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.
BF
Great introductory course to show DS enthusiasts the multitude of available tools. Part of every solution is picking the right tool for the job, therefore this course is important to pay attention to!
MO
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.
AA
Good Course overall focus in basic tools. the course could be shorter as someone who know which language who want to use and familir with the tools already shouldn't learn all the course materials
GC
It serves perfecty its aim that is giving a first glance of the open course tools for data science. Of course each tool is briefly touched and it hands over the student the duty to deepen each tool.
MA
The course is overwhelming for a beginner with no experiecne of programming. The examples given in the class seem difficult and should have been of a lower difficulty level to keep the hopes high.
FD
Some of the lab assignments had instructions that didn't line up with how the programs actually worked. This was particularly the case for modular flow where auto-numerics seemed impossible to use.
AA
The skill share network has a problem that it does not work at all there must be a tutorial course for using it before the actual course startbut aside from that ... the course is perfect for me
YK
There was a problem with the connection to R lab, never fixed. Also, some tutorials are outdated. These are the negative parts and why I give four stars. Other than that I like the course so far.
RJ
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.
FC
It would be nice if you could update the material since some tools have changed either name or the way they look compared to the videos/images. Very good material though, I enjoyed the course much.
RR
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!!!!
TY
The course is interesting. It presents large spectrum of tools. It could be more helpful to provide general information on different tools and focus on few of them such as R, GitHub for example.
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I am amazed that IBM even put this course out on Coursera. It is the worst course I have come across. It is a humongous waste of time. It supposedly introduces the IBM Watson Studio but ALL the videos are outdated and do not reflect the platform currently on IBM's website. It is impossible to follow the instructions. You just have to sign up to the platform and hobble along trying to make sense of it. The quizzes were a utter waste of time. They don't even reflect the content in the videos. Many of the questions had topics that were not even covered. I was unable to do the final assignment because it's impossible to get to the Jupyter notebooks page when you sign in. It was an incredibly frustrating experience. I wasn't alone - the discussion forums are filled with hundreds of comments on the same issue - not being able to find the webpage where they need to create a notebook and do the assignment. I did not see any of the moderators even bothering to reply. Why have you put this course out here if you have no interest in providing quality content or help when your content is utterly outdated. I question IBM's reputation. This is a shambles of a course that I actually paid for (still paying since they changed it to a monthly debit!). I am in two minds whether to even continue with this certificate - seems
YOU LEARN ABSOLUTELY NOTHING! One star it's even too much. You learn how to make accounts on the IBM's proprietary platform so that you can pay one day (there is a monthly limit about the stuff you can do and if you don’t pay you are screwed). Giving away all your private data, of course. A platform that for free gives you the computing power of a 15 years old machine (with many other drawbacks). Jupyter Notebooks, R, Scala, it's all out there without Watson Studio, you could have installed Anaconda for example with all the beauty and speed of your personal machine (who doesn't have at least a dual core nowadays?). Without considering that would have been a lot more useful. So, you will learn only which open source tools you could use, but they send you on a proprietary platform to do that, without teaching you anything about those tools. Wait, they ask you to write 1 + 1 on python when you take the exam, you are all set with data science. This is a shame!
The experience of using IBM Watson really sucks! Tons of problems just kept poping out all the way from creating accounts to using notebook. And these problems have been existing for months, which is really shameful. Hope there's somebody can fix these problems right away.
Altough it may be understandable, this is just ads for IBM's products. So it's like if I paid to see ads.
instead of 5 minute snippets of a handful of data science tools, it would have been more practical to focus on one and spend some time on it...i'm really disappointing in this series
The entire section is without any introductions, any explanations. The services described in the video and actual (revised) versions don't line up. Instructor is just a useless, read-it-on guy that doesn't add anything to the learning experience. Overall, useless crap.
[Reviewing the entire specialization but points are applicable for each course]
I signed up for the IBM Data Science specialization and I was genuinely excited to start it for some 4-5 weeks (I had a GCP exam coming up). I eventually started the specialization beginning of August `20 and started making my way though it and I was amazed … amazed of how much a pile of bullshit this specialization is. I made it though the first 4 courses and at the end of the SQL for data science I couldn’t take it anymore. Here’s why:
1. First and foremost, the entire specialization (all 4 courses I have taken at least) were full of typos and broken URLs which a lot of other students confirm as well. This does not speak professionalism to me but whatever, lets move on.
2. The in-video quizzes and following tests are simply ridiculous … you are expected to have memorized content word by word rather than understand thing for your own and be able to explain them. Some of the question were so far away from tech courses it is not even funny.
3. The final assignments are a total joke. We are asked to review each other which IMHO is a terrible idea since we are all just starting up. Nothing stops you from giving top marks to a bad assignments and vice versa.
4. We eventually got to the more techy part and even got code snippets and jupyter notebooks to look through but they were still bad. There was no proper order in which information was presented i.e. you would read python and seaborn code in the SQL course’s tasks even though python and matplotlib/seaborn are discussed in the following courses.
5. And my final and biggest problem with this whole specialization is that it all feel like an extended advertisement of this piece-of-dodo tech inbred excuse-of-a-software called IBM cloud. There are constants up-sells here and there how almighty IBM is and how great their cloud and IBM Watson Studio are … they are not. I had to spend 2+ hours fixing problems with jupyter notebooks and their cloud just to complete my assignments which both took me 30ish minutes. They mention open source and even though there are open source equivalents to jupyter they insist using IBM cloud. I kept having the feeling they are more focused on promoting IBM products than actually bringing quality content.
6. Now after finishing the SQL course there was a 1min survey which I gladly filled in basically letting them know their specialization if terrible and is doing more harm than good in my opinion. I even sent them a quick challenge because I do not think IBM maintains this course at all or even reads the reviews. You can see my challenge to IBM here: https://bit.ly/3geOyfb
I was very saddened by the quality of the specialization and the content and was wondering whether I should even try and finish the remaining courses but after reading some reviews on the remaining courses I figured out it was just more of the same. If you are in the same boat I would recommend the kaggle micro-courses which I will focus on starting next week.
In conclusion, I got this whole specialization for free via financial aid and I have to say even though I did not pay a dime I feel I need to be compensated by IBM and refunded real money for torturing myself with their courses.
This course is useless. I spent several days watching videos and reading the materials only to get stuck in the end with no help in sight. Dozens of students are in the forums asking for help but even the instructor couldn't help us with the watson studio software.
This course is an absolute catastrophe and I look forward to moving on to the next one.
The first 2 weeks are a giant collection of endless software lists. Rather, I would have liked to see a disciplined presentation of the data science process itself (aka Course 3 of the certificate) and then only the softwares. The only useful piece is the chart presenting the steps of the data process. The rest is purely inadequate at this stage, I am sorry.
The course jumps back and forth from the very easy/trivial to the downright specific/complicated without any inbetween. Why mention Kubernetes, gateways, runtimes, PMML and other endless jargon when people don't have a clue where these fit in the bigger picture ? Instead of presenting 68 different softwares and acronyms, I believe it would be more valuable to present Jupyter Notebooks properly, from scratch and focusing on its foundational features. That would be 2-3 hours well invested.
One last note: the "Free Python 3.6" environment (using zero machine unit) is not available anymore. I had to create a separate environment manually, with 1 CPU and 4Gb RAM (using 0.5 u/hour) as a second best alternative.
Links are out of date
useless and non-up-dated
Please make this lesson easier to understand. The tutorials regarding how to use the open source tools are also out of date which makes the lesson very confusing. I had to do additional research on youtube, which I honestly found more valuable than this course.
the videos are outdated.
if you register watson studio with default settings (your country insted of USA) chances are it will not work
While the content was informative, the tutorials are not well matched to the work required and there is zero support available, other than the discussion Forum where many others have been waiting weeks for helpful responses. I have learned from this course to look outside Coursera for support when trying to accomplish the course objectives. This is not what I expected. You will see, if you search the internet, many other tutorials exist which actually provide feedback in real time..especially for working in R. I come to this course with no prior computer programming experience which is likely the reason I have struggled and been so lost. This was promoted as a beginner course but you will see, if you go through the lessons yourself, as if you were a new learner, that suppositions have made about the learner possessing prior knowledge to succeed in this course. If you read the discussion Forum you will see several people expressing surprise that IBM is associated with a course this poorly organized and supported. I was excited to learn about the many Open Source Tools available for Data Science, and did appreciate the exposure to Watson Studio. I plan to revisit IBM's videos on youtube to gain more experience. For the problematic disconnect between the instructions given and the assignments expected I would not recommend this course to anyone.
I enjoyed the first part of the specialization and was all set to pay to subscribe for the remaining 9 courses. Open Source tools for Data Science is an absolute mess though. From the first chapter, the resources are littered with spelling errors. Mini-quizzes pop up during videos asking questions on content which hasn't yet been covered. The labs are vague and make little sense, and then to top it off, Week 3 has a lab and video tutorial for a totally outdated version of Watson Studio which looks nothing like the current version and is impossible to follow along with.
I've now unsubscribed from this course. I'll come back to it if and when it's updated.
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!!!!
The course is interesting. It presents large spectrum of tools. It could be more helpful to provide general information on different tools and focus on few of them such as R, GitHub for example.
Videos are bit out of date so could be sometimes tricky to find the needed instructions!
I have coding experience for more than 6 years in my field and I do have a Ph.D.; I do not mean that I am smart, I just mean I am used to learning on myself as well as teaching programming (Java) to beginners! The course, tools for data science, seems there is no thinking in the teaching material! what a beginner level means and what are the teaching tools and requirement are! Directly to a terminal in Linux and start doing things that definitely need an intro and some pre actions! What a waste of time! If there was a zero star, I could give one!
The videos and explanations are not great If you are a beginner, do not take this course.