Badge of Practice
Course Description
This course presents a basic introduction to data science and analytics. Discover what data science is, what data scientists do, and why data science is important in the modern world. Learn about techniques used by data scientists to collect, summarize, visualize and draw conclusions from data. Topics for exploration include data types, data sources, data cleaning, Exploratory Data Analysis (EDA), data visualization, machine learning concepts, Big Data concepts, and ethical/legal issues surrounding the use of data. Programming experience or mathematical expertise is not required for this course.
Learning Outcomes
- Define data science and explain its importance.
- Identify and distinguish among types of data (e.g., structured, unstructured, semi-structured, numerical, categorial).
- Describe basic data cleaning techniques and their importance.
- Discover patterns or relationships in data or summarize data by applying Exploratory Data Analysis (EDA).
- Recognize and describe appropriate tools or technologies to visualize data.
- Identify and categorize common machine learning algorithms, including models that implement supervised learning, unsupervised learning, and reinforcement learning.
- Describe Big Data concepts and recognize tools for analyzing Big Data.
- Draw a reasonable conclusion from a data analysis.
- Identify ethical, privacy, or legal issues surrounding the use of data.
Instructor
- Data ScienceĀ InstructorRachel Ashman, MSIT
- University of North Georgia
Teaching:Fall
September
3
Fall
2025
Start Date
January
5
Fall
2026
End DateUpcoming refers to the next academic term after the current one.
January
6
Spring
2026
Start Date
April
30
Spring
2026
End DateDuration
16 weeks
Instruction Method