Driving Results through
Business analytics
Data-Driven Decision Making!
Bring Business Analytics to life with Interactive Smart Learning. MyEducator’s resources immerse students in hands-on, data-driven environments where they analyze data, apply key concepts, and build real-world skills. With auto-graded assessments, AI-powered support, comprehensive instructor materials, and seamless LMS integration, instructors can easily deliver rigorous, applied learning. Students also earn stackable microcredentials to validate their analytics capabilities.
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This learning resource provides a structured, hands-on introduction to Microsoft Power BI. Students will leverage interactive reporting and dynamic decision support capabilities to explore, analyze, and communicate insights from real-world financial and accounting data. This skillset prepares future professionals to tell powerful business stories in areas such as budgeting, forecasting, auditing, and performance analysis.
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AI is significantly enhancing business analytics by improving efficiency, accuracy, and speed across various stages, from data preparation to insight generation and decision-making. This learning resource has business understanding at its core with a focus on Excel as the vehicle to exemplify basic models, internal mechanisms, and logic. It also demonstrates how AI can enhance and externally validate solutions in this new age of AI.
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Coming Soon!Data Visualization: A Multiplatform Guide to Data Storytelling with Excel, Tableau, Power BI, and AI
This learning resource is an applied introduction to telling clear, ethical stories with data. Students learn how perception, measure selection, and design affect understanding. They will practice chart selection, layout, and annotation and learn to design dashboards that support decisions and communicate a narrative. Concepts start in Excel, then progress through either a Tableau or a Power BI path for hands-on labs and a final capstone project.
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Coming Soon!Business Statistics and Analytics: A Comprehensive Foundation for Modern Decision-Making
Equip students with the statistical reasoning and analytical tools needed to make sound, data-driven decisions in today’s business environment. From foundational probability concepts to advanced regression techniques, this book bridges statistical theory with real-world business applications, preparing students to confidently interpret data and drive results in any organization.
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This book prepares managers to direct analytics work from the business question through deployment. Students use AI to generate code, but they learn enough to read it, test it, and decide whether the result deserves trust. Organized around CRISP-DM, the book connects data preparation, modeling, evaluation, communication, governance, and project economics to the decisions senior leaders actually make.
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This resource introduces students to data mining using the CRISP-DM methodology. It is intended for students and business professionals who are interested in using information systems and technologies to solve organizational problems by mining data, but who may not have a background in computer science. Students will learn the concepts and techniques required to successfully mine data in AI Studio, R, and Python.
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This resource teaches students to turn raw data into decisions using Python. Students progress from programming fundamentals through cleaning, profiling, and visualizing data, into applied statistics like correlation, regression, and group comparisons, finishing with data storytelling and a capstone project that produces a full analysis supporting a real business decision. Coding exercises are checked with an automated Python grader for immediate feedback, and students learn to work alongside AI tools throughout. It’s built for students with little or no background in programming or statistics who want to develop genuine technical skill in a field the U.S. Bureau of Labor Statistics projects will grow 34 percent through 2034.
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In today’s rapidly changing IT environment, organizations use a variety of technologies and tools to create machine learning environments and perform data analytics. This resource provides a practical introduction to performing exploratory data analytics to create machine learning pipelines using a combination of Tableau, Excel, and Azure ML Studio. Students will learn common practices in each phase of the CRISP-DM methodology.
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This resource teaches students to build, evaluate, and communicate analytical results in Python, the most widely used language for analytics and machine learning in practice. Framing data literacy as the new spreadsheet literacy, the course takes students from Python fundamentals through the CRISP-DM framework, covering data cleaning, feature engineering, regression, and classification, while integrating AI-assisted coding and analytics workflows throughout as a core professional skill. Students finish with model deployment, analytics strategy and governance, and responsible analytics practices, then apply everything in an end-to-end analytics pipeline project and technical interview preparation.
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This resource uses a variety of data analytics processing applications—such as Python, Jupyter Notebook, and Microsoft Excel, as well as several other softwares—to introduce students to the data analytics process. This resource is intended to help students gain experience with key machine learning algorithms and provide students with the knowledge they need to use that experience in real life applications.
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Optimize decisions by leveraging data-driven insights to proactively determine the best actions for future success. Build upon your introduction to programming (in Python), statistics, data cleaning, and automating workflows from prior courses. This resource advances analytics skills from distilling descriptive analytics from historical data and anticipating future trends with predictive analytics. Master applying advanced algorithms, decision optimization while minimizing risks with prescriptive analytics using both Excel and Python models.
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This resource covers the latest and most common techniques for both descriptive and predictive data analytics using practice-based video tutorials. Students use dashboard design and storytelling in Tableau to describe the current state of an organization based on measurable data. Students will learn basic predictive methods in Excel for multiple regression and the assumptions of linear regression, and then turn to advanced algorithms and techniques using Microsoft Azure Machine Learning Studio.
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This resource introduces students to basic statistical methods that provide insight into current business operations and help predict future conditions for effective business planning. Students will use descriptive and inferential statistics to describe current data, make estimates about larger groups, and inform smart business decisions.
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This resource is designed to take the student from zero knowledge about structural equation modeling to being confident about not only HOW to test their theories but also WHAT the results mean and WHY the results are what they are. With a focus on concepts and procedures rather than underlying algorithmic mechanisms, this resource prepares students to conduct analyses independently and confidently.
Request FREE Instructor Demo Access to any of our Interactive Business Analytics Resources!
Simply let us know who you are, what school you teach at, which resources you would like access to, and we’ll do the rest! If you don’t see exactly what you’re looking for, reach out here. We are standing by to help you customize the perfect resource for your classroom.