These three courses cover different topics related to data and analytics and do so in different ways.
Business Analytics teaches participants to apply basic statistics to real business problems and includes hands-on practice implementing analyses in Excel. The course covers descriptive statistics, sampling and estimation, hypothesis testing, and regression analysis. The course is intended for individuals at all stages of their careers who would like to strengthen their analytical skills, including college students and recent graduates without a background in statistics, those considering an MBA or other graduate program, or professionals seeking data literacy.
Data Science Principles introduces key concepts in data science—such as prediction, causality, visualization, data wrangling, privacy, and ethics—but does so without coding or mathematical application. The course is intended for organizational leaders and managers to be prepared to act on data analysis and to decide whether data science applications are appropriate tools for their businesses or organizations. The course is also well suited for business operations specialists to understand the building blocks of basic data visualization.
Data Science for Business moves beyond the spreadsheet and provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information. Starting with the questions you need to ask when using data for decision-making, this course will help you know when to trust your data and how to interpret the results. By the end of the course, you should understand how to create a data-driven framework for your organization or yourself; develop hypotheses and insights from visualization; identify data mistakes or missing components; and, speak the language of data science across themes such as forecasting, linear regressions, and machine learning to better lead your team to long-term success. You will learn how to create a compelling story that uses proven, collected data to make core business decisions, and explore coding environments such as R and visualization software.
