Data Science für Python: NumPy, Pandas, Matplotlib & SciPy

📚 **Course Review Summary** **Pros:** - **Einsteigerfreundlich:** The course is highly regarded for its suitability for beginners, especially those with limited programming experience or specifically in Python. It's praised for providing a solid foundation and offering a gentle learning curve, making complex concepts more approachable. - **Quality Production:** The course content is delivered in high-quality videos, ensuring that the material is both informative and engaging. - **Comprehensive Overview:** It offers a comprehensive overview of essential libraries such as NumPy, Pandas, Matplotlib, and SciPy, allowing learners to grasp the basics effectively. - **Structured Approach:** The course is structured in a way that provides a clear learning path or "red thread," which helps students to follow along with ease. - **Supplementary Materials:** Additional resources are included, contributing to a well-rounded learning experience. **Cons:** - **Assumption of Prior Knowledge:** Some reviewers feel that the course assumes certain basic knowledge (e.g., understanding of loops and lists) without providing sufficient introduction or explanation. - **Inconsistencies in Content:** There are instances where the content is described as confusing or where solutions to problems are not fully explained, leaving some areas unclear for learners. - **Mathematical and Packet-Specific Explanations:** Some mathematical concepts and specific packet explanations are oversimplified or lacking in detail. - **Tutorial Accuracy:** A particular tutorial on creating line diagrams in Matplotlib was criticized for using an incorrect approach to handle outlier data. - **Additional Resources Limitations:** While additional materials are available, the lack of a Q&A forum within the course itself is noted as a significant limitation, as students have to rely on external platforms or services for support. **General Feedback:** The overall feedback for this course is positive, especially considering it's a free offering. Most users appreciate the effort to make complex topics accessible to beginners. However, some suggest that there are certain prerequisites or expectations of prior knowledge that should be made more explicit to potential learners. The course also receives praise for its clarity and structured approach but is criticized for inconsistencies and a few inaccuracies within the content. Users express their satisfaction with the course for what it offers, yet also note areas where it could be improved, particularly in the way mathematical concepts are taught and by providing a dedicated Q&A platform. **Recommendation:** For beginners looking to get started with data science tools in Python, this course is recommended as a solid introductory resource. However, users who already have some programming knowledge or those expecting comprehensive explanations of advanced topics might find it somewhat basic and may want to look for more advanced resources. Overall, the course is a valuable tool for beginners and is commended for its free availability.