Introduction to Data Science
This three-part series introduces the basics of data science and how it uses statistics, computer science, and analytical thinking to find insights in data. You will learn what data scientists do, from defining problems and exploring data to building models and sharing results through visuals and storytelling. The series also explains the work done by professionals like statisticians, computer scientists, and engineers, and the tools they use. It covers why ethics, collaboration, and critical thinking are crucial in this field.
Lessons, videos, and short quizzes will help you understand key ideas such as structured and unstructured data, predictive modeling, and data visualization. By the end, you will know how to ask the right questions, study the data, and explain findings that support better decisions in any field.
Use the following links to register for the three parts of Introduction to Data Science Certificate and the learning assessment.
Note: You can add these courses to your cart one by one and complete checkout once.
Data Science, Critical Thinking, and Problem Solving
Problem Solving with Dimensional Analysis,
Descriptive Statistics, Charts and Graphs
Python Programming for Data Science
This three-part series teaches Python programming specifically for data science applications. The first certificate covers Python fundamentals including variables, data types, operations, and input/output methods. You learn control flow with if statements and loops, plus object basics that form the foundation of Python programming. These core concepts prepare you to write functional Python code from scratch.
The series then advances to functions and error handling, showing you how to create reusable code blocks and manage program errors effectively. You work with essential data structures like lists and arrays before moving into pandas, Python's powerful data analysis library. The final certificate focuses on pandas operations, from basic data manipulation to advanced techniques for filtering, merging, and analyzing datasets. By completing all three certificates, you gain the Python skills needed for practical data science work in any industry.
Use the following links to register for the three parts of Python Programming for Data Science Certificate and the learning assessment.
Note: You can add these parts to your cart one by one and complete checkout once.
Getting Started with Python for Data Science
Credit for Prior Learning Requirements
Participants who successfully complete the learning assessment for Introduction to Data Science may earn up to 3 hours of college credit for DASC 10003 - Introduction to Data Science through the Credit for Prior Learning Program.
Participants who successfully complete the learning assessment for Python Programming may earn up to 3 hours of college credit for DASC 16003 - Python Programming for Data Science through the Credit for Prior Learning Program.
Those who pass the learning assessment may apply for college credit after:
- Being admitted to the University of Arkansas, Fayetteville.
- Earning a minimum of 3 credit hours with a minimum 2.0 GPA.
- Submitting a Credit for Prior Learning form within five years from the date the assessment was successfully completed.
Students will be assessed a non-refundable fee when credit for prior learning is applied. The undergraduate fee is $30 per credit hour. The student will receive a mark of "CR" for the course on their academic record, and the credit hours will not be factored in when calculating the student’s GPA.