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Data Science Methodology, IBM

3,352 notes
317 avis

À propos de ce 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

par SJ

Aug 09, 2018

This is my favourite in the series, the 10 questions to be answered were mind opening. The repetition after every video makes easier for important points to stick to the brain. Very good indeed...

par 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.

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317 avis

par Ivan Morales Duarte

Apr 19, 2019

Excelente curso que explica detalladamente los pasos a seguir para abordar un problema y encontrar su solución.

par Rohit Tilakraj Gupta

Apr 18, 2019

Awesome concept to study.

par Sadiq Saghir Haidar Ghaleb

Apr 18, 2019

A very wonderful course filled with interesting information. I would like to thank IBM as well as the Coursera platform as well as the course Instructors.

par Moray Barclay

Apr 17, 2019

Very good overview of te emd-to-end process. Thought it would be a bit dry but very useful.

par Roshan Pandey

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.

par Hardik Patel

Apr 17, 2019

Please improve pedagogy

par Alexandru Stanuta

Apr 17, 2019

Interesting course, although some very "thick" notions. It looses 1 star because of the final assignment, which is very vague and open to all kind of interpretations.

par Stephane Bourdon

Apr 15, 2019

Nice course

par Andrei Piliugin

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.

par Somaya Mostafa Mohammed

Apr 12, 2019

good course