The course is outdated needs major improvement, the instructor is so messy, he has taken a theme with a hospital which is so hard to understand from people that don't have a medical background! Also, the instructor's voice is depressive, and the video provided had almost no relation to what he was saying.

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

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.

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.

Very little time is spent explaining in detail the various stages of the data science methodology. also the case study used to illustrate the methodology is unnecessarily complicated. This course needs a refresh.

Poorly structured and doesn't teach you as much as you might think. A lot of the labs don't work and often takes forever to load if you're lucky. Do not recommend.

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.

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

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

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

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

The "Data Science Methodology" course, part of the IBM Data Science Professional Certificate specialization on Coursera, is an outstanding educational experience. This course brilliantly demystifies the data science process, providing a clear and structured approach to solving data-driven problems. The instructors present the material in an engaging and easy-to-understand manner, making complex concepts accessible to learners at all levels. One of the standout features of this course is its practical, hands-on approach. Through real-world examples and interactive exercises, students can apply the methodologies discussed, reinforcing their understanding and building confidence in their skills. The course also does an excellent job of emphasizing the importance of each step in the data science lifecycle, from data collection and preparation to model building and evaluation. The inclusion of industry best practices and insights from experienced data scientists adds tremendous value, offering learners a glimpse into the professional world of data science. The course materials are well-organized and comprehensive, ensuring that students have all the resources they need to succeed. Overall, this course is a must-take for anyone looking to gain a solid foundation in data science. Whether you're a beginner or someone looking to refine your skills, this course will provide you with the knowledge and tools necessary to excel in the field. Highly recommended!

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).

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.

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.

The IBM Data Science Methodology course aims to equip individuals with the fundamental skills and knowledge necessary for data science. The course covers data collection, preparation, modelling, evaluation, and implementation of data-driven solutions. Participants typically learn how to utilize popular data science tools and techniques, such as programming languages like Python and R, data visualization methods, and machine learning algorithms. To gain more insights into the course content and quality, refer to online reviews and testimonials from previous participants. You can also access the course materials directly through IBM's official website or learning platforms.

I recently had the opportunity to participate in a course that I found to be immensely valuable and thoughtfully designed. The course content was delivered in a manner that ensured optimal understanding and retention of the information.

Throughout the course, the instructor consistently demonstrated a remarkable ability to provide information and then reinforce its importance by contextualizing it within the broader scope of the subject matter. This teaching approach greatly enhanced my comprehension and allowed me to grasp the significance of each concept presented.

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.

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.

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 !!

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.

I recently completed the Data Science Methodology course on Coursera, and I must say it's nothing short of brilliant! The content is exceptionally well-structured, making complex concepts easy to grasp. The practical approach to real-world problem-solving scenarios greatly enhanced my understanding. The engaging delivery by the instructors, coupled with hands-on exercises, kept me motivated throughout. Overall, a fantastic learning experience that I highly recommend for anyone diving into the world of data science.

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.

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.

I think methodology is the most important part. I've been being experienced since my undergraduate level that any problem can be solved when it's methodology is clarified. Many of our teachers asked us of the method first, then insisted to do. So, from this course arranged by 'Coursera', anyone having some story-telling capacity with some science-background including math, can learn a lot for solving today's business problems lie in marketing or other areas.