not great

Too easy

Too easy

Not Bad

not bad

its ok

good

nice

cool

A

Outdated material that has not been updated...

The first week is mainly memorization with no hands on/interactive items.

The second week gets better, but the instructions are a bit outdated for JupyterLab functions such as the 'insert' tab which is nowhere to be seen. Items of the markdown lab simply don't work, as when it wants you to copy the html code to Jupyterlab the code is actually compiled into a link (hard to explain but basically means you would have to separately look into creating HTML hyperlinks).

The third week is mainly about IBM's own products such as Watson Studio, but the course is obviously outdated in this aspect with items being called different names and payment verifification required to simply creat an IBM cloud account. Sadly for me, any payment method selected was declined and so the process was highly frustrating.

The fourth week peer reviewed project was something that I had originally thought to be a compilation of materials earlier learned that would have to be put together and peer reviewed. It turns out that the peer review project requires skills that have not been covered in any other lesson/lab. Specifically elements of the markdown language like creating bulleted point lists, links, and the like.

I feel like this course should be labeled as intermediate just because it's outdatedness in many aspects would require a person to have moderate experience with tools/troubleshooting in order to get from beginning to end of the course.

Given the cost of this course (40 dollars a month as of the moment), I would not reccomend anyone else take it if they are planning on learning and enjoying the learning process. Instead I would reccomend only those who know how to troubleshoot outdated materials and simply wants a certificate.

For anyone else, although not completely related, I would definitely reccomend you go through free courses with free certificates on Kaggle as it is much more hands on and intiutive, with a much more advanced grading system that makes it not just easy but FUN to learn.

link to kaggle courses with free certification: https://www.kaggle.com/learn

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.

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.

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...

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.

Realmente el contenido es genial porque es progresivo desde un nivel de principiante, pero es muy frustante cuando quieres avanzar en un curso, y necesitas programas externos que son de pago, pero que para el curso se supone son gratis. Pues ahi es donde llega el problema, que cuando estas ya metido de lleno en el curso, te haces la cuenta para hacer los ejercicios que te mandan y ahora el codigo ese gratis que te dan para esos progrmas no es valido, y te piden la tarjeta de credito. Estoy en el curso a 96% de completarlo, y no me es posible poder mandar mi enlace de ejercicios a los demas compañeros, porque no me deja entrar en la plataforma a no ser que pague la cuota anual. Mañana se me acaba la suscripcion gratuita de 7 días, y obviamente despues de esto, no voy a pagar la cuota, porque al final no voy a tirar el dinero, para no poder sacarme nada. Imagino que a muchas personas les ha ido bien, yo lo he intentado intentdo repetir todo, y no ha sido posible.

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.

The IBM cloud interface has been updated but the course material does not reflect that change.yet. It's a minor annoyance but this was my first introduction to IBM Cloud and trying to follow the steps was frustrating.

Also a lot of tools were quickly overviewed but there was hands on activities for only two of them. Some tools didnt load correctly for a while (couldnt edit Jupyter Notebooks for a couple of hours). The material felt siloed and unorganized and we were jumping from one topic to the other without spending time getting familiar with the applications.

It would have been a great idea to introduce a couple of data sets at the begining of the course, let the students get famliar with them, and run this data through the tools to see the impact of each one. The current approach made the tools seem disjointed and independent of one another

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.

Good introduction to programming languages and useful development environments. However, the IBM Watson section felt like a long commercial for IBM products. That section makes you jump through a number of hoops to register for IBM Cloud, a service that requires a credit card to even register if you don't follow the free trial instructions that are only given after the video lesson telling you to sign up. If you follow the video instructions you can lock yourself out of getting a free trial because your email can only be assigned to one account. IBM/Coursera's advice if this happens is "use another email address". Beyond that the instructions for completing exercises using that tool are out of date and not generally correct. This one section was enough to almost make quit this whole course, really disappointing.

I found the material difficult only because the videos were unorganized, often having multiple speakers in one video. Week 3 was a complete mess. I could not complete the lab exercises because IBM suspended my account (that had only just been created), but I'm not sure I could have followed along anyway because the videos did not match up to what the website looked like. Questions for the quizzes and final exam were extremely confusing, and I had to make multiple attempts to pass. The material just would not stick. I wish it had been presented more clearly. I am not sure of the relevance of the material, or how/where to use it. Weeks 1 and 2 seemed okay, but Week 3 as I said just went out the window. I was looking forward to learning more about the tools, but I still feel quite lost.

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.

They brand it "Introductory" but there is little introduction. Instead, it serves at best as a refresher of topics for someone who used to know these a while back. It is NOT for the beginner as the mother course would have you believe. Going into specifics: Module 1 must be the dullest most boring class I've ever taken. the AI speech enumerates 50+ open-source and commercial tools in data science and we are supposed to learn them by heart? What is the point? Module 4 rushes you into R and GitHub: it packs so much content and gives so few explanation; instead, it again enumerates concepts and attributes as if they're a grocery shopping list for a wedding of 300. The final exam has 2 broken questions that make them unsolvable.

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.