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

4.7
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15,091 calificaciones
2,274 reseña

Acerca del Curso

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

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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26 - 50 de 2,280 revisiones para Análisis de datos con Python

por Aditya J

18 de may. de 2019

None

por William B L

20 de mar. de 2019

The techniques, methodologies, and tools presented here are essential parts of the data analysts tool box. The coverage was, in general, well done. I am glad I took this class, and look forward to the next.

That said, there were problems:

1) The meta parameter, Alfa (or is is Alpha) is never explained, except that it helps. To be useful, the student needs to know a bit more. Also, the spelling should be consistent between the training texts and the lab.

2) The lab needs maintenance to keep up with changes in the Python packages. I received warnings about using deprecated functions and values.

3) The text needs grammar/spelling checking, for example, the end of the course exam is labeled "Quizz"

por Karen B

25 de may. de 2019

Does an excellent job in providing the Python commands needed to do data analysis, along with some descriptions of what the steps actually involve. Has quite a few typos and minor issues -- looks a little sloppy.

por Matthew A

13 de abr. de 2021

During the 4th week of the course, lots of important information and explanations are over summarized and in some cases skipped over. Learning tools outside of what is provided in the course or a decent understanding statistics is required in order to be successful in this course.

por Thamarak

22 de ago. de 2020

This course is too hard. This should be go on more slowly and explain more about meaning of each value described. The course is not for beginner and not for a person who doesn't have enough statistics background.

por Sobhan A

6 de may. de 2020

Low quality.

Do not recommend this course at all.

Boring teaching method.

Full of errors.

No IT support for problems.

por Titans P

17 de ago. de 2020

worst ever

the greatest thing i have learned here is patience and searching online

por HIMANSHU S

30 de jul. de 2020

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.

por Usman A

29 de jul. de 2020

AN excellent course. Hands-on training on the cloud makes an individual really involved. So far the best online course I have ever taken, and I have learned Python programming a lot from this course.

por Hakki K

9 de jul. de 2020

Hi,

I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".

Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)

Course 1: approximately 9 hours to complete

Course 2: approximately 16 hours to complete

Course 3: approximately 9 hours to complete

Course 4: approximately 22 hours to complete

Course 5: approximately 14 hours to complete

Course 6: approximately 16 hours to complete

Course 7: approximately 16 hours to complete

Course 8: approximately 20 hours to complete

Course 9: approximately 47 hours to complete

This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.

(*): https://www.coursera.org/professional-certificates/ibm-data-science?utm_source=gg&utm_medium=sem&campaignid=1876641588&utm_content=10-IBM-Data-Science-US&adgroupid=70740725700&device=c&keyword=ibm%20data%20science%20professional%20certificate%20coursera&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=347453133242&hide_mobile_promo&gclid=Cj0KCQjw0Mb3BRCaARIsAPSNGpWPrZDik6-Ne30To7vg20jGReHOKi4AbvstRfSbFxqA-6ZMrPn1gDAaAiMGEALw_wcB

por Vincent L

17 de sep. de 2018

Ton of errors, both minor and major, in the videos and the quizzes. For example, saying the a difference between two variables is significant because p > 0.05. I report them all and I've stopped counting.

Not professional at all.

por Anastasiya B

22 de sep. de 2019

Low technical quality of the course with lots of typos, errors and comletely mess in final assignment.

Low quality of material, bad structure, and you can get your certificate just by clicking shift+ enter

por John K

7 de jul. de 2019

Poorly put together course - especially the labs. Frequent misspellings, incorrect links and confusing instructions. The technical problems are a greater challenge than the course material.

por Uygar H

14 de mar. de 2019

I have really learned many things in this course which are meaningful and helpful in real life. It is not just lines and numbers , it is exciting how you can apply these methods to find solutions in your real life problems. Combined with strong Python skills , you will enjoy more..Thank you

por Daniel T

9 de abr. de 2019

This was a great review of stuff math I learned in high school and college. Of course it's all easy now because it's baked into Python. We used to do it by hand and with slide rules back in the early 1970s

por Shashank S C

6 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.

por Firat E

4 de jun. de 2019

It is really a good course, simple to understand and very complete. Thank you !

por Ashirwad S

21 de may. de 2019

Recommended course to understand the how to do data analysis using python

por Jim C

20 de may. de 2019

Well organized, good explanations, and very good labs.

por Aditya M

21 de may. de 2019

Overall apt content for beginners and naive learners.

por Vineet M N D

20 de may. de 2019

Great experience

por Shernice J

30 de mar. de 2019

Instead of having a lab after each topic, this course one lab per week encompassing all of the topics. Some might find that better than having smaller labs but to me the information was assimilated better when i did a lab right after the topic. That being said, you can open the lab first and follow along with/after each video. You just need to be mindful of what works best for you. Taking time to understand the code is a must and some more documentation would be helpful. I wasn't a beginner with Python and it took some time and work out what was happening at times.

por Akiru J C

12 de abr. de 2022

I really enjoyed this course. Few things to suggest:

- Go over Statistics in more detail. Had I not studied Statistics in university, I may have found this topic confusing.

- Felt like I could have learned more if the labs were not filled-out halfway

- Too many multiple choice questions in the quiz and final. These should be more interactive with lines of code we would type insetad of clicking a bullet.

- The math covered in this course was very high level. I.e., Chi-square and linear regression require more hands-on practive in order to grasp.

por Itshak C

13 de abr. de 2021

Loved the labs. Hated the Videos. The amount of information that is thrown at you in a 1 min video is very unsettling as it makes you think you haven't understood a word of what they say and then the labs immediately clear everything up and then you feel like the smartest person alive. It's an uphill battle at times but the end result is pretty helpful regardless of the reason you're perusing the course.

por Devansh N

5 de may. de 2020

Was a bit tough to keep up at the week 4 and week 5 but overall a very good course