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Course Description

Attention: 100% Financial Assistance to Students in Need!

  • Higher Education Emergency Funds (HEERF) up to $1,300 are available to assist micro-credential students with the cost of educational expenses, including tuition and fees, food, housing, course materials, technology, health care and childcare.
  • You will need to make a deposit of $50 to secure your spot in the course.
  • Please indicate financial need on the questionnaire at checkout.
  • The final bill is due on November 18, 2021.

Course Description:  This micro-credential course will teach students statistical literacy in the context of a business setting. The course consists of three five-week modules. The first module focuses on the fundamentals of applied data analysis tools. This module covers descriptive statistics and basic statistical tests that are commonly used in a business setting, such as chi-square analysis, a two-sample t-test, ANOVA, linear regression, and logistic regression. The second module focuses on big data and predictive analytics. This module covers experimental design and model selection. Additionally, this module discusses data cleaning and the steps that need to occur with data – especially big data – prior to running an analysis. The third module focuses on data visualization. This module covers visualization approaches such as the pie chart, histogram, scatterplot, and bar plot. Visual story-telling and the art of how to communicate statistical concepts to a non-quantitative audience will be emphasized. Throughout the course, students will be using the software R Studio to analyze real-world data sets. After taking this course, students will be in a position to approach a data set and perform an analysis that tells the story behind the data; this then will provide key stakeholders important information that will allow them to make an optimal business decision.

After taking this course, students will be able to:

  • Explain the concepts of descriptive statistics and use sample statistics to make inferences about population characteristics. 
  • Understand statistical processes and choose which process to use for particular data analysis applications.
  • Interpret statistical results as a basis for decision making.
  • Translate a business question into a predictive analytic algorithm. After selecting the appropriate predictive analytic method, will be able to obtain results to answer business questions.
  • Apply data visualization best practices to business settings while avoiding visualization techniques that can mislead an audience.
  • Communicate insights about data in various formats, including oral presentations, written reports, and visualizations.
  • Prepare professional reports and make effective and informative presentations.
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