New England College

Easy assignment. He gave almost full points for all assignments if you finished on time.

He is a nice guy. It is easy and interesting to take this course.

Easy to get an A

**Review of CRN112 Virtual Orientation** The **CRN112 Virtual Orientation** class was a well-structured and informative introduction to the academic environment. The quality of the course was commendable, as it effectively covered essential topics such as university resources, academic expectations, and student success strategies. The materials were well-organized, making it easy to follow along and absorb the necessary information. Additionally, the use of multimedia elements, such as videos and interactive discussions, helped keep the content engaging. Classroom discipline was well-maintained despite the virtual setting. Clear guidelines were set from the beginning, ensuring that students remained respectful and participated actively. The instructors moderated discussions effectively, fostering a supportive and professional learning environment. Expectations regarding attendance and engagement were communicated upfront, which helped keep students accountable. Overall, the **CRN112 Virtual Orientation** successfully achieved its goal of preparing students for their academic journey. The course provided valuable insights and practical guidance, making the transition into the program smoother. While the class was generally well-paced, incorporating more live Q&A sessions could enhance interaction and allow students to clarify doubts in real time. Nonetheless, the course was an essential and well-executed introduction to the academic experience.

Internship 1: Data Analytics has been an invaluable learning experience, bridging academic knowledge with real-world application. The course structure emphasizes hands-on experience, allowing students to apply data analytics techniques in a professional setting. The quality of the class is excellent, particularly for those seeking practical exposure to industry practices. The homework load is moderate but meaningful—assignments focus on real datasets and analytical problems, reinforcing concepts covered in class. While the workload can be demanding at times, it is manageable and contributes significantly to skill development. Classroom discipline is well-maintained, ensuring a productive learning environment. The professor enforces clear expectations regarding attendance and participation, which helps maintain engagement. Attendance is strongly encouraged, if not mandatory, as discussions often include insights and case studies not found in the readings. The professor’s teaching style is structured yet adaptable, blending lectures with interactive sessions and industry examples. They encourage student participation and provide constructive feedback, which enhances learning. Additionally, their expertise in data analytics makes the course material both relevant and engaging. Overall, this course is highly beneficial for students pursuing careers in data analytics. It provides a strong foundation in practical skills while reinforcing theoretical knowledge. The balance between coursework and hands-on experience makes it an excellent opportunity for professional growth. I would highly recommend this class to anyone interested in applying data analytics in a real-world setting.

Its an interesting, fun course where we have to pick up a company and then identify a new product that we can launch into their ecosystem and then analyze the product further over the course

As this is a online course, I wanted to say manly about the assignments. The asssignments are very intensive as they are require us to analyze the balance sheets by connect our account to Mcgraw Hill and every student has their own set of questions. The professor overall is very helpful when needed.

Would recommend this teacher.

The Data Science Tools class offers an excellent introduction to essential tools and techniques for anyone pursuing a career in data science. The quality of the class is top-notch, as the curriculum is well-structured and balances theoretical concepts with practical application. The course covers key tools such as Python, R, and Jupyter Notebook, and provides ample opportunities to practice through hands-on assignments. The homework load is manageable but requires consistent effort. Weekly assignments are designed to reinforce class topics and push students to apply what they've learned. While the workload might feel intense at times, it ultimately enhances understanding and ensures skill development. Classroom discipline is well-maintained, creating an environment conducive to learning. The professor sets clear expectations and enforces policies, such as no use of distractions like phones during lectures. Attendance requirements are strict but fair, and active participation is encouraged, which helps in building a collaborative learning atmosphere. The professor's teaching style is engaging and effective. They make complex topics approachable by breaking them down into simpler concepts and providing real-world examples. They are also approachable outside of class and genuinely invested in student success. Overall, this class is a valuable learning experience, especially for students passionate about data science. The balance of rigor and support makes it challenging yet rewarding, and it leaves students well-prepared to use essential tools in future projects or professional roles.