AI for Tech Leaders

Data science methods are used across several industries to deliver value to businesses. Machine learning (ML) is a data science method that uses prediction algorithms that find patterns in massive amounts of data, allowing machines to predict future results and make decisions with minimal human intervention. Through this course, learn foundational methods for using machine learning. In this course, you will examine what machine learning is, how it is categorized, and some everyday use cases for supervised and unsupervised machine learning. Then you will discover feature engineering and its impact on model performance. Next, focus on common types of machine learning tasks, such as clustering, classification, and simple and multiple linear regression. Finally, explore various machine learning challenges and how to overcome them. Upon completion, you will be able to define machine learning and methods for using it.

In data science, many statistical and analytical techniques can be used to pull meaningful insights from data. Some advanced data science methods rely on other foundational data science methods, such as text mining. In this course, you will learn about advanced data science methods and their use cases. Begin this course with an exploration of advanced machine learning (ML) methods, such as text mining and graph analysis, and their uses. Next, you will discover the anomaly and novelty detection processes. You will examine association rule mining and neural networks, including their use cases across industries. Then you will focus on common challenges during artificial intelligence (AI) and ML model training, the trade-offs between model complexity and interpretability, and the role of natural language processing (NLP) in text analysis. Finally, you will investigate the potential of computer vision techniques and applications of reinforcement learning.

Artificial intelligence (AI) provides cutting-edge tools to help organizations predict behaviors, identify key patterns, and drive decision-making in a world that is increasingly made up of data. In this course, you will explore the full definition of AI, how it works, and when it can be used, focusing on informative use cases. You will identify the types of data, as well as the tools and technologies AI uses to operate. Next, you will discover a framework for using the AI life cycle and data science process. Then you will examine how data science, machine learning (ML), and AI are relevant in the modern business landscape. Finally, you will investigate the key differences between AI and traditional programming approaches, the benefits and challenges associated with integrating AI and ML into business approaches, and the potential impact of AI on job roles and workforce dynamics. Upon completion of this course, you'll be familiar with common concepts and use cases of artificial intelligence (AI) and be able to outline strategies for each part of the AI life cycle.

Understanding model evaluation is crucial for making reliable, accurate, and ethical decisions when using artificial intelligence (AI) and machine learning (ML) in practical scenarios. In this course, you'll explore AI/ML model evaluation and interpretability in-depth, gaining a strong grasp of these essential components to make AI/ML work effectively for your organization. This course focuses on the key concepts and metrics needed to assess how well models perform. Understanding model evaluation is crucial for making reliable, accurate, and ethical decisions when using AI/ML in practical scenarios. Upon completing this course, you will be well-prepared to make informed decisions and maximize the potential of AI/ML within your organization.

In today's fast-paced business landscape, data is the driving force behind strategic decision-making. Navigating the latest trends in data has never been more important or offered greater potential benefits for savvy leaders. In this course, you will be introduced to adaptive governance, data governance and agility, and unlocking the value of data through monetization. Then you will explore the role of artificial intelligence (AI)-driven storytelling, decision-making with AI insights, augmented data management, and integrating AI and automation into data management. Next, you will discover democratizing real-time data, tools and technologies for real-time insights, and ethical considerations for data practices. You will learn about data privacy and trust and the role of leadership in data adoption, and dive into data innovation case studies. Finally, you will examine continuous learning in the data landscape and emerging data trends.

The modern leader needs to gain insights into cutting-edge technologies and strategies. With those insights, the opportunities are practically limitless. In this course, you will be introduced to the power of practical data fabric, beginning with an exploration of data fabric, the benefits of data fabric, data mesh, and the relationship between data fabric and data mesh. Then you will discover how data fabric addresses data silos and complexity. Next, you will investigate data fabric architecture, the advantages of a data fabric architecture, and the role of data fabric in analytics. Finally, you will learn about operationalizing data fabric, the challenges and limitations of data fabric, and data fabric use cases.

Data is the driving force behind strategic decision-making, and the most successful organizations are the ones that recognize and champion understanding the health and performance of their data systems. They recognize the need for knowledge and skills in order to harness the full potential of data in our rapidly evolving digital world. In this course, you will unlock data observability, beginning with an introduction to data observability, benefits and challenges of data observability and the data observability framework. Then you will dive a little deeper and compare data observability to data quality. Next, you will explore data observability tools, different types of data observability, and data observability best practices. Finally, you will discover data observability use cases and the importance of having a data observability strategy.

Converged infrastructure is represented by systems containing preconfigured software and hardware in a single integrated software-defined architecture. Composability is a system design principle that focuses on the interrelationships of components. In this course, you will delve into converged and composable systems, beginning with an introduction to converged and composable systems, the benefits of converged and composable systems, and the elements of a converged system. Then you will focus on the modularity and interoperability in composable systems. You will learn about integrating diverse technologies into unified systems, business considerations for system strategies, composability building blocks, and adaptability in modern business. Finally, you will explore convergence and composability in cloud computing, challenges in converged and composable systems, composable systems and leadership, and use cases for converged systems.

Modern organizations must embrace data governance methodologies to remain competitive and compliant; of late, artificial intelligence (AI) has become a must-have for organizations. AI TRiSM (trust, risk, and security management) is the key concept that ensures AI models are governed and trustworthy. In this course, you will explore the role of AI TRiSM in risk management. Then you will focus on the pillars of the AI TRiSM framework, advantages of AI TRiSM, and how AI can be leveraged for informed decision-making. Next, you will discover how organizations can take advantage of AI TRiSM and examine the fundamental factors that make AI TRiSM successful. Finally, you will delve into how to achieve success with AI TRiSM and implement an AI strategy in your organization.

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 proper data teams, 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. Learn how to determine which strategy is preferable for a data team. Next, explore the potential shifts in job roles and responsibilities due to AI integration, the role of managers in driving AI adoption and change management, and strategies for fostering a culture of innovation and AI awareness within the workforce. 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.