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Opiniones y comentarios de aprendices correspondientes a Análisis de datos con Python por parte de Habilidades en redes de IBM

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16,210 calificaciones

Acerca del Curso

Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models. Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data - building machine learning regression models - model refinement - creating data pipelines You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge....

Principales reseñas

SC

5 de may. de 2020

I started this course without any knowledge on Data Analysis with Python, and by the end of the course I was able to understand the basics of Data Analysis, usage of different libraries and functions.

RP

19 de abr. de 2019

perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.

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2201 - 2225 de 2,449 revisiones para Análisis de datos con Python

por Lucas T H D

2 de jun. de 2020

Some of the instructions were not clear enough, with a couple of typos here and there. Alot of explanations can be given to the code, e.g. what is for what. Also, before the video quizzes, needs to let learners look at the screen, pause before flashing out the quiz. Overall, good experience. Aside from having some difficulties trying to understand some parts of the module, but able to pick up Data analysis thanks to the course.

por Liam M

17 de ene. de 2019

So far the other courses in the Data science specialisation contained a final graded assignment. I found them really useful. This course didnt. Also, instead of telling us about all the tools available in the libraries, maybe explaining why we would use them would be better. I could code these functions myself if I understood them, but just using a library seems like it could lead to laziness and a lack of understanding.

por Josep R C

20 de may. de 2020

+Useful course for beginners. You get to learn basic concepts although these are not enough to get to work on real projects. Another good point is the set of useful libraries and methods presented in the course.

-Downsides of the course are the amount of mistakes found in the labs which are supposed to help understand the theory seen in the videos, but in some occasions can even mislead and mess the students up.

por Vimal O

9 de nov. de 2021

On overall IBM data science professional certificate track: Pros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

por Carsten K

11 de mar. de 2020

Great coverage of topic, but unfortunately comes with several imprecise (or even planely wrong) explanations in the videos. Video quality (style of presentation) is ok, but sometimes missing things are slightly missaligned or questions show up before the topic/sentence is finished - could use some polishing. The hands-on labs are great though - if the notebooks open or the servers are reachable.

por Kevin B

19 de oct. de 2022

Warning for those whose native language is NOT English: These IBM Data Science courses are in DESPERATE need of review by a native English speaker. If English wasn't my first language, I can only imagine how much I would have struggled. It is pretty unbelievable that they expect people to pay money for courses that have so many many grammar, syntax, and audio transcription errors.

por Felix S

1 de jul. de 2019

Material to learn data analysis was very good but had quite a few bugs. It was very annoying to review the assignment of a peer because it is not possible to zoom into the screenshot. Furthermore did I need to flag a person because he had copied screenshots and his notebook was empty or only with screenshots but I was still required to review a second person to complete the course.

por Jackson V

5 de jun. de 2019

Not as impressed with this course as the previous courses. My main complaints were:

-Seemed to be some gaps between the lectures and labs

-Some lectures seemed rushed through w/ simple questions, and did not prepare well for the lab

-Pre-written code in labs would produce errors

-Spelling mistakes (i.e. the week 5 "Quizz")

-No final project to conclude and summarize up our learning

por Chioma J E

9 de abr. de 2019

The course was not detailed enough. I think the instructor assumed that people taking the course would know a lot about Regression, Correlation and some other statistical functions, that it was hard to understand or follow at times. Maybe consider 'dumbing' down down the statistical functions so that newbies can also follow.

Overall interesting course. Thank you.

por Kam S H

23 de ene. de 2021

First 2 weeks were fine for beginners, but after week 3 where all new different syntax and concepts like seaborn, visualization, Regression models etc etc were thrown in, it got way too advanced for beginners especially when there insufficient and effective practices available to hone the knowledge. Have to spent most of the time self-learning on other websites.

por Nikhil B

25 de feb. de 2019

This is an excellent course for beginners in the data analysis and data science fields as it explains deep technical concepts in layman terms along with the Python code for the same. However, not a perfect course for someone wanting to go into conceptual depth or wanting to expand their knowledge of analysis in Python beyond use of standard packages.

por Fares A G

18 de mar. de 2020

Needs to rely less on the cognitive class platform, just host the ipynb files externally as the labs are inaccessible alot of the time. Course only covers regression models, I would've liked to see SVM, KNN and other algorithms. However the course excels in explaining the relevant maths related to regression and regression evaluation

por Mbongeni N M

9 de sep. de 2018

It was educational, but when you pass a quiz, there should be an option to get answers to the questions you got wrong. And the practice exercises were filled with mistakes, particularly week 5. And the instructor was not responding to students' questions for week 5, which was one of the most challenging weeks. That was annoying.

por Yariv Z

23 de may. de 2020

A lot of un addresses subjects. Many mistakes both in the videos and in the labs.

Overall after viewing all the videos again and summarizing for my self everything, I felt a lot better with the material but I think the course is not organized. I also think that it should get into some mathematical subjects more thoroughly.

por Brisa A

28 de jun. de 2019

A lot of errors make the course confusing. Also, the assigments and labs are "too easy"... it is clearly shown in the videos that there is much more to be done, but the course only demands you do about 50% of what is taught. How are we supposed to really learn without practice?? Give us real and demanding projects!

por Antonio P

5 de mar. de 2019

The content was good, but there were numerous mistakes and inconsistencies (i.e. a chart would show a red line as a training set but the write-up would say the red line was a testing set). Also, I would have preferred to have shorter and more lab activities. The lab activities were too few and each was too long.

por Vyacheslav I

15 de nov. de 2019

Grammatical mistakes, low quality videos, low quality slides and videos. Labs are okay, though no in-depth clarifications and explanations are given. Like "to do this you write this". Options? Explanations? What for? It's too much. Just remember how we wrote these lines and copy-paste them in you code later.

por Hemanth S

4 de may. de 2020

Course is a bit too short and way too fast paced for what it is trying to convey! Of course people will be able to complete the course without problems but, have to re-visit and brush knowledge on these a lot more. Anyways, it is a bit of confidence booster. You feel like you learnt a new course.

por Rakshita S

26 de jul. de 2020

The reason I am giving a three to this course because compared to rest it was a bit fast-paced. Also, I feel we need a prerequisite of statistics before starting this course which was not mentioned anywhere.

Guess it is time for a lot of practice. Wish there were more assignments as well.

por Fernando M M E

23 de oct. de 2021

I am doing this course as part of the IBM Data Analyst Certificate and even it was the 7th course I take I don't feel it was well explained. The videos pass very fast and the explanations are insufficient to understand what happen in the labs. I think there is place for improvement.

por Sisir K

15 de feb. de 2019

Highly technical and complex in nature. Difficult for people just starting out with data science. The hands-on labs are more useful than the videos themselves. The quizzes in between videos felt a bit too easy and mostly comprised of examples (as questions) in the videos themselves.

por Jingyi Y

16 de abr. de 2022

The final assignment is terrible. I've spent a long time setting up the environment because the online notebook is not available. And some questions are hard to find what they are really aimming for. And instruction is actually bad, at least compared to the course.

por Raghav N

14 de sep. de 2018

This course is definitely very helpful to people who are passionate about Data science and have basic to intermediate understanding of Python but this course can be much better if it includes coding assignments rather than quiz submission. It was a great experience.

por Ahmed O S

1 de ene. de 2023

The course is great, however it seems to assume knowledge of things that are not listed as prerequisite knowledge, mainly Data Visualization methods in Python and Regression models. I would also have loved if there was a recommended reading section on these parts.

por Roberto B

10 de jul. de 2019

I'm not convinced that this is a great way to learn, I just feel there needs to be a better way of learning this than the approach this course takes, I kind of learned the python commands but I'm not sure I understand how to apply them in the real world. We'll see