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

17,910 évaluations

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!...

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 :)


18 juin 2021

Very interesting course. It shed a light on what the structured approach really is. It's worth to pause for a moment with every step of the methodology and think how to apply it in real life. Thanks!

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51 - 75 sur 2,222 Avis pour Data Science Methodology

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 Adan A

12 mai 2020

very boring and very difficult case study ... This is one of the worst course and also instructor seems like some kind of robot is talking to us

par Sandeep T

10 mai 2020

Too much is paced into short 4- mins video

Sounds like a chabot was reading a text

Better visual aids are requred than the PPT format chosen

par Kamila K

28 déc. 2019

This feels like a massive waste of time. If I didn't need to complete it for the certification it would not be completed.

par Amiya R B

17 févr. 2020

I think the trainer just read out some material prepared by him. concepts are uncleared to me. bad experience

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 Kshanti G

8 juin 2022

I​ enjoyed the content of this course and felt like it gave good examples and case studies. The assignments were helpful to get a feel for using Jupyter and python for data science, and the real world data set was useful. I did not understand their differentiators for descriptive vs predictive approaches, and earlier in the course they had a third- classification- which seemed to have been merged into one of the two others later on.

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!

par Hasan M A M E

15 févr. 2021

First, I really appreciate the helpful support you've given me, I am interested in Data Science Methodology and this course helped me a lot to understand those concepts in an easy and right way. In this course, I learned how to use the data within the decision-making process, how to apply the data correctly to the problem at hand, a methodology that can be used within data science.

Thank you once more for your help in this matter.

par Diego R M V

25 mai 2020

I really enjor it. Make me thing as different way in systematic but creative way. There are different ways to solve a problem based on the question we are getting. I know that we only cover some of them for trying not making the course that large. Would be wonderful to have optional resources beyond the course on how to attack different kind of questions as well as evaluating which kind approach use.


30 mai 2020

Good Day

I am personally thankful and grateful for this opportunity .

Thanks and Warm Regards.


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par Felipe T

26 sept. 2020

This course helped me get more familiarized with structuring an efficient methodology to use data science in different scenarios. Seeing data science as an iterative process that is always subject to improvements and recognizing how the different stages relate to others helps to understand that it is a process that never ends and provides an efficient solution to specific problems.

par Damilola O

21 avr. 2020

The Data Science Methodology really opened my mind to the meticulous process of 'solutioning' as a Data Scientist. Understanding Business Case and what the business owners want, dimensioning to know the approach analytics, knowing the data set to work with and how to analyse same, and ultimately modelling and also learning from the model via feedback so as to make things better.

par Eve B

9 juin 2020

A quite good course, I like methodology and theory behind data science. I am glad the peer review is 10%, and the quizzes total 90%. I think this helps students to have objective standards and not be dependent on anonymous peer reviews.

Just a little point at the end: The example of the US health care system does not seem to be completely useful for students of other regions.