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Learner Reviews & Feedback for Tools for Data Science by IBM

4.5
stars
28,175 ratings

About the Course

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 to use 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 and Cloud-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 features and 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 test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers....

Top reviews

ED

Aug 14, 2022

I love the detailing of every aspect of this course. The Labs, the free subscriptions and free trials provided by IBM Skills Network, everything has been so amazing. Thank you Coursera, thank you IBM.

MO

Apr 17, 2023

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.

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2401 - 2425 of 4,596 Reviews for Tools for Data Science

By Trevor Z

Aug 4, 2022

The first week of the course was a fire hose of information regarding all the open source and commercial tools avaliable to data scientists. The second week was very helpful in helping me learn more about how to use Juypter notebooks, git, and git hub. However, I do have to say that the education on R/R Studio was rather skimpy. I learned more about R/R Studio from watching a 2 hour tutorial on YouTube than I did from this course. The week 3 material got me excited to learn more about how to use IBM Watson and other tools associated with it to perform analysis. The capstone project was perfect for this course. I think I learned the most from the hands on project. This is a good course but it could be made better in regards to its approach to R/R Studio and not overwhelming the student with all the tools avaliable in the first week of the course.

By Xin Y

Dec 25, 2019

I like the approach of an overview of all the tools. However, I noticed that because some of the online platforms such as Watson Studio updates regularly, the placement of different pages and buttons are different from what's in the video. So that requires some exploration, which I think is good as it's usually part of the job when a data scientist is at work. That can, however, create some more confusion and time pressure for some students I imagine. I also found the Watson Studio docs page where there are additional learning that can be done on one's own by just studying and following the sample notebooks. I think that should be emphasized so that students who are motivated can go learn themselves. Same with the Watson youtube videos.

By Bernardo A

Dec 27, 2020

In general it is a good course for beginners, especially for the presentation of the workflow in automatic learning, deep learning and AI. I also have the same opinions about the use of Python, r, rstudio, Jupyter Notebooks and GitHub. There are aspects that should be improved, for example the sequence between the contents should be more linear, there are sections that are announced but not developed, like the use of SSH keys. In this case it was necessary for me to visit other places to have better information.As for the exams, these should be better aligned with the course content. By improving these aspects the course would be more productive

Translated with www.DeepL.com/Translator (free version)

By Amy H

Jun 29, 2019

This was a good course overall. The videos were helpful in showing how to navigate different notebooks and had links to other sites with further info if we needed. My only negative is that they haven't properly updated the course to match IBM Watson Studio and still have the video showing the old version. I had to spend hours on my own searching through the site to figure out how to create a project in this new format. I appreciated the promo which allowed me to access more of Watson Studio, but maybe offer more explanation on how to create projects within different versions. Otherwise, a really good course on the basics.

By Shakirah A

Oct 18, 2020

I would say this is sufficient for beginners who are new to Data Science. It's definitely worthwhile to learn from experts and I love the fact that students are given the chance to redo assignments if they do not pass the required grade. The labs such as using the Jupyter Notebook, R Studio and uploading notebooks to GitHub are also really helpful. I would have given five stars if all the tutorial videos of using the tools are the same as the current version of all the toolkits but it's really a great way to kickstart on gaining insights on Data Science. Kudos to everyone who finished the course!

By L

May 16, 2020

The topic explanation is great, quizzes are good and the pace is perfect. The only issue is the IBM Watson Studio. Issues all over the place (timeout, cloud down for maintenance, issues with logging in, issues using the Watson service) and no response on forums to the more serious queries. I reported an issue, updated the query and no response for now 9 days and going. Had to pay for an extra month due to this and reset deadlines twice. Reported issue again today on the course page, to Coursera and to IBM. I could have done the audit version, got the same amount and saved money.

By Danny M W

Apr 27, 2021

Excellent coverage of all major and semi-major data science tools, including where they fit into the ecosystem of data science, and hands-on practice with some major ones. Modules 1 and 2 gave equal time to all the most common vendors and organizations. Module 3 was much more specific to IBM tools, which is understandable because it is an IBM course. The mandatory modules were extremely high-quality and professionally built. The optional modules seemed more like casual, unscripted discussions, with pronunciations I found difficult to follow. Overall, a worthwhile course.

By Rahul J

Feb 12, 2021

A good course that introduces many aspects of data science by discussing open source tools that are consistently used in the field of data science. I like the IBM Watson Studio system that allows you to remotely run projects using various hardware and software environments. A lot of the tools discussed are IBM proprietary tools but they do discuss the theory and reason for using such tools. This is valuable to understand data science at a beginners level. All in all a good introductory course that helps fill in gaps of knowledge.

By Timo H

Oct 19, 2022

Good Intel on the most common tools to get an understanding of the general idea on working within the data science field. One star away due the obvious reasons on pushing the IBM products on an IBM course while there would be easier to acces tools available online and even Coursera has their own Jupyter labs installement like in the Applied DS with Python cert. Unless recruited by IBM I don't see myself working much with the IBM cloud and Watson studio. All in all good course and was fast to push thru.

By Sridhar M

Jul 28, 2020

I would have given a full 5 star but for the for the fact that the quality of the audio for some of the modules is poor and the instructions for the labs are outdated (Watson Studio on IBM Cloud has changed and you need to update the course with new screenshots so that the learners can have a better user experience). The course content is just about right and it meets the objective of an Introductory course. Thank you for giving me this opportunity to provide this feedback.

By Deleted A

Jul 11, 2020

A one stop destination to gain know-how of the tools at your disposal if you aspire to be a Data Scientist. The course perfectly summarises all the tools used by Data Scientists today along with their pros and cons. It even goes into some detail for JupterLab, R Studio IDE and IBM Watson. A few videos can be made better though, sound quality makes it a bit tougher to understand what the speaker is saying. A good course overall with lots of videos and labs. Liked it!

By Jose A

Oct 14, 2022

The Course offers some pretty valuable information for begginners. However, it is a little overwhelming at the beginning because it throws a lot of technical program names at you since the beginning. Although all these programs are useful as toold for data science, the vas majority are not even discussed after that initial mention. would be very helpful if these are reduced/removed so it makes the actual programs one is focusing easier to rememeber.

By Matthew B

Jun 11, 2020

The open source part was great; wish it had more! It feels a little vendor lockin-ny but that was to be expected. The exercises are great. I think adding a few more screenshots, a bit of proofreading, and ensuring the OS being used by the instructor is mentioned in the Git section will make it a little less challenging for people not familiar with either*NIX platforms. That being said, it was a GREAT introduction to the tools used for data science.

By Austin L

Apr 30, 2019

The material was very good. However, the video showed DSX which is quite different in look-and-feel from Watson Studio. It took me one week of intermittent trials to navigate the issues with creating a project, but then only an hour to finish the course project. Suggestions: tell the user to delete resources if they have a lite account; search for Watson Studio and go to that, not to the main menu. Overall, an informative course. Thank you Polong.

By Monica P L

Oct 9, 2019

I could know many skills and features of two different Data Scientist platforms: Skills Network Labs (learning platform) and IBM Watson Studio (Enterprise platform known as Data Science Experience).

I could also create and play with Jupyter, Zepelling and R Notebooks. My only recommendation is keeping all videos and course material updated (current platforms versions) in another case it is quite difficult to follow properly. Thank you very much!

By John V

Oct 2, 2019

I'm very satisfied with the substantive material covered in the course. I'm not giving it five stars because the presentation in week 3, specifically the instructions for setting up the notebooks in IBM Cloud were confusing. I understand that you are transitioning the branding but it was confusing. Students were on their own to sort out how to make it work. Overall, though, I'm happy with the sequence and will continue on to the third course.

By Mathilda M B

May 20, 2019

This course provides a good introduction to tools that can be used for data science (e.g. Jupyter notebooks, IBM Watson Studio). Packed with information and good introductory lab exercises to help students familiarise with the tools. On the other hand, I hope the parts mentioning IBM Data Science Experience are updated to mention IBM Watson Studio, because it did get a bit confusing in certain parts of the course. Aside from that, great course!

By Justin P

Jul 29, 2020

3 and a half. Tedious, kind of confusing. 1 teacher more refined and professional and organized in the lesson plan than the other and you can tell which is which. A lot of information, not always a lot of explanation. A lot of things that can go wrong and unless you wanna wait days for responses, pretty good chance you're gonna have to figure it out yourself.

I learned a lot but it was harder than it needed to be.

By Dirk V

Nov 20, 2019

Appropriate and concise overview on the discussed tool environments given in this course. It may be useful not to enforce unnecessary external account subscriptions in order to allow for exercises or earning grade points, e.g. either Skills Network Labs or Watson Studio. Working with the latter was a bit of a challenge due to different appearance of the current platforms as compared with the course resources.

By Nuno N

Jul 18, 2022

A good overview of common tools used in data science, with an understandable focus on IBM Watson Studio and colleagues - it is after all an IBM course. Nevertheless, good pointers to open source tools too. In many ways a bit superficial, but I guess that's exactly the scope of the course - let the student know what tools are available. They can then decide what areas they want to look more deeply into.

By Fabio A R

Mar 18, 2022

The course was great, as well as the instructors. What wasn't good was the support in the forum with the Labs, I understand IBM Cloud is an independent platform from Coursera, but is IBM the one accrediting the course!, so I expect the support team in the course can help solve issues with the Labs besides the silly answers repeating the instructions of the Labs and can go further helping the students.

By Samuel M

Aug 27, 2021

In my honest opinion, the beginning was a bit overwhelming. Keeping up with all the named tools which are just being introduced to me was difficult, especially when they're mentioned with the assumption that one already knows what it is. However, the rest of the course was well structured. Diving into specific tools and breaking down their main purposes and ways to utilize them was easier to follow.

By Sophie T

Dec 31, 2018

The course is very informative on the subject of tools available for Data Science. However, the tools have upgraded their user interfaces since the course was made, and so there are a few visual discrepancies between what you see in the lectures and tutorials and the actual tools. This might cause some confusion, but none that will prevent you from actually getting around and completing the course.

By Wallace G

Oct 17, 2021

Not as well put together as the first section. This was entirely lecture and slide based -- the first section (Intro to Data Science) had video interviews, etc., that made it much more engaging. This had lectures where the voice recordings and editing were very low quality and often different VO's on the same lecture. Not as well packaged and presented as the original section in this course.

By Lean P

May 30, 2022

I think it would help learners if the videos show step by step process of each software because the knowledge base is different with each student. There is also a discrepency between the current updates and when the videos were made. A copy or a handbook (e-book) to be provided will be an asset. This is to help keep the knowledge in sequence and provide aid for later review.

thanks

Asif