DATA ANALYTICS

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.

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.

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.

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.

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.

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.

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.

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.

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.