Course

Course Summary
Credit Type:
Course
ACE ID:
PATH-0010
Organization:
Location:
Online
Length:
6 weeks (192 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 introduction to statistics
Description

Objective:

The course objective is to introduce students to the foundational statistical concepts that business and data analysts use every day. Students will begin by describing datasets with summary statistics and simple data visualizations before moving on to some of the more powerful techniques made possible by statistics: statistical inference and predictive analytics. Through probability, hypothesis testing, and linear model building, students will use limited data to draw conclusions about the underlying patterns that drive everyday phenomena, from rideshare tipping behavior to the changing temperature of the Earth.

Students will primarily use Google Sheets to apply these statistical techniques to data, though they will have exposure to Python via Google Colab notebooks throughout the course. Note: This course does not require any knowledge of programming or the Python language.

Learning Outcomes:

  • Perform exploratory data analysis and report insights using descriptive statistics and visualizations.
  • Perform inferential data analysis by computing confidence intervals and construct a simple predictive model.
  • Use probability distributions to model data and run an A/B test to determine the significance of statistical differences between two samples of data.
  • Use linear regression to characterize relationships between variables and make predictions.
  • Examine correlation coefficients and residuals to determine the significance of relationships between variables.

General Topics:

  • Descriptive statistics Computing confidence intervals Creating a predictive model Probability distributions Linear regression
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Coaching/Mentoring
  • Computer Based Training
  • Discussion
  • Laboratory
  • Learner Presentations
  • Practical Exercises
  • Project-based Instruction

Methods of Assessment:

  • Case Studies
  • Performance Rubrics (Checklists)
  • Presentations
  • Quizzes

Minimum Passing Score:

70%
Supplemental Materials

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