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Voltar para Data Science Methodology

Comentários e feedback de alunos de Data Science Methodology da instituição IBM

4.6
estrelas
16,628 classificações
2,016 avaliações

Sobre o curso

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....

Melhores avaliações

AG
13 de Mai de 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 :)

TM
18 de Jun de 2021

Very interesting course. It shed a light on what the structured approach really is. It's worth to pause for a moment with every step of the methodology and think how to apply it in real life. Thanks!

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1726 — 1750 de 2,016 Avaliações para o Data Science Methodology

por Lionel

22 de Jun de 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.

por Anna N

17 de Abr de 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.

por Rick G

31 de Jul de 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!

por Venkatesh S

18 de Set de 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.

por Marie D

24 de Fev de 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.

por Ankur G

19 de Mai de 2020

A good course to get insights about methodology used within Data Science to analyze and visualize data to make effective decisions. I thank the professors to make this course interesting.

A couple things which I think can improve the quality of this course. Videos can be made in a better way so as to facilitate people with non programming background. Also the case study used to explain the concepts in the videos isn't the same as the one used in the notebooks. If the case study used is same in both videos and notebooks, It would enhance clarity of the taught topics.

por Paul A M

8 de Mai de 2020

A very good overview of the problem solving methodology for data science projects. The capstone exercise was practical and helpful to put all of the pieces together in a logical order. Perhaps analytic approach and model development and deployment could have used additional modules or case studies. The single module for each is a good start, but a second case study could better illustrate the difference among predictive, descriptive, or prescriptive approaches and outcomes.

por Michael K

7 de Abr de 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.

por Haim D

5 de Mai de 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.

por Robert B B J

25 de Abr de 2020

Lab exercise/further reading doesn't make sense to me since I'm new to data science. Got a headache following what happening with the codes. The methodology introduced here is an IBM methodology and its pretty easy follow. Some of the terminologies are not enunciated clearly and it's pretty hard to track and understand. Overall, this course is a basic understanding of Data Science approaches and the use of important use methodology.

por Alireza F

3 de Jan de 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.

por Brian C

5 de Nov de 2019

A little wordy with the labs focused on shift-entering prewritten code as opposed to giving significant input. Also felt that one peer-grade being factored into final deliverable is a little sketchy. I had one peer completely fail my deliverable selecting lowest marks on each section of the schema yet when submitted a second time with no change (i was honestly happy that my deliverable met the requirements) i was awarded 100%.

por Nathan E

6 de Fev de 2020

I think the content presented was okay, and was generally presented quite clearly. The labs were well structured and easy to follow, but I didn't feel that I was learning skills to understand when to use different methodologies, or what kinds of challenges I might face along the way. The example given was clear and easy to follow, but I don't feel that I learned a lot that prepared me to analyze other data science questions.

por Vincent Z

13 de Jan de 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.

por Karel H

24 de Mar de 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.

por Josephine C

14 de Abr de 2020

An informative introduction to data science methodology, but the presentation of the material could use more work. The videos could use better production values, with perhaps a bit of music and more visual aides. There is also an annoying six seconds of silence at the beginning of each video which made me think there was something wrong with my audio. It would also be nice if some of the labs were a bit more interactive.

por Jennifer B

31 de Dez de 2019

While it is important to demonstrate that there is more to data science than simply applying a tool, this course did little more than name some steps in the methodological process and give a one or two sentence description. The main case study was fine for me as I have a health background, but were full of undefined clinical terminology. The description of what belonged in each step is somewhat inconsistent.

por Saman R

22 de Jul de 2019

The lecture videos are extremely verbose and monotonic. The features on the lecture slides have low resolution, and consequently, it's hard-to-impossible to read some of the contents on the charts and graphics. The lecturer talks non-stop without properly distinguishing between the steps. Lastly, the lecture slides are often redundant and have contents that don't really represent the step being lectured.

por Christian H

12 de Jan de 2020

the course videos are sometimes not exactly to the point when describing what has to happen in the different stages of the provided methodology.

this makes doing the final peer-graded review somewhat difficult.

also the description of the final assessments objectives is super vague (especially compared to the very good descriptions of the final deliverables and assessments in the other courses!)

por Avinash B

18 de Nov de 2019

Videos are at a high pace and the hospital use case introduces lots of information without proper slides,

when there is different text or points in the slides compared to the audio, it is hard to focus.

My sincere recommendation is to first talk the point in the slides, then explain the details. Also animations can be used to hide content and keep the focus on one item at a time.

por Reid N

12 de Mai de 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?"

por Morgane B

23 de Ago de 2020

Ce cours présente quelques méthodes d'analyse, mais elles ne sont pas assez structurées. Une présentation plus exhaustive des méthodes avec des exemples, voire une nomenclature pourraient être plus utiles. Le cours gagnerait en qualité s'il donnait un schéma par type de données et méthodologie de traitement conseillée avec ensuite les outils techniques recommandés.

por Hadi A

26 de Jun de 2019

Its an amazing course to give you an introduction to Data Science Methodology. But the case chosen was a hard case to understand specially if someone is a beginner in statistics and not into the medical field. I wasn't the only one who got confused while using the methodology on the case shown. Hopefully, a simpler case gets introduced in future.

por Dita A

4 de Mar de 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! :)

por Brandon B

29 de Abr de 2020

CONS: I would really prefer more interactive lectures. The lectures tended to be boring and monotone. Also the case study content many times was difficult to grasp because it is very specific to hospital field.

PROS: The material covered is quite beneficial in understanding the overall data science process. It is a nice summary.