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Data Science Methodology(으)로 돌아가기

IBM의 Data Science Methodology 학습자 리뷰 및 피드백

4.6
6,631개의 평가
638개의 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 626개 리뷰 중 526~550

교육 기관: Yifan H

Aug 22, 2019

love the food recipe case! i am not familiar with clinical case but the food recipe case helped me learn the theory.

교육 기관: Janhavi D

Aug 20, 2019

It is great introduction for someone who has no idea about data science. The methodology is clearly explained. However, the example discussed is a little difficult to follow.

교육 기관: Divya

Jan 31, 2019

Concepts could be even more clear and understandable because this seems to be important subject

교육 기관: Hareesh T

Jan 31, 2019

An introductory overview of what Data Science actually is meant for.

교육 기관: Mark H

Jan 31, 2019

Course was ok. It's difficult to formalize data science into a generic methodology where subject matter expertise is separated from the process.

교육 기관: Ariel E

Jan 31, 2019

I'd like to see exercises where we can practice the methodology phases

교육 기관: Alireza F

Jan 03, 2019

Overall l it is a very good course. but on the lab section, the instructor's english is not very good. He can not deliver his thinking very well. You have to translate it to your self everything you read on the lab. In the business understanding section, he can not deliver the problem. Readers can not understand what he wants and what the goal is. IBM should rewrite this section so make it easy for readers to understand it better.

교육 기관: José M P A

Jan 03, 2019

A little boring...

교육 기관: Dita A

Mar 04, 2019

The course is good but the way the example is explained is a bit confusing, especially the when jumping from study content/material to the example.

The peer to peer review for the final assignment is veeeerrryyy subjective. I had to submit 3 times (with little to no change on my answer) in order to pass. Good luck on getting a nice reviewer! :)

교육 기관: adwayt n

Mar 05, 2019

It was very theoretical and, at times, a little boring as well. I was hoping for more of a hands on experience. But it was definitely very instructive and educational.

교육 기관: Sylwia K

Mar 09, 2019

For me this part was a little too much descriptive and given example (hospital patients) is not very easy to understand

교육 기관: Paren A

Mar 09, 2019

Nice overview, but brushed over far too many topics very briefly.

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: Roy R

Apr 04, 2019

Difficult being able to apply the final test to the complete module objectives. Though it was good foundation, felt it should be split into several workable modules/stages instead of all 10 methodology steps at once.

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: 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: 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: Vincent Z

Jan 14, 2019

Very general and abstract presentation of what the Data Science recipe is. Still nothing practical three courses into the data science specialization... Had I followed the schedule, I would be 9 weeks in with nothing to show off. At least, this course gives a nice overview of what a data scientist will be doing, but I think this should have been presented in the first week of the first course, without necessarily testing it.

교육 기관: Ivo M

Dec 13, 2018

The narrator was quite fast and I could not engage with the video lectures so well on this course. Consider review the Hospital case study too, which is quite complex when trying to understand the new concepts on the methodology.

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: sairam p

Apr 11, 2019

concentrated largely on theory.

교육 기관: Rohit G

Apr 30, 2018

Nice course

교육 기관: Rakshit K

Sep 11, 2018

If you could have explained the terms related to machine learning more and if you could have spend more time on understanding the Actual problem of the case study and then slowly built up the solution it would have been great course. I loved the organization of course but not the flow of the course. Thank You.

교육 기관: ushayelisetty

Sep 18, 2018

very nice course knowledgeble

교육 기관: Jayan T

Oct 22, 2018

Its an important topic for data scientists, but wish it was taught in a more interesting way with multiple examples of different types instead of one case study.