Data Driven!
Open your students’ eyes to the exciting and powerful world of data analytics!
Our modern and up-to-date interactive resources provide knowledge, competencies, and skills-based experiential learning in technology applications. Enhance your teaching with video walkthroughs, step-by-step instructions, robust datasets, auto-graded assessments, and business application projects with ample instructor material and full LMS integration. Request full access, and let data drive your course!
Mark Keith
Data science is changing the world. This resource provides an introduction to programming in Python, statistics, data cleaning, and automating the data analytics process to prepare students with critical skills and conceptual understanding in data science. It is targeted toward students who have a limited background in programming or statistics but want to build a thorough technical skillset.
Mark Keith
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 Microsoft ML Studio. Students learn common practices in each phase of CRISP-DM.
Matthew North
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.
Mark Keith
Data mining techniques are changing constantly. Through practice-based video tutorials, this resource teaches the latest and most common techniques for both descriptive and predictive data analytics. Using Tableau, students learn dashboard design and data storytelling in organizational settings. Students also learn basic methods in Excel for multiple and linear regression, then turn to more advanced algorithms and techniques in Microsoft Azure Machine Learning Studio.
Ronald Miller, Boyd Timothy, Megan Costantino, Bonnie Allred Dalley, Andre Luis Rossi de Oliveira
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.
James Gaskin
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.
Mark Keith
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.
Douglas Dean
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.
Randy Bulriss, Mark Keith
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.
Mark Keith, James Gaskin
This resource focuses on the role of information systems in organization architecture and strategy with an application-focused approach. In addition to learning about information systems, students will design, build, query, maintain, analyze, and strategize to support business decision-making.
Nick Ball, Gove Allen
Teach Excel and assess your students in the live Microsoft environment with robust, detailed grading utilizing our MyEducator add-in. This resource has 100% coverage of content in both written and video format. Database and SQL content provide an opportunity for an application-focused learning experience.
John Anderson, Nick Ball, Gove Allen, Robert Jackson
Go beyond the basic and intermediate skills of Microsoft Excel with this advanced learning resource. With automated detailed grading in the live Microsoft environment and step-by-step multimedia training, your students will learn powerful data analysis skills to match today’s growing demand.
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