• identify an organization's data-driven culture and AI strategy
• identify how bias is created, its types, and strategies to recognize and avoid data bias
• identify the categories of data analytics uses
• identify the emerging trends in data analytics and their roles across industries
• identify the need for building an AI-powered workforce
• identify the potential shifts in job roles and responsibilities due to AI integration
• identify what data ethics is, why it is important, and when it should be considered
• list the key principles of data ethics
• list the right questions to ask when selecting tools for data teams
• name the main ethical considerations for artificial intelligence (AI)
• name the roles of the data science team and the tools they use
• outline how to move an organization toward a more data-driven culture
• outline strategies for fostering a culture of innovation and AI awareness within the workforce
• outline the purpose and components of the data analytics maturity model
• outline what managers should consider when adopting artificial intelligence (AI)/machine learning (ML)
• recognize examples and takeaways of data ethics frameworks across different industries
• recognize the purpose and uses of data storage tools
• recognize the purpose of using data collaboration and visualization tools
• state the aim of data cleaning and analysis tools and technologies