Chevron Left
Data Science Methodology(으)로 돌아가기

IBM의 Data Science Methodology 학습자 리뷰 및 피드백

4.6
7,142개의 평가
694개의 리뷰

강좌 소개

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....

최상위 리뷰

AG

May 14, 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 :)

TX

Apr 01, 2019

It just totally rebuilds my mind in thinking about how I should approach solving problems. I feel that I'm learning strong framework for an evidence-based logical approach. Just like a consultant.

필터링 기준:

Data Science Methodology의 684개 리뷰 중 601~625

교육 기관: Roshan P

Apr 17, 2019

This could be little bit more in detail. The content and the methodology was introduced but could be more in detail about all the analytical approaches available and why we chose decision trees for the CHF.

교육 기관: Siddhartha P

Apr 21, 2019

Very short and filled with too much jargons. A much simpler case study would have been great instead of deep diving into the world of Life Science & Healthcare

교육 기관: Haim D

May 05, 2019

The course is good and interesting, but I feel that it lacks the hands-on part, and that it could be more engaging. I feel that this course should be after the students have a tool that they can manage the data with, and that they can start dirty their hands with data.

The course as a stand alone course doesn't contribute a lot - it's interesting only as part of the whole certification, and should be linked to other tools in order to bring more value.

교육 기관: Andrei P

Apr 13, 2019

The information was somewhat confusing at times and it was kinda hard to follow the lectures even though the information provided was quite basic nad not too complex. I guess the problem with this course is the way the information presented and the overall flow of the presentation.

Also the labs, they confused me even more because we get presented with some amount of code which was not covered before. You are supposed to be able to complete this course without any coding, but you get all this unnecessary code, which doesn't even matter in the end but adds to the confusion and makes the lab harder to follow. I think it would be better to get rid of the code, or to include these labs after the python course, so the students can easily follow what's actually going on in the labs.

As i figured from the discussion section there is a number of students that were a bit confused about what actually should be in the final assignment (myself included). I had to rewatch all of the videos and revisit all of the labs just to get vague understanding of what needs to be done.

I am still unsure if what i wrote in the final assignment was even 100% correct (even though i got the top score), simply because these assignments are being judged by peers, not mentors.

교육 기관: Raymond P

May 11, 2019

I think it's a little bit vain to introduce in such way for some people without much background in statistics and machine learning .

교육 기관: Reid N

May 13, 2019

A fairly odd way to teach the process of data science. I think this should be combined with the introduction to data science course and perhaps simplified/clarified. The amount of jargon between this course and the other courses is significantly greater, and while the course did a decent job, I still leave the course thinking, "hmm, what *exactly* did I learn from that class?"

교육 기관: Adam S

Apr 06, 2019

It was ok. Definitely one of the weakest of the cert. Instructions were vague for the final assignment.

교육 기관: Jennifer K

Apr 04, 2019

The topic is super important and interesting. The content of the course was a bit hard to follow. More real-life examples may have helped.

교육 기관: Karel H

Mar 24, 2019

The exam for week 2 was terrible. The questions were way too tricky it was not necessary. Also I only was reviewed by one peer for my final assessment. This was bad because I deserved 100% and they gave me a only "Good" mark on one section probably because they figured out I gave them a "Good" mark on a section which they only did good on. More peer reviews should have been done than just one. I deserved a higher grade.

교육 기관: Michael K

Apr 07, 2019

This was the first course in this series that seemed to provide some knowledge. That said, the cognitive class external tool is painfully slow to use. I'd recommend skipping the ungraded assignments, as the payoff isn't worth the time you'll waste waiting for the notebook to open. This must be an obsolete tool, which IBM stopped supporting at some point.

I'm hoping the next course will allow me to run python on my cpu, rather than using a broken cloud tool.

교육 기관: sairam p

Apr 11, 2019

concentrated largely on theory.

교육 기관: Declan H M G

May 13, 2019

I found the material here vague and difficult to follow at times. Which led to confusion particularly about what was expected with the peer graded assignment.

교육 기관: David N B

May 24, 2019

Very little assistance from Moderators

교육 기관: Daniel T F

May 16, 2019

Presents you a good overview according the main topics of data science methodology. The case study is a good example to illustrate to content. But with respect to my experience the labs are very limited concerning the learning effect.

교육 기관: Harpreet

May 28, 2019

please update video with new IBM cloud UI screen

교육 기관: Nicolas G

May 29, 2019

This course was more organized the others, however, it needs to provide its student with more definitions

교육 기관: Muhammad U T

May 31, 2019

It provides a satisfactory overview of the data Science methodology, but the slides and the videos does not suffice the needs to fully understand the concepts and the Labs. Supplementary readings for this course are MANDATORY to understand and fill the knowledge gaps for several topics named in the videos.

교육 기관: Abhijeet B S

Jun 03, 2019

CHF case study was the worst part

교육 기관: Jacqueline ( G

Jun 04, 2019

I think the example on IBM is good, but on the lecture slide is not very easy to understand.

교육 기관: Amir S

Jun 08, 2019

The assignment is not very clear. The example had better to be more iterative

교육 기관: Ramakrishna B

Jun 10, 2019

More explanation would be great.

교육 기관: George Z

Jun 17, 2019

Very boring

교육 기관: Philipp K

Jun 12, 2019

too much information on slides. Use more pictures for visualization.

교육 기관: Arup K N

Jun 23, 2019

This course is more theoretical than practical which kinda makes this course a little boring then the other courses.

교육 기관: Deepratna A

Jun 22, 2019

Could have made it more interesting