Chevron Left
Retour à Data Science Methodology

Avis et commentaires pour d'étudiants pour Data Science Methodology par IBM

15,498 évaluations
1,838 avis

À propos du cours

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think! LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

Meilleurs avis

13 mai 2019

This is a proper course which will make you to understand each and every stage of Data science methodology. Lectures are well enough to make you think as a data scientist. Thank you fr this course :)

26 févr. 2020

Very informative step-by-step guide of how to create a data science project. Course presents concepts in an engaging way and the quizzes and assignments helped in understanding the overall material.

Filtrer par :

26 - 50 sur 1,822 Avis pour Data Science Methodology

par Huzaifah S

18 janv. 2019

The example should be easier than CHF in the videos like the example of cuisines was. The CHF example was good but it was not self explanatory and it might be hard for some people.

par David M

26 juin 2020

Great course, but it goes over some key concepts very quickly. It wasn't a problem for me because I'm familiar with statistics and I conduct social science research. But for someone who is completely new to these topics, I think this course would lack enough detail in order to be useful.

par Johannes

16 janv. 2019

this course should be a little later in the IBM sylalbis

par George O

21 août 2020

[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:

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.

par André K

20 mai 2020

Unfortunately, this particular course dissonates a lot from the previous ones made by IBM on Coursera. The material is very poor, the narration is very fast (we're not all native English speakers!) and most of the time it doesn't match what we see on the screen. It's completely confusing, it's impossible to aprehend any information on these videos. The study case is far from a good example to be understood, it only makes the classes even more confusing than they are.

The notebooks are of very poor interaction, and even the quizzes and exams are not pedagogic. I really felt very much frustrated with this particular course and I hope no other will be as bad as this one, as I felt I had just wasted time and money doing it. I really felt like I've learned nothing from it. Reading the "IBMOpenSource_FoundationalMethologyforDataScience" 3 times and then making an exam about it would be 10 times more effective learning than wasting hours on this terrible course.

I am really shocked with the lack of quality of this particular course, comparing to the other which are simply amazing. Please, substitute this course ASAP for a good one, because I am sure it is lowering the overall quality perception of anyone who is following the 9 courses to reach the certificate.

Sorry about the honesty, but it was very hard to go through this course. I am still shocked about the difference between this one and the others IBM has offered.


par Roman S

10 avr. 2020

Actually a really interesting topic but unfortunately made quite poorly. Visuals were complicated, had spelling mistakes and often went at 1 slide / 60 seconds which meant you had no idea which part of the wall of text the tutor was reading. The extremely monotone voice relapsed my attention in the first 10 seconds...

par Cedrick N

6 oct. 2019

You killed part of my enthusiasm and interest for this Data Science program because of your lame videos, it feels like you went back in the early 90's to make them and the voice is so hypnotic that I couldn't keep my focus, I don't think that I learned much here, I just wanted to go through it as fast as possible.

par Abdullah A

16 mars 2020

the main issue with this course is the ibm skills network lab, it have much much errors and lags and to much delay , some times if I closed the tab of the lab i can't reopen it which cause slowdown to my progress in this course and skip many things and labs . Otherwise everything is ok

par Amber Z Q

4 juil. 2019

It's like being taught by a robot. It's just not as effective when the "teacher" doesn't communicate to you like a human. It feels like it was just a voice reading a book to you without proper explanation. As a result this course was unnecessarily difficult to understand.

par Parth J

8 févr. 2020

Not conducted in the way it should be. Too complex to comprehend and difficult to correlate sometimes. Speaker's language was mechanically scripted, boring and non interactive. Important topic barely touched the the surface where deep explanation was required.

par Julian L G

16 déc. 2018

More or less a complete waste of time. Some of the Jupyter notebooks were interesting, but not enough to make this anything other than a way to stretch out your enrollment period in the course...

par Ioana R

12 mars 2020

The videos were hectic with information. I felt the need for more explanations or reading material. I am not sure how to apply what I learned in this course.

par Shamir P

24 avr. 2020

Prior to undertaking this course, my experience with data science methodology was non-existent. I was not aware of the robust framework developed by John Rollins at IBM, and how it could be used to solve a problem for a business - even if data science was not the end goal. A key lesson from the course as my takeaway would be that it taught me to ask more questions, but more importantly to keep asking questions from different angles.

The course provided me with an invaluable shift in the way that I think about classifying a problem, analysing a problem and then the numerous methods that are available to me when developing a solution.

I would strongly recommend this course even to non-data scientists who require problem-solving tools for their work.

Well laid out, although I do wish that the videos provided a bit more detail on other reading references or articles to gain deeper insights on some of the concepts. (Google definitely helped though).

par Austin F

7 nov. 2020

This is the third course in IBM Professional Data Scientist Certificate, and it so far is by far the best. Some of it was that there was actual material to learn. The first course was data science hype videos. Like a video version of the book "Competing on Analytics" or a long-form Businessweek article. The second course just seemed to be hyping / explaining IBM's watson ecosystem, but often with clunky instructions. This course had some substance to it. Also, whenever the IBM ecosystem needed to be used, it just took one click to open the notebook that was needed. No four-page instruction handout that doesn't work well if you already have an account. Just a single link. It was beautifully simple execution.

par Raíssa B T

14 avr. 2020

I appreciate the classes and this whole course. The content is groundbreaking to me! It's such a gift to have learning materials from IBM. Thank you very much.

I like the content but the way it has been ministered/taught could be more dynamic. I like the short and directly-to-the-point videos, but I guess to make comprehension more direct. I mean that if the written information appeared as soon as the speaker mentioned that, it could be more didatic for the student. Sometimes, I didn't know if I payed attention to the written content or to the spoken content. As I am not a native English speaker, for me was sometimes chalenging, thus made me read e watch many times the videos.

par J C V

12 sept. 2019

Gives the basic understanding of the methodologies involved in data science domain. Outlines the step-by-step stages of the methodologies. Allows you to think like a data scientist for the final project (although not extensively). Didn't cover all the possible models that a data scientist uses on a daily basis. This course tries to explain the things with the help of case studies which consists the basic models and analytical techniques. All the way, this course walks you through the basic fundamentals of the stages in data science methodology.

par Prabhakaran E

3 janv. 2020

The course paints an overall picture on the complete set of steps that are followed while working on a Data science project. The best part are the exercises, where we are required to solve a problem to identify the cuisine of any recipe by using a decision tree algorithm. One thing which I found tough was that the python coding part was not explained even a bit. A brief information on the various functions and methods that are used as a part of exercise would be even more helpful. Other than that, its a great course for beginners.

par Ashok K

6 janv. 2019

Good course. Thanks to the instructors, IBM and Coursera for making this course available online.

One small thing I would like to request for the answer-input area for the final Peer-Graded assignment. is to provide mechanism to add images and or link as well. That can be very useful for anyone who want to add images of Decision-Tree or Data Model etc. in addition to text explanation to make it more clear. The workaround to load images on Google-Drive and then copy-paste text-link in the answer-box was

okay as well, I guess !!

par Jeanne L M

28 avr. 2020

Best course for starters, really understood key concepts and how to apply them into labs and practice questions. Moreover, this platform really tested my core understanding through the intensity and volume of practice questions (which Cognitive Class only had 3 questions each per practice). Can't wait to get my Badge for this course! One thing is that we learners have to pay access for subscriptions in order to get our certifications and badges which the fees were not mentioned beforehand until before the check-out page.

par Jianxu S

24 août 2019

I would probably give 4.5 stars if there is such choice. Overall, it is good and fun to work through the material but there are places where the message was not crystal clear. For examples, the analogy between data scientist and cook is not always helpful. One of the quiz question described model 2 but was associated with the wrong cost ratio (4:1 instead of 9:1). If Receiver Operation Characteristic (ROC) curve is an important concept then perhaps a little bit more explanation is warranted.

par Abhishek G

2 oct. 2020

This course is very special as it gives the practical knowledge of how does a data scientist think while doing his job. It teaches us to create different visions to see a single problem with a different mindset. The practical example of "Congestive Heart Failure", teaches the realistic thinking of a data scientist.

This course is the third part of the multi-series course of IBM and whatever I learned in the previous two courses, all those were implemented in this course.

par Daniel F

24 janv. 2021

This course is incredibly important in my opinion as it really focuses on the methodology, and not technical aspects, forcing you to think like a data scientist and teaching you on how to approach business problems that can be solved by using data. This is a key course/lesson that is usually missing from other Data Science tutorials and courses, which tend to focus more on how to perform a certain step, and not show the great picture of what all the steps are.

par Oritseweyinmi H A

2 avr. 2020

I have previously dabbled in various parts of the full data science process. Including data collection, data understanding and data preparation. I have also separately worked on data modelling and data evaluation on Kaggle. However I am very grateful for this course, as it has enabled me to be able to appreciate the big picture view of data science and has provided me with a framework to use for future data science projects. Insightful and very comprehensive!

par Vairavan P

18 juin 2020

I loved this course. I am very new to data science and I was stuck on what is data science and how to start with data science. This course gave me a very good insight right from how to start analyzing the problem and what are the stepped to be followed in each and every stage. The main highlight of this course is they use a case study to explain what happens practically in each and every stage. It helps in properly understading the concepts

par K L K

26 oct. 2020

This course takes you through the mind of a data scientist. How a data scientist strategically thinks to solve a problem? The methodology of problem-solving will be embedded in your mind, more relevant to data science problems. How a data scientist should behave at various stages and how it can be effectively done, what are the alternatives, and what is mandatory? These questions are answered when you follow this course. Good one!