14 Semester Credit Hours; Curriculum: 0158
The certificate provides an introduction to a rapidly growing field of data analytics. It is intended for students and professionals who want to work confidently with data without diving into coding. Through interactive hands-on projects utilizing tools like Microsoft Excel, AI-powered analytics platforms and leading data visualization software, students will learn how to clean, visualize and interpret data to uncover patterns, enabling them to tackle real-world business challenges and drive data-informed decisions. Unlike data science programs that emphasizes coding, algorithms and statistical modeling, this certificate focuses on the practical application of no-code tools and technologies to translate data into business intelligence and communicate results effectively across any industry. This certificate can be completed in one semester.
| Code | Title | Hours |
|---|---|---|
| Courses for a Certificate | ||
| CAB 135 | Electronic Spreadsheeting Using Excel | 2 |
| CAB 235 | Advanced Spreadsheeting Using Excel | 2 |
| CIS 102 | Job Search Principles and Tools | 1 |
| CIS 120 | Introduction to Data Analytics | 3 |
| CIS 240 | Data Visualization Using Tableau | 3 |
| CIS 271 | AI for Business Solutions | 3 |
| Total Hours | 14 | |
Internship (recommended):
An internship is vital for a Data Analytics Certificate as it provides hands-on real-world experience, allowing students to apply their theoretical knowledge, gain practical skills, and build a professional network crucial for launching a successful career in the field. In addition to finding internships on their own, students are welcome to use Oakton's Internship program for assistance. Visit www.oakton.edu/internships or email internships@oakton.edu for more information.
Program Learning Outcomes
- Utilize advanced Excel tools, including pivot tables, data models and analytics tools, to solve real-world business problems.
- Explain the foundational concepts of data analytics, including data cleaning, analysis, and interpretation, to make data-driven decisions.
- Develop interactive data visualizations and dashboards using visualization tools such as Looker or Qlik Sense to communicate insights effectively.
- Design automated workflows and visually compelling data reports using AI-powered tools and visualization platforms to enhance business operations and meet stakeholder needs.
- Analyze business datasets to identify patterns, trends and actionable insights using no-code machine learning platforms.
- Evaluate ethical considerations in data management, including security, privacy and compliance with regulatory standards.
- Utilize job search principles and tools to enhance employability.