The course materials are extremely outdated and due to this reason I've spent way to much time trying to figure out how to set up my notebook. IBM Watson is just not user friendly and not easy to navigate at all and the basics were not covered anywhere. It just shows that you don't care at all.

This is very unprofessional and I am seriously considering dropping out of the remaining courses from this professional certificate and I am definitely not recommending any IBM courses to anybody. I would give it -5 starts if possible.

Learned very little to nothing. Bias was very clear to get students to just use IBM products. Information and teaching method was unorganized and jumbled, seemed like it was cut and pasted from previous versions, and left out all the context of the original. Stressed me out and quite frankly made me consider quitting all together. This course was not worth the money I paid and I think I'd like to request a refund.

Watson and Skill Net are massively complex tools that are hard to use or initiate. That one needs to study these tools for the sake of just using them is a caveat. These tools will go away / be superseded by something else, and this study time is lost. Hope to get some deeper understanding of Data Science soon..

A gigantic advertisement of IBM Watson Studio.

Not only that, but the studio itself does not function properly.

Some of the videos were made by an IBM professional in his car!!! Honestly, you can see him on his webcam...

I found not more than 20% of the course useful.

A waste of time and money!

Quick basic course, which doesn't deep into each topic, most of the courses introduce IBM tools. doesn't really useful.

the best course for the beginner who is going to start his data science journey. This course tells you all options like tools, libraries, programming languages, etc. Highly recommended for beginners.

It's been a pleasure for doing this course at IBM via Coursera. Excellent experience on this course. Projects are good to do and peer to peer submission is good. I like to go for other course on it.

Great course with practical approach to tools that come handy beside data science with python such as git and github, Some basic R coding and a great introduction to IBM Watson studio and cloud.

This course helped me finding open source tools. I knew about Jupyter Notebooks, but I also got to know more tools. Further, I got IBM subscription too, it would definitely help me in my work.

This course is very nice to understand Python, Zappelin and R Studio basics on code and concepts, in which you will get hands on along with creating a free IBM Cloud and Watson Studio account.

Full throttle introduction to various tools available alongwith guided hands on experience. The more hands on experience one gets, the better for overall understanding.

Brief overview of the open source tools such as Jupyter and RStudio.

. Pretty good for beginners

It's really useful and comprehensive course specially for beginners.

Very helpful course to develop skills in Data Science .

Excellent course

This course provided a concise overview of data science tools and sufficient exposure to scenarios that show how to use them. The Watson Studio needs to be updated to reflect the current sign up process, however. I took of one star for this reason.

Overall the course content is very informative. I've learnt a lot. But in some sessions it is a little bit difficult for learners without any coding experience to follow, especially the Github sessions in Week 2.

The tools mentioned in the video all changed the interface, hence it's hard for you to corelate the actual tools to the ones in the course.

Some times it can be a bit difficult to folllow all the stuff related with IBM watson studyo or the anline lab

Need More Experts view. Videos are like reading from ppts. Need more videos from experts and professors

Hi,

I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".

Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)

Course 1: approximately 9 hours to complete

Course 2: approximately 16 hours to complete

Course 3: approximately 9 hours to complete

Course 4: approximately 22 hours to complete

Course 5: approximately 14 hours to complete

Course 6: approximately 16 hours to complete

Course 7: approximately 16 hours to complete

Course 8: approximately 20 hours to complete

Course 9: approximately 47 hours to complete

This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.

(*): https://www.coursera.org/professional-certificates/ibm-data-science?utm_source=gg&utm_medium=sem&campaignid=1876641588&utm_content=10-IBM-Data-Science-US&adgroupid=70740725700&device=c&keyword=ibm%20data%20science%20professional%20certificate%20coursera&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=347453133242&hide_mobile_promo&gclid=Cj0KCQjw0Mb3BRCaARIsAPSNGpWPrZDik6-Ne30To7vg20jGReHOKi4AbvstRfSbFxqA-6ZMrPn1gDAaAiMGEALw_wcB

I recently completed a course on IBM Cloud Pak for Data and Watson Studio, and I would like to share my reflections. The course introduced me to the fundamentals of working with projects, notebooks, integrations, and access controls within the IBM environment. One of the key lessons was understanding how projects serve as the central workspace for organizing data, notebooks, and models. I learned that external tools such as Git repositories can be connected through the Services & integrations option, which is essential for version control and collaboration. Creating new projects begins from the Work with Data section, which provides a straightforward entry point for building and managing assets. The course also reinforced the importance of Jupyter notebooks. I practiced using Markdown cells to add documentation and explanations that do not execute code, which helps make notebooks more readable and professional. Another important area was runtime environments. I discovered that these are accessed through Templates, which display the available configurations for Python, R, and other engines. This clarified the difference between monitoring usage and actually selecting environments. Finally, I learned about personal access tokens and how to define their scope. To enable Git integration, the correct setting is Select scopes > repo, which ensures the token has the necessary permissions for repository access. This highlighted the importance of security and proper access control when working with external services. Overall, the course gave me a solid foundation in managing projects, integrating tools, documenting work, and handling environments responsibly. I feel more confident in navigating Watson Studio and Cloud Pak for Data, and I can apply these skills to both collaborative and independent data science projects.

I recently completed the "Tools for Data Science" course on Coursera and found it an excellent resource for anyone interested in this field. The system provided a comprehensive overview of the various tools used in data science, including popular programming languages like Python and R, libraries, and development environments.

One of the things I appreciated most about the course was how it balanced theoretical concepts with practical exercises. The lectures were clear and easy to follow, and the hands-on assignments allowed me to apply what I learned in a real-world setting.

The course also included a variety of supplementary resources, including quizzes, discussion forums, and additional readings. These resources helped reinforce the key concepts covered in the course and provided additional context and examples.

Overall, I highly recommend the "Tools for Data Science" course to anyone looking to build a strong foundation in this exciting and rapidly growing field. The course is well-structured, and engaging, and provides a great starting point for anyone looking to explore the world of data science.

Este curso ha sido sin duda uno de los mejores que he tomado en mucho tiempo. La calidad de la instrucción fue excepcional. El contenido del curso fue muy completo y actualizado, cubriendo todos los temas relevantes en el área de estudio. Los recursos proporcionados fueron de gran ayuda para entender los conceptos y profundizar en los temas. Además, las actividades y tareas asignadas fueron muy desafiantes pero a la vez muy útiles para afianzar el conocimiento adquirido.

La estructura del curso y la metodología utilizada fueron muy efectivas. Además, la plataforma virtual utilizada para el curso fue muy intuitiva y fácil de usar. Los materiales y recursos estaban disponibles en todo momento, lo que facilitó mucho el estudio y el aprendizaje.

Por todo ello, este curso es altamente recomendable para cualquier persona que busque mejorar sus conocimientos en esta área de estudio. La calidad de la enseñanza, el contenido actualizado y completo, y la metodología utilizada son algunos de los aspectos que hacen de este curso una experiencia de aprendizaje única y enriquecedora.

This is a comprehensive and invaluable resource for anyone looking to dive into the world of data science. The course not only covers essential tools like Jupyter Notebooks, GitHub, RStudio, and Watson Studio but also provides hands-on experience through practical assignments and projects. What I found most impressive was the structured approach to learning these tools. Each module is well-organized, with clear explanations and interactive exercises that reinforce understanding. The instructors are knowledgeable and provide excellent guidance throughout the course, making complex concepts accessible. By the end of the course, I felt confident in using these tools independently, which significantly boosted my skill set and marketability in the field of data science. Whether you're a beginner or looking to enhance your existing skills, I highly recommend this course for its content quality, practical relevance, and the opportunity it offers to gain proficiency in essential data science tools.