The videos about GitHub were lack of pedagogy. fast, not clear and the subtitles in most of the cases did not match.

Horrible course. It felt like a commercial for IBM products rather than teaching you how to actually use tools.

Is it adv for IBM? I didn't learn, and I feel like I paid for an ad.

Not useful for a beginner without CS and programming knowledge!!

It's some kind of advertisement for IBM products

loss of money

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.

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how touse them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data andCloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their featuresand limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to testeach tool and follow instructions to run simple code in Python, R, or Scala.

This second course for the specialization program for Data Science is really helpful, informative and very practical. All the instructions are clear and precise when it comes to use tools like Jupyter Notebooks, R Studio and GitHub. The videos are well made and the labs will let you gain important practical experience. The way you can do the labs on the cloud and are guided through them is wonderful. Step by step instructions. The discussion forums are helpful as well. I was a little afraid of using GitHub and Jupyter notebooks and Jupyter-like environments, but now I know it is not that difficult. I now know the basics. And the basics are the fundamental ideas for you to keep going and learn in the future. Also, the explanations about libraries, languages, open-source and commercial tools, and data science categories are clear and let you have a very informed overview of all that.

I'm giving this the 5 Star review that it deserves, even though I ran into multiple issues when setting up IBM Watson Studio. The application from the video differed greatly from whatever version of Watson Studio that I had logged into - as if the video content was outdated. This made the navigation of the setup very cumbersome and time-consuming and it also hindered and delayed my ability to complete the assignments. However, everything else was either on point or better than the training materials (specific to the DS tools) that I had previously used. It appears that other students had completed their final graded assignments w/o any issues, judging by their submission date after I was tasked to grade their projects...

This was a big boring or time consuming Courese. I invested like 3 months in this course as lost intererest in week 2 or end of week 1. There were so many names of Softwares and Tools which I though are very imporatant and to be memorised thus I was not aware do I really need to remember them or they are for information purpose only. As for rest of the course where Jyupter or Watson Studio Lab comes in then the course get intresting and there was definately fun things to learn about. I even made few notes and posted it on my website tapishdongre.com which will be definately useful in the future while working. In short Week 1 and Week 2 is Boring, Week 3 and Week 4 are Superb thus giving 5 stars for the overall experience.

I really enjoyed taking this course! The well thought-out and thorough course content kept me captivated through the whole course! I really appreciate the effort put into helping us students learn the most current technologies! Since I've recently finished a course on Machine Learning, I do have a suggestion for possible improvement of IBM SPSS Modeler's Analysis Node. That is to show calculated precision and recall, in addition to accuracy; especially when there is a low probability of occurrence when accuracy value can be misleading. Thank you, again, and may the love, joy, and blessings of our Lord Jesus Christ be with you and all who take this course!

This is the first course I have completed on Coursera. Overall it definitely meets its objectives. It is written for beginners and it indicates so in the course description. Yes some of the materials are a bit out of date but I do not find anything impeding. At times, for examples, during the setup phase for the tools, I felt stuck and the fact there was no one around to answer questions made it more challenging. However, part of the learning is to deal with uncertain situations and prepare us for the future. I did feel more satisfied when I finally overcame the challenge.

There is a great deal of overview information in this course regarding the various tools and platforms for data science. I was excited to begin working with Jupyter Notebooks and Watson Studio and was completely thrilled to learn about the other tools available, which I also intend to learn. I had not realized what data science was and what one can accomplish through it prior to taking this class, and I see endless opportunity for applying the framework. I cannot wait until I learn enough to actually begin applying these skills to real world problems!

This course is really good to get some basics of the tools but if you want to get better in these tools you either need to have basic knowledge of computer programing prior or you need to explore each tool yourself and give it huge time.

I would suggest the course organizer give some optional practice materials so that people can practice it and get familiar with the tools. Also, the course videos introducing IBM Watson studio are a bit outdated with the current website interface so some times it can be a problem to understand what to do.

The course was very helpful and concize. It focuses on getting the learner familiarized with the most common tools used in data science and the ways they can be accessed through open-source software. My only feedback is on the tests. It seems to me that many times they focus on concepts explained once during one of the videos or texts, instead of focusing on the parts where the learner is engaged practically. I would suggest to modify the questions or to add a few intermediate activities where we go over those "theory" concepts once more.

It is a good course. Lot to learn about Data Science. There is a small problem at times. Like the IBM Watson Studio instruction do not match with the actual website. BUT this minor small problem is easily over come. As I am discovering. The IBM's website is being upgraded for GOOD REASONS. SO even if there is some mismatch in their video instructions, no problem !!! One can find newer relevant answers on Google search or on FORUMS. Which is very good in learning to overcome minor difficulties. So Overall VERY GOOD as I am also learning.

I love you Coursera, I am really thankful for the Coursera community to provide us the course is free of cost. so many students like me suffering from financial challenges and Coursera provides us the very best course free of cost. thank you very much, Coursera.

I want to tell about something about this course, this course was really awesome and able to understand all the concept of opensource tools for data science like jupyter zeppelin and IBM Watson. this course is very helpful for me. Through this course, I learn a lot of things.

I am very passionate about learning Data Science. This course was great for a beginner, like me, with no programming skill. I am an older individual and have used DOS when it first was taught in my High School. Many eons ago :) Some of the links are in need of updates. I had to install a CRAN mirror before I was able to get R Studio. Other than that, I tried to suggest fixes when I saw them. The course is well presented and I love the introduction to the course with the eclectic individuals speaking about Data Science.

This course introduces the learner to the open source tools for Data Science in a very efficient manner. It describes the usefulness of different open source tools and at the same time provides hands on experience with them. There will be graded quiz sessions about each open source tool and it really helps to get the concepts clear. At the last, we have to write a Jupyter notebook and share it with our peers. We have been really guided very well to create our shareable notebook. Thanks for this course.

I personally learnt a lot from this course. The sheer amount of new things, like the various Data Science (DS) open-source, commercial and cloud-based tools, the various languages used in DS, the various powerful libraries, etc., blew my mind. Add to this, GitHub, Jupyter Notebooks, IBM Watson Studio (optional), this course is very heavy on both weight (importance) and content. I feel like I know a lot more than before as I've completed this course. Thank you for this wonderful course offering.

Thanks, Instructor, Many thanks Coursera. I really appreciate the support you've given me. I like this course. I learned first what “Data science” means that can affect my whole life not only my career. and this course will help me a lot to understand those concepts in an easy and right way. In this course, I’ll learn how to manage, extract, transform, analyze, and visualize data. Now, I might be able to survive data science without programming skills if I use the right set of tools Right now

Tools for Data Science course was a great introduction for someone entering the data science field like me. The videos and transcripts clearly explained the concepts. The practice tests and quizzes at the end of each module in the course make you prepare well for the peer review assignment and final exam at the end. After completing this course, I became familiar with commonly used data science tools such as Jupyter Notbooks, GitHub, R studio, libraries and languages.