Database Management & Design
  • Type Validation
  • Level Intermediate
  • Time Weeks
  • Cost Paid
Additional Details

Database Management & Design

Issued by Oklahoma State Regents for Higher Education (OSRHE)

This credential paced course connects classroom math to the real world. Students will learn how to use statistics to find facts in data and understand how to use that information honestly. By the end of the class, they will be ready to use what they’ve learned to solve problems and come up with new ideas in science and engineering.

  • Type Validation
  • Level Intermediate
  • Time Weeks
  • Cost Paid
Additional Details

Skills

Earning Criteria

  • Complete DATA 2113 Database Management & Design
  • Course introduces statistical methods and their applications in science and engineering and covers fundamental statistical concepts, including descriptive statistics, probability, and inferential statistics. Students will learn to apply statistical techniques to analyze and interpret data, design experiments, and solve real-world engineering and scientific problems. Key topics include probability distributions, hypothesis testing, regression analysis, and quality control.
  • The course emphasizes the use of statistical software for data analysis and visualization, enhancing students’ proficiency in tools such as R or Python. Through hands-on projects and case studies, students will develop critical thinking and problem-solving skills, enabling them to make data-led decisions in their respective fields.
  • By the end of the course, students will be able to communicate statistical findings effectively, understand the ethical considerations in data analysis, and appreciate the role of statistics in quality control and reliability engineering. This course prepares students for more advanced studies and professional practice in science and engineering disciplines.
  • Skills: 1) Statistical Programming: Proficiency in using software like **R or Python** to automate data analysis and create professional visualizations. 2) Hypothesis Testing and Regression: The ability to use mathematical models to find relationships between variables and test if results are statistically significant.
  • Skills 3) Experimental Design: Skill in setting up scientific tests and engineering experiments to ensure the data collected is valid and useful. 4) Quality Control and Reliability: The technical ability to use statistics to monitor production standards and predict the lifespan or success rate of engineering systems.
  • Data-Driven Decision Making: Students will be able to move beyond guesswork, using hard data and probability to make informed choices in science and engineering contexts.
  • Statistical Communication and Ethics: The competence to explain complex findings to non-experts while maintaining honesty and transparency in how data is handled.
  • Critical Problem Solving: Students will be able to take a messy, real-world problem and break it down into a statistical question that can be solved with logic and evidence.