Aspire Journeys
AI and ML for Decision-makers and Leaders
- 18 Courses | 12h 4m 9s
The "AI/ML for decision-makers" journey is a comprehensive program designed to educate leaders on the fundamentals of artificial intelligence and machine learning. The journey covers a wide range of topics, including the basics of AI, the potential applications and high impact use cases of AI, and the ethical considerations involved in implementing AI solutions, visualizing data for impact, and steps involved in designing the best AI strategy for their organization. This journey also focuses on educating leaders on various AI/ML techniques to unlock tremendous value in their data through improved customer service, streamlined business operations, and the realization of new business models and opportunities. Participants will also learn about the latest trends and advancements in the field, and various myths and misconceptions about AI. The program is designed to provide decision-makers with the knowledge and skills they need to effectively leverage AI to drive business, coordinate the Data Scientists during their prototyping phase, and design the best suitable AI strategy and AI teams to capture value for their organizations. By the end of the journey, participants will have a deep understanding of AI and its potential and will be well-equipped to make informed decisions about how to integrate AI into their organizations.
Track 1: Fundamentals of AI and ML
In this track of the AI and ML for Decision-makers Aspire journey, the focus will be on the data science methods and an introduction to Artificial Intelligence.
- 4 Courses | 3h 57m 43s
Track 2: Developing an AI/ML Data Strategy
In this track of the AI and ML for Decision-makers Aspire journey, the focus will be on developing an AI/ML data strategy.
- 6 Courses | 4h 29m 19s
Track 3: Visualizing Data for Impact
In this track of the AI and ML for Decision-makers Aspire journey, the focus will be on visualizing data for impact with data visualization, visual design theories, analyzing misleading visualizations, and data storytelling.
- 4 Courses | 1h 48m 50s
Track 4: Cloud Computing and MLOps in AI/ML
In this track of the AI and ML for Decision-makers Aspire journey, the focus will be on cloud computing and MLOps.
- 4 Courses | 1h 48m 17s
COURSES INCLUDED
Fundamentals of AI & ML: Foundational Data Science Methods
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.
12 videos |
44m
Assessment
Badge
PMI PDU
Fundamentals of AI & ML: Advanced Data Science Methods
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.
14 videos |
1h 9m
Assessment
Badge
PMI PDU
Fundamentals of AI & ML: Introduction to Artificial Intelligence
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.
15 videos |
1h 4m
Assessment
Badge
PMI PDU
Fundamentals of AI & ML: Metrics & Evaluation
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.
14 videos |
59m
Assessment
Badge
COURSES INCLUDED
Developing an AI/ML Data Strategy: The Data Analytics Maturity Model
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.
10 videos |
39m
Assessment
Badge
Developing an AI/ML Data Strategy: Building an AI-powered Workforce
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.
11 videos |
45m
Assessment
Badge
Developing an AI/ML Data Strategy: Data Analytics & Data Ethics
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.
8 videos |
38m
Assessment
Badge
Developing an AI/ML Data Strategy: Aspects of a Robust AI Strategy
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.
14 videos |
48m
Assessment
Badge
Developing an AI/ML Data Strategy: Data Bias & Ethical Considerations in AI
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.
13 videos |
35m
Assessment
Badge
Developing an AI/ML Data Strategy: Data Management & Governance in AI
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.
14 videos |
1h 1m
Assessment
Badge
COURSES INCLUDED
Visualizing Data for Impact: Introduction to Data Visualization
Using data visualizations effectively and correctly is a part of building a data-driven culture in your team. Data visualization creates accessible, understandable, and effective graphic representations of data to help teams understand the patterns and trends in their data and make data-driven decisions. In this course, you will learn about the fundamentals of data visualization, why it is important, and how data visualizations can be useful to your team. You will also explore different types of data visualizations, their use cases, and how to interpret them. Finally, you will discover how to select appropriate tools and visualizations. Upon completion of this course, you'll be able to define the fundamental concepts, types, and uses of data visualization.
8 videos |
26m
Assessment
Badge
Visualizing Data for Impact: Visual Design Theory
Visual designs play an important role in the presentation of data. Understanding and implementing visual design principles can help you build data visualizations that effectively communicate the message and make an impact on the target audience. Through this course, learn visual design principles and how to apply them to data visualizations. Explore elements and best practices for designing compelling visuals. Next, learn how to design effective visuals using contrast and position, as well as sizing and grouping visualization items. Finally, discover how to arrange items, use legends, address data set gaps, and use color for visualizations. After course completion, you'll be able to outline and apply visual design best practices to visualize data.
7 videos |
26m
Assessment
Badge
Visualizing Data for Impact: Analyzing Misleading Visualizations
One of the challenges of data visualization is recognizing and avoiding misleading visuals. These and other common mistakes make data visualization less effective and can lead to incorrect conclusions. Through this course, learn about misleading statistics and visual distortions. Examine some common data visualization mistakes, including data overload, interchanging charts, and the use of color, as well as how to recognize and correct them. Next, explore examples of deceiving statistics, visual distortions, and graphs and how to avoid being misleading. Finally, learn about omitting data, improper extraction, and correlating causation. After course completion, you'll be able to avoid mistakes when visualizing your data.
8 videos |
27m
Assessment
Badge
Visualizing Data for Impact: Data Storytelling
Data storytelling lets you set up and reveal key results quickly and in an organized fashion. It is a great way to make findings impactful and meaningful for an audience. Through this course, learn about data storytelling and how it can help elevate your data visualizations and create impactful narratives for an audience. Explore the theory and purpose behind data storytelling and how to contextualize and refine insight. Next, discover how to engage with an audience and put together an outline. Finally, learn how to plot data points to a storyboard and format a story for delivery. Upon completion, you'll be able to outline elements of data storytelling and apply them when presenting data.
8 videos |
28m
Assessment
Badge
COURSES INCLUDED
Cloud Computing and MLOps: Cloud and AI
Cloud computing is the on-demand delivery of computing services over the Internet. It enables scalable artificial intelligence (AI) and other advantages such as increased speed, scalability, and reduced cost. Through this course, learn about the role of cloud computing in AI. Explore the benefits and challenges of cloud computing, how to implement a cloud AI strategy, and the elements of the cloud computing architecture. Next, discover the importance of AI as a Service (AIaaS), the role of AI tools in data management and governance, and best practices for AI cloud security. Finally, learn about key cloud technologies for AI and emerging trends for cloud computing and AI. After course completion, you'll be able to outline elements of cloud computing in AI.
11 videos |
45m
Assessment
Badge
Cloud Computing and MLOps: Introduction to MLOps
The term MLOps is a combination of machine learning (ML) and DevOps. Used across several industries, MLOps is a valuable method for developing and testing machine learning and artificial intelligence (AI) solutions. Through this course, learn the basics of MLOps. Explore the elements of XOps, MLOps, and DataOps and their uses. Next, examine the importance of version control in machine learning and learn about version control types and tools. Finally, discover the roles and responsibilities of humans in ML pipeline automation and investigate ethical considerations and best practices for MLOps. By the end of this course, you be able to define MLOps and recognize its uses.
13 videos |
35m
Assessment
Badge
Cloud Computing and MLOps: ML Pipelines
ML pipelines help organizations improve the standards of machine learning (ML) models, improve their business strategy, and reduce redundant work and miscommunication. They consist of a series of ML workflow steps performed in a connected and automated/semi-automated way. Through this course, learn the basics of ML pipelines. Discover the uses and benefits of ML pipelines and the characteristics of manual and automated pipelines. Next, explore best practices for building pipelines and the three types of environments in the MLOps process. Finally, examine the importance of CI/CD in ML, the purpose of ML pipeline testing, and ML pipeline testing tools and frameworks. Upon completion, you'll be able to define ML pipelines and their benefits.
11 videos |
26m
Assessment
Badge
Final Exam: AI and ML for Decision-makers
Final Exam: AI and ML for Decision-makers will test your knowledge and application of the topics presented throughout the AI and ML for Decision-makers journey.
1 video |
32s
Assessment
Badge
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