assignment problems

Labs don't work

Very very basic

I recently joined the "Python for Data Science, AI & Development" course as part of the "IBM Data Science Professional Certificate" specialization on Coursera, and I am thoroughly impressed with the quality and depth of the content. This course is a fantastic introduction to Python, especially tailored for those interested in data science and AI development. The instructors did an excellent job of breaking down complex concepts into easy-to-understand segments, making it accessible even for beginners. The hands-on labs and assignments were particularly beneficial, allowing me to apply what I learned in real-world scenarios. The Jupyter Notebooks provided a practical environment for coding, which helped reinforce the learning experience. I appreciated the comprehensive coverage of Python libraries such as Pandas, Numpy, and Matplotlib. These tools are essential for data manipulation and visualization, and the course provided clear, practical examples of how to use them effectively. The integration of AI topics added a unique dimension, highlighting Python's versatility in this cutting-edge field. Overall, the "Python for Data Science, AI & Development" course is well-structured, engaging, and highly informative. It has equipped me with valuable skills that I can apply in my current projects and has significantly boosted my confidence in using Python for Data Science and AI Development. I highly recommend this course to anyone looking to enhance their programming skills and dive into the world of data science and AI.

IBM's Python for Data Science, AI & Development course on Coursera is an exceptional learning experience. This comprehensive course equips students with the knowledge and skills required to excel in the rapidly evolving fields of data science, artificial intelligence, and development using Python. The course curriculum is thoughtfully structured, beginning with the fundamentals of Python programming and gradually delving into more advanced topics like data analysis, machine learning, and AI. Each module is well-organized, with clear explanations and hands-on assignments that reinforce learning. What sets this course apart is its practicality. Real-world examples and industry-relevant projects allow you to apply what you've learned in a meaningful way. The instructors are knowledgeable, and the peer-graded assessments encourage collaboration and deeper understanding. Furthermore, IBM's reputation in the tech industry adds credibility to the course, making it a valuable addition to your resume. Whether you're a beginner or have some Python experience, this course provides a solid foundation and the confidence to tackle complex data-driven projects. In conclusion, IBM's Python for Data Science, AI & Development course on Coursera is a game-changer for those looking to thrive in the world of data and AI. It's an investment in your future that offers both knowledge and practical skills to advance your career.

This course was a perfect introduction to python for data science. I already have a B.S. in political science which required a few semesters of statistics. We mainly used Excel and SPSS. I wish I had taken a course like this because I’ll say that I much prefer Python to SPSS and Excel. I find Python more functional but far less user friendly. What helped a lot here was that I have a background in windows and pc hardware. I also have a little experience with Linux and .bash scripting. I’ll admit, this course would have been much more difficult without the computer knowledge I already had.

I’m currently working full-time trouble shooting large 3D printers 40 hours a week. I’ve been pondering what to go to graduate school for. This course has helped with that decision. I’m leaning toward a masters in the applied data science.

I plan on taking the other data science and applied data science courses on Coursera as well. Any and all continued learning I can get will be valuable.

What was most challenging? Learning the syntax and structure of the python language. I’m still learning it and it’s going to take quite a lot of effort to master it. Attention to detail is an absolute must in programming or coding—albeit a short script or manipulating a data set.

Also, I found that the Anaconda suite was the best choice to complete the course. It was a little more user friendly than the bare-bones IDLE/Python combination.

Content Quality: The course should cover a broad range of topics from basic to advanced Python concepts such as data types, data structures, control flow, functions, classes, modules, and popular libraries/frameworks (e.g., NumPy, Pandas, Flask, Django). Clarity and Explanation: The instructor should be able to explain concepts clearly and effectively, catering to learners of different levels. Complex topics should be broken down into understandable chunks with practical examples. Engagement: The course should keep learners engaged through interactive elements like quizzes, coding exercises, and projects. Projects and Exercises: Hands-on projects and exercises are crucial for reinforcing learning. They should be diverse, challenging, and relevant to real-world applications. Community and Support: A supportive community or forum where learners can ask questions, share insights, and collaborate can enhance the learning experience significantly. Updates and Relevance: Python is an evolving language, so the course content should be regularly updated to keep up with the latest language features, best practices, and trends in the Python ecosystem. Reviews and Feedback: Checking reviews and feedback from previous learners can provide insights into the course's strengths and weaknesses.

I was a complete newbie to Python, and coding in general, and this course made it easy for even a beginner like me to understand. I would honestly love to take an extended version of this course. That said, I have recommendations for improvement:

1) the labs didn't really make you think terribly hard about how to solve the questions, and I would have loved more complex lab work, especially because of the next point...

2) The complexity of the final project basically skyrockets from the rest of the course work. I feel like an extra week or two of going over the additional knowledge necessary to really succeed in the final project without major struggle would help tremendously. Conceptually, it seemed like it should be REALLY easy... if only I had a little more applicable practice work under my belt before hand. (I finished it successfully, but it was a bigger struggle than it perhaps should have been. I think many other people are in the same boat.)

This action-pack course is exactly what I am looking for. It's down-to-earth and practical. Instructors explain with videos once, then you get walk through in the labs, like a step by step guide.

The videos are of bitesize length so it's easier to concentrate on the concepts., and followed by quizzes that ask only the essentials. i love the part that i can experiment with the code myself after researching the concepts further on the internet.

It may be pretty demanding for complete beginners because each concept is introduced very succinctly, so if you have no clue with python at all, i think you need to research extra a lot in order to understand the concept. There are also a few minor typos which may affect the understanding, just really minor ones like a becomes b while b becomes a, or some general english typos.

Perfect for those who want to get a taste immediately what data science looks like, like myself.

This one is definitely one of the better courses by IBM. Joseph Santarcangelo and the staff needs to be commended for putting in so much high quality content. The videos are sharp and bite-sized with questions within the videos to keep focused. There is sufficient coding in the lab exercises and the videos are not too theoretical like the low quality courses such as IBM's React and Node.js courses. There are still room for improvement as some of the labs contain errors, the practice quizzes are copy-pasted from the super-easy video quizzes and there is no project to tie everything together. I think a data science project involving APIs would have been an excellent way to end the course instead of the easy multiple choice final exam. Finally, I really think "AI" should be removed from the title as it is a bit misleading. There is no machine learning, deep learning or anything AI related in the course.

Python's ease of use is one of its greatest strengths. Its simple and intuitive syntax allows beginners to quickly grasp the basics while offering advanced features that cater to experienced programmers. With Python, you can dive straight into coding without being hindered by complex syntax, enabling swift prototyping and experimentation.

When it comes to Data Science, Python shines brightly. The abundance of libraries, such as NumPy, Pandas, and Matplotlib, provides an extensive toolkit for data manipulation, analysis, and visualization. These libraries, coupled with the powerful machine learning frameworks like Scikit-learn and TensorFlow, enable efficient data modeling, predictive analysis, and deep learning implementations. Python's seamless integration with statistical packages, such as SciPy and Statsmodels, further enhances its capabilities in statistical analysis and hypothesis testing.

Curso adequado tanto para completos iniciantes (primeira parte do curso) quanto para quem já conhece o básico e gostaria de conhecer e expandir seus conhecimentos nos módulos Pandas, Numpy e Matplotlib.

A parte teórica é apresentada com excelência em videos curtos de maneira bem direta e sintetizada, ideal para desenvolver um bom ritmo de aprendizado e de conclusão do curso.

A parte prática é montada de forma a possibilitar qur você aplique na hora exatamente o que acabou de aprender, acessando a plataforma após cada video por meio de links. Lá você vai encontrar exercicios simples e com explicações e passo a passo

O projeto final é simples, porém muito enriquecedor. Aplicando diretamente conhecimentos sobre os módulos aprendidos e códigos python na análise de um dataset disponibilizado e na geração de um dashboard de visualiação de dados.

The modules teaches the basics of Python and begins by exploring some of the different data types such as integers, real numbers, and strings. Learn how to use expressions in mathematical operations, store values in variables, and the many different ways to manipulate strings. Python data structures by explaining the use of lists and tuples and how they are able to store collections of data in a single variable. About dictionaries and how they function by storing data in pairs of keys and values.

 The best part of this course is the unique ways of collecting data by the use of APIs and webscraping. It further explores data collection by explaining how to read and collect data when dealing with different file formats.

The "Python for Data Science, AI & Development" course that IBM is providing on Coursera is a fantastic and in-depth study. It covers many different subjects, such as the basics of Python, data analysis, machine learning, and artificial intelligence. The training is well-organized and offers practical experience working on actual projects. The competent and interesting educators make difficult ideas simple to comprehend. Quizzes and practical tasks help reinforce learning. Overall, it is an excellent course that gives useful skills for aspiring professionals in the industry and is highly recommended for anybody wishing to grasp Python for data science, AI, and development.

The courses on the Applied Data Science specialization will spoil Coursera for you. I tried the Applied Data Science specialization out of curiosity; and quite frankly, I was happily surprised by quality. I really enjoyed the narrator and the amount of work put into the slides. They are effective at getting the point across and the course content gives you exactly what you need to succeed on the tasks at the end; granted, you have to put in some work, but overall is quite manageable to an "advanced beginner" like me. I felt challenged but not overpowered by the content. I really can't say enough good things about the this specialization and this course.

Having completed 100% of the "Python for Data Science, AI & Development" course, I can confidently say that it's a game-changer. This course provided me with an in-depth understanding of Python, AI, and Data Science, allowing me to tackle complex projects with confidence. The instructors' expertise and hands-on projects were instrumental in my growth. The supportive learning community made the journey even more enriching. With flexible scheduling, it accommodated my pace. I now feel well-prepared to embark on a rewarding career in these fields, and I wholeheartedly recommend this course to anyone seeking a transformative learning experience.

I recently completed the "Python for Data Science, AI & Development" course, and I must say it was an incredibly enriching experience. The course provided a solid foundation in Python, with a particular focus on its applications in data science and AI. I learned about key Python libraries like Pandas, NumPy, and Matplotlib, which are essential for data analysis and visualization. Additionally, the hands-on labs and projects helped reinforce the theoretical concepts, making it easier to apply Python skills in real-world scenarios. I highly recommend this course to anyone looking to get started with Python in the field of data science and AI.

I recently completed the Python for Data Science, AI, and Data Science course on Coursera, and I must say it exceeded my expectations. As someone new to the field, I found this course to be an excellent starting point for understanding the fundamentals of Python and its applications in data science.

The course structure was well-designed, with each module building upon the previous one. The instructors did a fantastic job of explaining complex concepts in a clear and concise manner. The lectures were engaging and easy to follow, and the accompanying hands-on exercises allowed me to apply the concepts I learned, reinforcing my understanding.

I completed the Python introductory course, and I must say it was an exceptional learning experience. This course is designed for beginners, and it really lives up to that promise. Here's what I learned and enjoyed. I gained a solid understanding of data structures like lists and tuples. These are fundamental in Python, and the course explained their usage and manipulation effectively. The course takes you from a complete beginner to someone who can apply Python in various scenarios. The vedios are engaging, and the course materials are well-structured. I highly recommend this course to anyone looking to start their Python journey.

This is a very concise and straightforward programming course with a neat hands-on approach. It doesn't waste time talking endlessly about all functions or every possible language feature, which is usually a source of demotivation for students and professionals that I've seen in other courses focused on quantity over quality. This course quickly presents you with the essential concepts so you can start practicing right away and apply it on your work, making it very clear that realistic software development means having the essential skills and always going for the documentation when you need something more specific.

A highly recommended course for students/professionals who want to learn about Python Programming and its fundamentals. Your journey in this course starts by familiarizing yourself with Python Basics (Data Types, Expressions, Variables & Operations) in Module 1 and learning about Python Data Structures (Lists, Tuples, Sets & Dictionaries) in Module 2. Module 3 focuses on Programming Fundamentals (Conditions, Branching, Functions, Objects & Classes), whereas Module 4 gives you a hands-on experience of working with Data in Python. As you reach the end, Module 5 tests your knowledge and skills through a Final Project.

An excellent course, provided by IBM, which does precisely what it says on the tin; providing a pretty solid overview and foundation for the applications of data science, AI and development tools using python. There is a lot veered, and you really do get out what you put in (so it is worth reviewing and revising the logic behind certain applications covered, in order to ensure you really have a decent grasp of how they work). This covered most of what I had hoped it would, and ties directly into the next course I had chosen in advance of deciding to complete this one.

I recently completed the Python for Data Science, AI, and Development course and found it to be incredibly informative and practical. The course covers the fundamentals of Python programming and delves into advanced topics such as data manipulation, machine learning, and deep learning. The instructors' clear explanations and hands-on exercises made complex concepts easy to grasp, and the inclusion of real-world case studies provided valuable context. Overall, I highly recommend this course to anyone interested in mastering Python for data science and AI applications.

Great overview of Python. It's quite a useful quick brief explanation of how touples, lists, sets, dictionaries, classes, defining a function, file reading/writing, logic statements, data frames, single and two dimensional arrays and a few packages work. Honestly, most of it would probably be gibberish/hard to follow if one didn't already have a computer programming I and possibly computer programming II course from a college or university under their belt. I feel like these could have been way more graded assignments/quizzes applicable and easily-made.

This is a good module for beginners in Python like myself. Great amount of examples and functions in the module which if practiced a few times will help understand them completely. I felt there would be no use in just rushing through the entire module and took time as scheduled and practiced the functions every now and then to get a better grasp.

May be the last bit on APIs was a difficult and the "nba_api" by Swar Patel was a bit sudden and I got lost.

But the final assignment was just perfect to understand my knowledge on the module.