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.
Upon completion of this course, students receive a micro-credential that can be stacked with Course 2: Machine Learning for Higher Education: Applied Foundations and Course 3: Machine Learning for Higher Education: Advanced Applications to satisfy requirements for the full Applied Analytics and Machine Learning for Higher Education Certificate.