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Learner Reviews & Feedback for Data Science Methodology by IBM

4.6
stars
19,899 ratings

About the Course

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems. Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python....

Top reviews

AG

May 13, 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 :)

JM

Feb 26, 2020

Very informative step-by-step guide of how to create a data science project. Course presents concepts in an engaging way and the quizzes and assignments helped in understanding the overall material.

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2126 - 2150 of 2,503 Reviews for Data Science Methodology

By ShanQiu

•

May 11, 2019

Not bad~

By Amarzaya

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Mar 4, 2020

liked

By Emmanuel O

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Jan 19, 2020

Great

By GOURAB G

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Aug 27, 2023

good

By Mohammad A

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Nov 13, 2022

good

By Adriana C

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May 1, 2022

good

By Raja M N

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Jul 7, 2021

good

By Muhammad S A

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Oct 14, 2020

good

By Shone G

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Jul 22, 2020

good

By Pagadala G s

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May 2, 2020

Good

By G V

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Mar 10, 2020

good

By Satishkumar M

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Jan 4, 2020

G

o

o

d

By Akhil K

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May 14, 2019

Good

By S M

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Jun 11, 2019

4.5

By Shruti R

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Apr 28, 2020

NA

By Adil S

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Jan 27, 2019

AS

By Nestor R V M

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Nov 12, 2018

:)

By Daniel L A

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Jun 22, 2019

-

By Andrei P

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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.

By Francisco M

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Feb 8, 2021

The course "data science methodology" provides a reasonable good overview of the main stages of a data science project based on a methodology similar to CRISP-DM methodology. Explanations are supported by two main examples: one related to "reducing risk readmission of patients in a hospital" and the other related to a study of "food recipes". In addition, some Jupyter Notebooks have been developed to make the course more practical. In general, I believe three weeks is not sufficient to cover all the phases of a data science project with enough detail. I think the video recordings do not provide clear and sufficient explanations of the different phases of a data science project. I also believe the course should provide further details, examples and Jupyter Notebooks to better address relevant issues in each of the phases of a project. I would add more "optional content" for students interested of additional details and examples. The final assignment seems not to be sufficient to prove that a student has understood the material. I think this course might benefit if students are already familiar with programming languages such as Python and query languages such as SQL (in other words, please consider adding these prerequisites for the course).

By Lionel

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Jun 22, 2020

Found that I learned best by reading the video scripts rather than watching the videos. Perhaps due to the fact that the material pertained to a methodology, which tends to be a abstract. The examples were a good start to applying the methodology, but there were a few gaps for me. Just as one example, the Biz Understanding portion was unclear. I intuitively applied my 6 sigma background and BU seemed to be like a definition of the problem statement in terms of measurable metrics, but the script and case study did not adequately walk through. When I read some of the peer assignments, i felt that some may not have applied the concept in the way I understood it. Another example, in Data Prep an illustration of the takeaways / deliverables from doing descriptive stats, pairwise correlations & histograms might have helped me visualize how I would apply the insights gained from those 3 steps to the data.

A more positive example is the illustration of data manipulation for missing data, bad data, etc. Relatively clearer.

By Anna N

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Apr 17, 2020

This class was OK. Solid introduction to the start to finish process of describing and solving a data science problem. Not super engaging, but that's OK.

My biggest problem is that the step where you turn a business problem into a data science problem is glossed over. I think they called it "analytic approach". It's easily the most important part of the course, and it is given very little attention.

This comes into sharp focus when you try to do the final project, and realize that unless you've done this professionally before, you really don't understand how to ask a question in a way that sets up the data science methods.

As an overview of a method, it's not bad. It really does highlight the iterative nature. But the final project is maddeningly vague and nearly impossible to do due to the "skipped" step inbetween.

I know that they didn't want to teach statistics, or assume people already knew statistics. But then the finally project should have held our hands a little bit better for this one step.

By Rick G

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Jul 31, 2019

I wanted more out of this class and I think this entire certificate should use this methodology as the manner in which all the classes and projects are done. It was still good to take to get a good foothold of the methodology, but by structuring that same methodology towards this certificate would go a long way in enhancing the overall experience. The first class could go over the analytic approach. The next three or four over data requirements and gathering data. Another three over the exploration and then use the final two classes or so to go over modeling and tweaking. There's potential for such a concept. Make it so!

By Venkatesh S

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Sep 18, 2019

I felt like there was too much emphasis on a top-down approach. Many a time one doesn't have the good fortune of going through the entire data science methodology as mentioned here. The client has already collected the data and then comes and gives you a problem. In this case, you need to have a bottom-up approach - play with the data already collected and see which analytic approach is feasible. In addition, not enough was done to say that this 'story' is the ideal scenario! Rarely do you get the chance to do a data science project so neatly. But it is always useful to know how things would work in a perfect world.

By Marie D

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Feb 24, 2020

The actual methodology and the questions to keep in mind for each step are very good, and it's good to have this foundation for understanding data science. But the course was poorly designed and not engaging. Too much jargon was used for a beginner course without explaining what terms mean. There was a glossary in the intro but it was just a list of words with no definitions (were we supposed to look them up??). I'm a native English speaker who works in healthcare and even I felt that the medical case study was too dense to really understand as a case study. The recipe analysis in the labs was much better.