Course Reviews for
Harrisburg University
8/10
average rating
no filters applied
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1 /10
Major: M.S. Project Management
PMGT 530 Risk, Procurement and Contracts
Instructor: Brian Grey
Delivery: onsite
1 /10
Very well trained lecturers and good instrument teaching object for learning
09/29/2026
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7 /10
Major: M.S. Analytics
ANLY 540: Natural Language Processing - Semantic Representations
Instructor: Kayla Jordan
Delivery: onsite
7 /10
This course meets twice a week, with one class on a weekday evening and another longer session on the weekend. The professor is knowledgeable and has a strong PhD-level background in the subject. The course is organized and the professor has specific expectations for assignment formatting, so it is important to follow the instructions carefully. The homework difficulty is moderate. In many cases, the code is provided or guided, so the main focus is not only writing code but also understanding the results and explaining the interpretation. The course covers topics such as topic modeling, LDA, and deep learning. One thing to note is that, in the current LLM era, some of the course content may feel a bit outdated compared with the latest developments in generative AI and large language models. However, it can still be useful as a foundation for understanding earlier NLP and language modeling concepts.
09/27/2026
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7 /10
Major: HCID
HCID 570 Design Patterns and Contexts
Instructor: Nathan Aileo
Delivery: onsite
7 /10
Prof. Aileo is very flexible and doesn´t give a crazy amount of work. However, the classes are mostly led by students. Each class has a presentation and then breakout rooms. It's hard to pass the class if you´re not connected. I would appreciate more time of lecture.
09/27/2026
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6 /10
Major: M.S. Analytics
ANLY 530 Machine Learning I
Instructor: Jonathan Korn
Delivery: online
6 /10
This course provides an introduction to applying machine learning models using either R or Python. The assignments are generally not too difficult and usually involve implementing different ML models. However, the instruction may feel somewhat limited, so students should expect to do some self-learning in order to fully understand the models and complete the assignments. The course does include a final project. For the final project, students need to apply different machine learning models to a classification problem. The project requires finding your own dataset, cleaning data, writing code, preparing a paper, creating presentation slides, and giving a presentation. Overall, the course is manageable, especially for students who are comfortable learning independently and practicing ML models through hands-on assignments.
08/10/2026
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5 /10
Major: M.S. Learning Technologies and Media Systems
LTMS 510 Learning Technologies and Solutions
Instructor: Brian Merrill
Delivery: onsite
5 /10
The class does not require full participation, but you need to interact sometimes. The teacher gives assignments that do not fully prepare you for. And is a very slow grader. Not difficult, but it can be hectic.
08/01/2026
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10 /10
Major: M.S. Project Management
PMGT 510 Principles of Project Management
Instructor: Anjali Barnick
Delivery: onsite
10 /10
I completed my first semester at HU for the MS in Project Management with Prof. Barnick, who taught us PMGT 510 (Principles of Project Management) and PMGT 511 (Large Language Models in Project Management). The presentation slides she curated for each topic were incredibly helpful; she'd walk us through a concept in its entirety, along with exactly what each assignment required and how to shape our work to meet her expectations. She's not a tough grader at all, but she places strong emphasis on conceptual clarity and on critical and analytical thinking. You get a lot of freedom to challenge ideas in her class and to express yourself confidently and freely. What also set her apart was her support outside of class: she answers messages, clears doubts, and responds to questions patiently, and she always points you to extra resources whenever you want to go deeper on a topic. She cleared up every "what if" I walked in with, and that patience made a genuine difference for someone starting from scratch. She never reuses an example twice. Her illustrations come from across her teaching career, her project management career, her real estate experience, and her everyday household and societal observations. She's remarkably well-informed and puts real effort into keeping every class interactive rather than monotonous. She's also super flexible and appreciates being informed ahead of time. Above all, she's chill, friendly, and approachable, and her energy is contagious. By the final week, our class refused to split up into different courses, and we all enrolled in the same ones just to carry the spirit she'd created into the next professor's class. I would recommend Prof. Barnick without hesitation to anyone who values conceptual clarity, real-world examples woven back into the coursework, meaningful group activities, and a class that interacts with and challenges one another. She's patient, insightful, engaging, and never mechanical or boring. She made grad school genuinely fun.
07/29/2026
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9 /10
Major: M.S. Analytics
ANLY 502 Analytical Methods I
Instructor: Brian Myers
Delivery: online
9 /10
Be prepared for a heavy amount of assignments. However, the course has a lot of useful information and materials for your career. Professor is easy to reach out and fast to response. Overall, it’s still a great course, bur be prepared for the workload.
07/28/2026
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8 /10
Major: M.S. Learning Technologies and Media Systems
LTMS 500 Macro Instructional Design
Instructor: Mark Moore
Delivery: online
8 /10
The teacher was very nice and calm, and always took the time to explain everything clearly. The assignments were straightforward and easy to follow. My only critique is that grading took a while, which made it harder to correct work using the feedback since so much time had passed by the time it came back.
07/27/2026
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7 /10
Major:
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Delivery: onsite
7 /10
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07/23/2026
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8 /10
Major: M.S. Analytics
ANLY 512 Data Visualization
Instructor: Alan T. Hitch
Delivery: onsite
8 /10
This course is a practical R-based data visualization class. The main reference is R for Data Science from 2017, which is a very useful book and worth reading; it is available through O’Reilly. Assignments are usually due every 2–3 weeks and are completed in R Markdown. For submission, you mainly need to upload the HTML file generated from the RMD file, rather than submitting through RPubs. There are no exams in this course. The final project requires you to collect data about yourself and create visualizations based on that data. At least one visualization in the final project needs to be interactive. Overall, the course is manageable if you are comfortable learning R and keeping up with the assignments.
06/26/2026
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