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Applied Analytics and Machine Learning for Higher Education
Certificate Program
The Applied Analytics & Machine Learning for Higher Education Certificate is a comprehensive, stackable credential designed to equip educators, administrators, and data professionals with the practical skills needed to transform higher education through data. The three-course professional development pathway is designed for experienced higher education professionals as well as new graduates and career changers who are ready to build skills in analyzing data, coding in Python, and applying both machine learning and Generative AI to solve real-world challenges.
Complete all 3 courses to earn the full certificate. To view details and register for each course, click the links below:
The first course in Applied Data Analytics for Higher Education introduces data platforms Python, Pandas, and Generative AI through hands-on experience with real-world metrics, data visualization, and dashboard creation. Participants learn to make informed decisions and actionable insights that support student outcomes.
The next course, Machine Learning for Higher Education: Applied Foundations, moves into applied machine learning foundations with classification, regression, and vibe coding sessions. Learners are introduced to the machine learning cycle, its application in predicting key student success metrics, and the distinctions between classical statistical methods and modern machine learning in educational contexts.
The final course, Machine Learning for Higher Education: Advanced Applications, deepens learners’ understanding of state-of-the-art machine learning architectures including ensemble methods, advanced techniques, and models. Participants work on a capstone project to design, implement, and present advanced machine learning solutions for complex higher education challenges.
Throughout the program, learners work with real institutional datasets and production-tested code that are directly applicable to their institution’s own data environment. By completing each course, participants earn micro-credentials that can be stacked together for the full certificate in Applied Analytics & Machine Learning for Higher Education, demonstrating the ability to apply data analytics, machine learning, and GenAI to transform education.
For more information, click the "Request Info” button to connect with a program developer.
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
In the full 3-course certificate program, you'll learn to:
Classify how data is used to describe students and institutions in higher education
Perform skills in data wrangling and engineering for educational data analysis
Design numerical and visual outputs that clearly communicate characteristics about students and institutions
Differentiate the fundamentals of machine learning within the context of higher education
Implement machine learning tools to solve real-world problems in higher education
In the Applied Data Analytics For Higher Education course, you'll learn to:
Calculate and apply real-world metrics
Gain hands-on experience with data cleansing & visualization
Make data-driven decisions with actionable insights
Solve challenges for institutions
Apply analytics in different educational settings
In the Machine Learning for Higher Education: Applied Foundations course, you'll learn to:
Understand the fundamentals of supervised machine learning for higher education
In the Machine Learning for Higher Education: Advanced Applications course, you'll learn to:
Build on coding & modeling tools
Implement the machine learning cycle
Use advanced models in the applied context of higher education
Predict key student success metrics
Dive deeper into the architecture of machine learning models
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
On-Demand Info Session
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
Ready to enroll? Choose your course and click the "Add to Cart" button to register!
Course Preview
Unlock a Free Preview of the First 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, the first of three courses in the CSULB's Applied Analytics and Machine Learning for Higher Education certificate program.
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 course in Applied Data Analytics for Higher Education provides hands-on experience with real datasets and industry-standard coding skills.
Applied Data Analytics For Higher Education
Applied Data Analytics for Higher Education introduces the essential tools and methods for analyzing student success data, focusing on key insights that drive student success. Beginning with the fundamentals of higher education metrics and the student journey, learners progress to firsthand coding with Jupyter Notebooks, Python, and Pandas as well as data visualization with Matplotlib and Plotly. Practical applications include building, cleaning, and analyzing real higher ed datasets to explore enrollment, retention, GPA, graduation, and performance metrics.
By the end of this self-paced online course, participants will have created their first interactive student success dashboard and developed the technical foundation needed for advanced analytics and machine learning in higher education. This 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.
You Will Learn To:
Calculate and apply real-world metrics
Gain hands-on experience with data cleansing & visualization
Make data-driven decisions with actionable insights
Class Schedule for Section Number CPIE374.(2262-1)
Date
Time
Day
Meeting Type
Location
Jan 12, '26
12:00PM - 12:01PM
Monday
Lecture
Virtual Classroom, Online
CSULB Online
Apr 24, '26
12:00PM - 12:01PM
Friday
Lecture
Virtual Classroom, Online
CSULB Online
Machine Learning for Higher Education: Applied Foundations
This course equips professionals with the practical skills to apply machine learning techniques that drive real impact on student success. Using Python and Scikit-learn, participants will learn to prepare higher education datasets, build and refine classification and regression models, and deploy predictive tools that forecast student retention, GPA, and progression.
Through a mix of guided lessons, code briefs, and interactive vibe coding sessions, learners gain experience in data wrangling, feature engineering, model evaluation, and Generative AI applications. Participants design and interpret predictive models to address real-world challenges in higher education, building technical expertise as they begin to compile a portfolio to showcase applied machine learning solutions.
Upon completion of this course, students receive a micro-credential that can be stacked with Course 1 and Course 3 to satisfy requirements for the Applied Analytics and Machine Learning for Higher Education Certificate.
You Will Learn To:
Understand the fundamentals of supervised machine learning for higher education
Class Schedule for Section Number CPIE375.(2263-1)
Date
Time
Day
Meeting Type
Location
May 4, '26
12:00PM - 12:01PM
Monday
Lecture
Aug 14, '26
12:00PM - 12:01PM
Friday
Lecture
Machine Learning for Higher Education: Advanced Applications
The final course, Machine Learning for Higher Education: Advanced Applications, deepens learners’ understanding of state-of-the-art machine learning architectures including ensemble methods and advanced machine learning techniques and models. Participants work on a capstone project to design, implement, and present advanced machine learning solutions for complex higher ed challenges. With a focus on real-world student success metrics like retention, GPA, and progression, participants gain hands-on experience preparing complex datasets, tuning hyperparameters, and interpreting results for meaningful impact on their own institution’s data systems.
The course concludes with a capstone project, giving learners the opportunity to design, implement, and present an advanced applied machine learning solution for higher education. By completing this course, participants earn a stackable micro-credential that, alongside the previous two courses, fulfills requirements for the Applied Analytics and Machine Learning for Higher Education Certificate.
You Will Learn To:
Build on coding & modeling tools
Implement the machine learning cycle
Use advanced models in the applied context of higher education
Predict key student success metrics
Dive deeper into the architecture of machine learning models
