Course: Developing an AI/ML Data Strategy

Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagnostic, predictive, and AI types of data analytics. Next, discover how data analytics can be used across teams and the benefits it offers. Finally, discover the different types of tools designed for data storage, cleaning, visualization, analysis, and collaboration. Upon completion, you'll be able to outline what data analytics is and list common data science tools.

Building a successful data team is a key part of a data strategy. To build a proper data team, it's important to know how they are structured and the roles of each member. Through this course, learn how to build an AI-powered workforce with a data team. Discover the need for an AI-powered workforce and three main structure types of a data team. Next, learn how to determine which strategy is preferable for a data team. Finally, explore the roles of data team members, how to evaluate an organization's strategy, and how to move an organization toward a data-driven culture. After course completion, you'll be able to outline the functions and best practices for a data team.

Growing fields of data analytics and artificial intelligence (AI) provide many benefits to individuals and society, but also raise ethical concerns regarding privacy, transparency, and bias. How can organizations collect, store, and use data ethically, and what ethical safeguards must be maintained? Through this course, learn about data ethics and its importance in AI. Explore the concept of data ethics and a manager's role and responsibility to maintain ethical standards on their team. Next, discover the key principles and considerations for data ethics in AI. Finally, learn about data ethics frameworks that are used across a variety of industries. After course completion, you'll be able to identify the importance of data ethics and its concerns and best practices.

In today's artificial intelligence (AI)-driven landscape, it's vital to recognize the ethical dimensions of AI implementation. This course covers essential aspects of AI strategy, including defining its components, understanding the leadership role, and conducting readiness assessments. You will also explore implementation challenges, use case identification, alignment with existing technology, and setting success metrics. You will discover how to leverage agile methodologies and business model impacts. Finally, you will learn to analyze case studies, clarify managerial responsibilities, and promote cross-functional collaboration in AI initiatives.

This course focuses on the ethical aspects of data analytics and artificial intelligence (AI) in our rapidly evolving world. It explores how AI and data analytics impact our society and emphasizes the importance of addressing ethical concerns, particularly data bias. In this course, students will learn to identify and address data bias in AI, exploring its real-world implications. Additionally, students will delve into the ethical aspects of AI, including transparency, fairness, and regulatory compliance, while considering the manager's role in ensuring ethical AI practices. By the end of this course, students will have a thorough grasp of the ethical issues that arise in AI and data analytics and be able to identify, assess, and mitigate bias in AI systems.

The rapidly growing fields of data analytics and artificial intelligence (AI) offer immense advantages to individuals and society. Nevertheless, there are also challenges related to data management and governance within the context of AI. Begin this course by exploring the practical knowledge and skills necessary for effective data management and governance in the context of AI projects. Discover how data quality, integrity, availability, and adherence to governance frameworks are crucial in AI projects. Next, examine data lineage, data privacy regulations, and data accessibility. Then focus on the risks of incomplete or biased data and methods for handling large and complex datasets. Finally, investigate metadata management, managerial responsibilities in data governance, and ethical considerations in data usage. At course completion, you will be able to effectively manage data in AI projects and navigate the complex landscape of AI and data analytics with a strong foundation in data management and governance principles.