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. Examine what machine learning is, how it is categorized, and common machine learning challenges. Next, learn about common types of machine learning tasks, such as clustering, classification, and regression. Finally, explore the types of regression, including simple and multiple linear regression. Upon completion, you'll 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. Additionally, some advanced data science methods rely on other foundation data science methods, such as the case of text mining. Through this course, learn about advanced data science methods and their use cases. Explore advanced machine learning (ML) methods such as text mining and graph analysis and their uses. Next, learn about the anomaly and novelty detection processes. Finally, examine association rule mining and neural networks and their use cases across industries. After course completion, you'll be able to outline advanced methods for data science.

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. You will identify the types of data tools and technologies AI uses to operate. Next, you will discover a framework for using the AI life cycle and data science process. Finally, you'll consider what you need to keep in mind as you implement AI techniques in your organization. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.