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Learner Reviews & Feedback for Tools for Data Science by IBM

4.5
stars
21,627 ratings
3,345 reviews

About the Course

What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. 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. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers....

Top reviews

AJ
Sep 15, 2020

Absolutely Loved this course!! Challenging at times to keep up with all the terms and processes. The course provided great insight into Data Science. Would highly recommend it as your first course.

RR
Apr 24, 2019

To the contrast of other reviews, I find the content very well bifurcated and fed to the learners. The course very easily digestable and I have had a great amount of fun learning it.. Go for it!!!!

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2901 - 2925 of 3,321 Reviews for Tools for Data Science

By Igor L

Oct 2, 2019

Too easy

By Farzan B

Oct 20, 2018

Too easy

By 손승건

Jan 16, 2020

not bad

By Sanket B

Jun 10, 2019

its ok

By Osama H

Jul 4, 2020

nice

By Chakradhar K

Apr 7, 2020

cool

By Humza A

Mar 1, 2019

A

By Léonore F L

Jan 10, 2021

This course was presenting students with some interesting and rich information about the tools they could use, but it should not be the second course of the certificate already.

It is dealing with concepts that are far too complex yet for students who just started to learn about Data Science. These concepts are not properly described and students have to go through the course with only a partial understanding of some core concepts they would need to understand what is further explained in the course...

So many things are still really unclear to me now that I have finished this course. It took me quite some time to complete it because I felt demotivated. Now that I have started the next course on Methodology, I feel much better and I see what it is like to have things explained in a pedagogical way! Analogies, repetition, examples... All this is very important to help students navigate a topic as new and sometimes as foreign as Data Science. I was not convinced at all by this course "Tools for Data Science" and I do not think that the little knowledge I gathered will stick, as it is not built on solid foundations. I cannot be able to remember what tool will be useful for doing what if I do not know what I can / would do with data science.

The labs were good! A nice way to get proper training!

NB: I know this class is designed by IBM but when it comes to tools, it feels like the company is really pushing their tools to the center of the stage. They of course mention alternative options, but they are not dwelled on at all, and whenever they can give limelight to their products, they did it. It can leave students wondering on the impartiality of the course.

By Zachary G

Jan 17, 2019

I have stopped going though the IBM specialization after this course - this review is for beginners (like me), who have no coding/programming background. Coursera disappointed me because instructors are not there to help - you post questions in the forum hoping that there is a more knowledgable individual who will help you with your question. And if there is no such person, then your questions will not get answered by anyone.

Secondly, it mentions that course is for beginners with no programming experience, but then some codes, syntax and computer science terms get thrown at you without explaining basics and then videos are rushed through, leaving student only confused and frustrated.

Thirdly, courses lack consistency, clarity and are overall are very sloppy - information gets thrown at you from all places with no specific structure (if you had taken courses on CodeAcademy, you will understand what I mean).

Lastly, I was disappointed by some videos from Zeppelin Tutorials where all that instructor did was just reading text from main zeppelin page! I could do that by myself.

I am reverting to learning with CodeAcademy which was my original choice, but I thought that maybe IBM will be a good name to showcase on social profiles. IBM here does not mean anything.

By David

Oct 8, 2020

A lot of information given about the different softwares (open source or commercial tools) and the different processing steps. The Jupyter Notebook section is fine whether used on the IBM platform, from Anaconda or from a bash terminal. I spent more time than necessary to get familiar with the tools as I found some explanations really bad. Thankfully I used a lot of command lines at work to navigate through our system so I was able to survive through some of the poorest tutorials.

The RStudio section is horrible and mainly useless with no explanation whatsoever on what is done (you just have to type what you have been asked with no questioning as anyway there is no answering). That was bad but wait to see the Data Refinery section. I wonder how a video like that could be published by IBM.

At the end, I will extract and use the information relevant to what I want to do and forget about all the rest. This course is about teaching people about Data Science not about mainly promoting IBM Cloud Pak and its suites of softwares. IBM should clean up this course by removing the poor quality tutorials and update the videos as their platform and tools have changed quite a lot. I am now hoping that the Course 3 gets better...

By Aakash K

Jun 11, 2020

This is supposed to be a beginner level course. In the introduction, it was clearly mentioned by you that no-prerequisite knowledge is necessary. This course was taught to us as if we are already some professionals in this field. A majority of the explanations went above my head. Also while demonstrating the registration to the tools, please ensure you update your course. The current version we are using and the version of the tool at the time of recording are quite different. You need to literally scratch your head in trying to understand.

For example, while using the Python environment in Jupyter notebook in Watson studio, your tutorial clearly shows us to select the Free version of the tool. But when I tried, there was no free version of the tool at all. I was given 50 units of asset after which I would be charged. Please upgrade your tutorial.

By looking at this course, I am not sure if you would focus on one tool at a time. Assimilating this much information for a beginner level learner is something un-comprehendable.

By Sobhan A

May 6, 2020

I can say overall it's a good program. However by reviewing the first few courses, you will get a headache, but the rest courses are very useful. Some parts are really useless for a data scientist and are more useful for programmers, which I recommend you do not put a lot of time on them and skip them as much as you can.

Apart from the material, I think this certificate is really useful for you to get a job. But, if you just want to learn new concepts in data science and do not need a certificate, I recommend you to take other courses other than IBM. There are very good courses on @linkedin Learning.

In this program, IBM pushes you to work only on its platforms which is really annoying and I think this a considerable drawback of this program.

If you spend at least 4-5 hours a day, you'll be able to complete it in 2 months. The subscription is monthly and it's around $50 CAD and you can unsubscribe whenever you want.

By Stephanie C

Dec 31, 2020

There are a lot of inconsistencies here. The quality of the pedagogy is generally abysmal to merely lackluster, production values on some of the videos are very low (I'm looking at you, series of poorly organized screencasts with mumbled and heavily accented, rambling narration). The final certificate says the course is three weeks of work when it's actually four. In the end, I passed with full credit but other than the guides to connect Jupyter to GitHub and then Watson Studio to Github it was all by dint of what I knew before I entered the course and not because of anything I learned in it. But it is useful to have those tools connected so it's worth doing that. I find it hard to believe this is 1 of 4 courses in one of the most highly-reviewed specializations for Data Science, though. Hard to believe and disappointing.

By Karen P

May 23, 2019

There are lots of technical glitches in this one. Quizzes in a video before you've gotten to that information, missing links, places that ask your thoughts on something that really doesn't need thoughts, etc. I think it's also misplaced in the series. It assumes that you know a lot more about data science than you do if you've just watched "What is Data Science?" They talk about some great resources for writing in Python, R and Scala, but you haven't yet learned them if you take this as the second class in the series. I think there's probably a lot I didn't pick up on, simply because I didn't have the base that knowledge needed to build on. It might be a great class (if they fixed the glitches), if placed elsewhere in the series.

By Mark V

May 26, 2021

I did not enjoy my time taking this course. There is simply TOO MUCH information, and it rarely gets expounded upon. I often felt like I was just getting information dumped on me, such as listing the various Python or R packages we MIGHT use one day. Additionally, videos would often use data sets as examples but would fail to explain their context and don't seem to provide the data sets to work with on our own. This wasn't the case for the entire course. I enjoyed Week 3 where we spent a significant amount of time on one thing, such as jupyter notebooks or github. Instead of being told how something worked, we were able to play with these tools ourselves, and as a result I feel that I learned SO much more.

By Yousef K

Dec 25, 2020

This course leaves you doubting whether you can become a data scientist rather than providing you with brief knowledge on data science tools. I think it needs quite a bit of restructuring because concepts are explained in videos as if they are being explained to computer scientists and engineers rather than newcomers to the world of data science. Nevertheless, I'm glad I was able to get through it and move on to other courses in the certification that actually matter. My advice for you: don't worry about the complexity of the material and don't waste your time trying to understand the details because it's simply not worth it. Just get a grasp of the essentials and move on.

By Aaron A

Sep 2, 2020

A good overview of tools that are common to the field. The videos are just plain horrible. The narrator does not explain anything that he is typing on the screen, he makes frequent mistakes, and parts of the screen are not even visible. One video shot showed the narrator recording the video from the driver's seat of his car! Quality needs more focus. Also, the explanation of some of the labs are confusing and many many many students encountering similar errors...but when looking at that discussion forum, staff just cut-and-paste same unhelpful message to everyone. I guess you either figured it all out and make it through the course, or give up in frustration.

By Polychronis ( P

Oct 20, 2018

Outdated lectures and low quality videos. Owful monotonous narration (puts you to sleep). It is as if IBM & Coursera are trying on purpose to put people off Data Science. :-)

The 1st course of this specialisation (What is Data Science) was truly educational, fun and easy to watch thus to understand and remember what you have learned. This course is the opposite.

IBM & Coursera people, the fact that someone is a super duper scientist doesn't automatically qualify to be a teacher nor a narrator, and please IBM, do improve the quality of the videos with better graphics and enriched material. We know that you can afford to do so.

By Daniel A

Jul 29, 2020

The first course in the IBM Data Science Professional track was excellent. This one is not.

This particular course is dry compared to the previous one. It’s difficult to pay attention when many of the videos are a boring presentation of dozens of data science tools. Week two is less dry but is poorly organized and needs more explanation and refinement (poor audio quality, lacking descriptions). There are also videos which are essentially repeats (cover the same information) of the previous course but of worse quality. Overall, this course lacks refinement and organization, and is not worth paying for.

By Jose A C

Apr 29, 2020

The content is a good intro into the tools that you are gonna use in a data career. However, I did have two major issues here where:

1) As IBM moved to Watson Studio from Data Science Experience, this did create some of the visuals and knowledge a bit outdated.

2) There was a massive disconnect when you have issues working Watson; especially with signing up and logging in. I see that this was a huge issue since then (judging by reading the discussion sections of the course) and this (along with my first point0 should definitely be discussed if you want this content to still provided.

By Monserrat R N

Jul 23, 2020

It is a very quickly course, the week of the open source tools it's seen really quick, you can barely understand anything and they put three different tools in one week, if you really want to learn them search another course; it is also a big propaganda for de IBM tools, and they put so much more effort on selling you the idea that their products are really necessary that in actually introduce you to the tools that a data scientist use, and its so hard to understand if you don't speak english as your mother language, hope that with time they can fix it.

By Jay J

Sep 5, 2019

This course is not good. It has taken me several times the amount of time specified due to the outdated videos that no longer represent the tool as provided. Struggling to get simple markdown commands to work in the notebook, but I successfully executed them in the previous toolset. Several back and forth's between the actual work and the videos is very aggravating. It would be nice if you at least provided some printable instructions. I feel that I have gotten off track and may not be able to find my way back to a normalized starting point.

By Rachel M

Mar 2, 2021

Very patchy. Some of the videos were quite informative but the practical exercises were a real problem. Often the instructions were impossible to follow as the software must be a newer version than that shown in the instructions. I had to Google for the latest instructions. Also the practical exercises rarely told you *why* you were learning anything; it said "type in this code" but you didn't understand the code. I was able to finish the course but only through sheer perseverance (and Googling for instructions) - it was not enjoyable.

By Shawn G

Apr 7, 2020

This course at one time would have been great. However, for myself and many others (based on numerous discussion posts during my time taking the course), there was a multitude of technical errors and outdated training. Zeppelin Notebooks was not a working functionality in the IBM Skills Network, and most of the walkthrough guides were unhelpful due to updates in the tools being showcased. The course is in need of some serious updating to catch up on many changes that have impacted the tools covered in this course.

By Tania R

May 13, 2020

Material in this course looked like part of something else. It's a beginner's course but suddently I'm faced with a lot of technical jargon with no explanation. I supposed to experiment with software and code I've never seen or know how to interpret and I'm just suppose to run it to see what happens. I have no idea what I was supposed to learn from it. Week 2 was demotivating and added nothing to my knowledge. This week was an absolute loss of time. I still learned something from week 1 and 3