Mastering data analytics with Python coding and Generative AI has become critical to achieve educational success. In just a few weeks, this Applied Data Analytics for Higher Education course will provide the tools and knowledge needed to become a data-savvy professional in higher education.
This online course will explore higher education data with an understanding of the student journey, focusing on key insights that drive student success. By learning to calculate and apply real-world metrics, you’ll gain actionable insights and make data-driven decisions to solve meaningful challenges for institutions. Hands-on experience with tools and techniques like data cleansing and visualization will instill the confidence to apply analytics in any educational setting.
This self-paced course features videos, quizzes, assignments, and other activities that lead to the creation of dashboards and code notebooks for educational use. Upon completion of course requirements, students will earn a certificate of completion and a digital badge.
Whether you're a current student, new to data science, or an expert in higher education, this course will make data analytics simple, practical, and impactful—helping you to transform education with data-driven decisions.
Flexible, self-paced online learning
Structured modules that build sequentially
Hands-on learning with real higher education data
Step-by-step instructions on how to analyze and prepare data
Requires approximately 8–9 hours per week over 15 weeks
Higher education professionals who want to utilize machine learning and advanced analytics
Professionals who are transitioning from other industries into higher education careers
Students and job seekers who need to build skills in institutional research and analytics
Lifelong learners looking for a self-paced opportunity to understand data analytics
How to work with datasets from higher education institutions to uncover actionable insights by analyzing metrics like admissions, enrollment, and graduation rates
How to collect data for student success metrics calculations, visualizations, and machine learning applications
How to design dynamic dashboards using Matplotlib and Plotly to transform complex higher education data into clear insights for institutional success
Program Outline
Master data analytics with Python and AI to drive educational success. In just a few weeks, you’ll learn how to harness real-world higher ed data to gain actionable insights and make data-driven decisions for institutions.
Introduction to Higher Education
Explore the foundational role of institutional research in transforming data into actionable insights. Learn about the history and evolution of analytics in higher education and the critical metrics driving student success.
In this module, students will explore:
What is higher education?
Data collected and metrics calculated throughout the student journey
Why student success metrics are important
21st Century Coding for Higher Education Data Analytics
Dive into Python programming for higher education data analytics, learn to navigate Jupyter Notebooks, leverage Generative AI for coding assistance, and use Pandas and Matplotlib to analyze and visualize datasets.
In this module, students will learn:
An introduction Coding in Jupyter Notebooks
An introduction to Python’s basic data types & logic
Python programing basics
Pandas and the 4 Key Datasets to become an expert
Foundations for visualizing higher education data in Pandas & Matplotlib
Mastering coding with Generative AI
Magic Pandas Library: Mastering Higher Education Data Preparation and Analysis
Build confidence with Pandas for data manipulation and gain hands-on experience analyzing critical higher education metrics like admissions, enrollment, and GPA using real-world datasets.
In this module, students will develop skills in analyzing:
Admission Data: Student Admission Metrics
Enrollment Data: Student Headcount, FTES, and AUL
Performance Data: Student Grade Point Average (GPA) Calculation
Performance Data: DFW Rate Calculations
Graduation Data: Student Graduation Rates
Preparing Higher Education Data for Advanced Analytics and Machine Learning
Synthesize skills from earlier modules to prepare data for machine learning, create interactive visualizations with Plotly, and design a comprehensive student success dashboard for higher education insights.
In this module, students will apply skills in:
Curating DataFrames for downstream analysis
Interactive visualization of higher education data with Plotly
Case Study: Your First Student Success Dashboard
Instructors
As the Assistant Vice President for Institutional Research and Analytics at California State University, Long Beach, Mahmoud Albawaneh, PhD, is passionate about leveraging data, machine learning, and AI to foster student success and drive educational innovation. With a diverse background spanning aerospace, manufacturing, consulting, and higher education, he brings a unique blend of analytical expertise and strategic insight to the table. He is committed to shaping the future of education by harnessing technology to create meaningful outcomes.
Mahmoud Albawaneh, Assistant Vice President, Institutional Research and Analytics, CSULB
Juan Carlos Apitz (MSc, MBA) is an Associate Director of Academic Planning and Enrollment Analytics Experienced Modeler with a demonstrated history of working in the higher education industry. He is a skilled professional in Python, Statistical Modeling, Social Network Analysis and R. Strong predictive analytics. He earned a Master of Science (MSc) focused in Applied Statistics.
Juan Carlos Apitz, Associate Director of Academic Planning, CSULB
Dr. Kagba Suaray, PhD, is currently in his 20th year as a professor of Mathematics and Statistics at California State University, Long Beach. In this position, he serves as Graduate Advisor for one of the largest Applied Statistics masters programs in the state. As co-PI of the Long Beach - Compton Data Science Learning Community and Southern California Consortium for Data Science projects, he has been a leader in advocating for P-20 data science pathways for Black and Brown students.
Kagba Suaray, Professor of Mathematics & Statistics, CSULB
Frequently Asked Questions
The Applied Data Analytics for Higher Education course starts on June 9, 2025.
The cost is $750 including class materials.
The Applied Data Analytics for Higher Education course is a non-credit course. Certificate of Completion and digital badges may be issued upon successful completion of the course.
To excel in this Applied Data Analytics for Higher Education course, the student should understand the key objectives and concepts like data collection, analysis, and interpretation; actively participate in lectures, discussions, and projects to apply knowledge; pass quizzes with at least 75% and complete all assignments, including the Capstone Project; practice using data analytics tools regularly; stay organized and meet deadlines; seek feedback from instructors and peers to improve continuously; and stay committed and proactive to succeed.
A Capstone Project is a final assignment that students typically complete at the end of their academic program. It's designed to showcase their knowledge and skills by solving a real-world problem or conducting in-depth research on a specific topic. Think of it as a big, culminating project that brings together everything they've learned during their studies. It's like the grand finale of their educational journey!
All students are required to have access to:
Laptop and power cord for online and in-person
To use an AI assistant within Vocareum, access an LLM like OpenAI ChatGPT, Google Gemini, or Microsoft Copilot via an API key. Alternatively, create an account with your preferred LLM and open it in a separate window for easy cutting and pasting.
Compatible web browser
Zoom (for optional online office hours)
Canvas-supported browsers and basic Canvas/LMS proficiency
Reliable high-speed internet
This course assumes no prior knowledge of higher education and is designed to be accessible to students with a basic understanding of first-year college-level mathematics or statistics.
Students should be ready with:
Self-motivation and independent learning skills
Time management abilities
Willingness to actively participate online
No, we do not provide job placement. However, jobs may be shared with students on Canvas, our online learning management system, as well as via social media channels such as LinkedIn.
The total course duration is estimated at 40-135 hours. This is a self-paced course with a deliberately sequential order. Each module builds upon the previous ones. It has 4 modules with 68 lessons, 42 quizzes, and 17 activities.
Building an AI-Powered IRA Assistant Using ChatGPT or GenAI
Tuesday, May 20, 2025 at 5:00 p.m. (Pacific) via Zoom
This online webinar will offer a practical, step-by-step guide to creating an Institutional Research & Analytics (IRA) Assistant using ChatGPT or other generative AI tools. Participants will learn how to upload and work with four standard datasets—Admissions, Enrollment, Completions, and Courses—and ask questions. The assistant will generate accurate and actionable metrics such as Headcount, FTES, Retention Rate, and Graduation Rate. This session is ideal for IRA professionals looking to streamline data analysis and make insights more accessible and immediate across campus.
Speakers:
Mahmoud Albawaneh, Assistant Vice President, Institutional Research and Analytics, CSULB
Juan Carlos Apitz, Associate Director of Academic Planning, CSULB
Kagba Suaray, Professor of Mathematics & Statistics, CSULB
Thank you for your interest in our Applied Data Analytics for Higher Education course!
This free preview serves as an introduction to the more comprehensive 15-week course, which provides hands-on experience with real datasets and industry-standard coding skills.
Got questions? Click the "Ask a Question" button and a member of our team will get back with you!
The following PDF shares an overview of the student journey, including what data and metrics we use to support student success.
Applied Analytics for Higher Ed - Admissions Metrics
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Course Preview
Unlock a Free Preview of This Course!
Are you looking to make data-driven decisions to improve student outcomes at your institution?
These free course materials will provide a visual preview of the student journey through higher education and the critical data points you can leverage at each stage. The infographic and video will give you a short introduction to the more comprehensive Applied Data Analytics for Higher Education program offered by California State University, Long Beach.
When you fill out the form above, you'll gain immediate access to a short video and an infographic that provide:
A clear visualization of the complete student lifecycle, from application through job placement
Specific metrics collected at each stage of the student journey
Formula breakdowns for calculating key retention, graduation, and success rates
Detailed examples of data points to track across demographics, academic performance, and student progression
Whether you're an administrator, institutional researcher, or academic advisor, these resources provide the foundation for implementing data analytics practices that can help transform student outcomes at your institution.
The full, 15-week course in Applied Data Analytics for Higher Education provides hands-on experience with real datasets and industry-standard coding skills.
