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Bewertung und Feedback des Lernenden für Datenanalyse mit Python von IBM

4.7
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14,913 Bewertungen
2,236 Bewertungen

Über den Kurs

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....

Top-Bewertungen

RP

19. Apr. 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.

SC

5. Mai 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.

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1651 - 1675 von 2,242 Bewertungen für Datenanalyse mit Python

von giuseppe t

31. März 2022

​It is a well structured and quite valuable course; it could have been a masterpiece, if it had provided more connections, explanations, insights, in other words programming background related to all those different topics touched over the weeks.

von Monalisa p

4. Nov. 2019

This Course is very helpful for the beginners. This course is very detailed, and well explained. You will go through all the important things required for data analysis. This course's Lab is very strong, I must recommend you to do this course.

von ERNESTO C O C

10. Feb. 2022

Bom conteúdo na abordagem das principais funções para análise de dados, porém, carente de fundamentação teórica em relação às análises. Por não haver pre requisito, os fundamento poderiam ter sido abordados, ainda que de forma superficial.

von Sachin G

4. Mai 2020

Very informative course... very well designed... a bit fast-paced but concise and clear

it's just that if the final project could have been little more challenging so that there are more opportunities to apply what we learnt in the course.

von Terry G

1. Mai 2020

Great course. I felt like I can run my own models and test them now. There were some strange errors throughout the notebook that were raised in the forums but not addressed in the documents. Aside from that, it was pretty good for a MOOC.

von Adarsh K P

26. Sep. 2019

ton of new stuff to learn from.... super informative course...this course will introduce you to a lot of useful and important stuff and the best part is that each topic is explained first then comes the coding part which is just awesome.

von Chris A B

15. Sep. 2019

This was a challenging course that covered a lot of items. I believe I need more practice in these items (Linear Regression, Polynomials, Ridge, Fit, Predict, etc.) in order to have a much better understanding of the course materials.

von Guilherme P d C

7. Apr. 2019

Model Development and Model Evaluation content requires more intuitive examples, maybe adding some flowchart to explain the reasons of every step in Modeling and Evaluation. I am making this suggestion to make the course even better.

von Joe M

27. März 2020

Interesting class. Clearly designed to cover a lot of ground but not always in the detail some may like. Emphasizes showing some basic analytic work flows, but does not always explain how or why of a particular step in the workflow.

von Logan W

7. Nov. 2019

This was a very comprehensive course, but it could definitely use some revising on the labs that caused output issues. Additionally, some of the peer-graded material couldn't be uploaded due to syntax. Other than that, very helpful!

von João L F C

16. Apr. 2020

It was a good introductory and pocket course for Data Analysis with Python to me. The concepts were given pretty much straight forward, and the assignement didn't diverged much from what had been already seen throughout the course.

von Jhon P

4. Juni 2021

At the end, the final project link was wrong and nobody from coursera or ibm give me an answer. Fortunately, course partners share the right notebook and thus, I could finish my course. The topics are very well for this course.

von Michael g

4. März 2022

t​his was one of the better courses in the 10 course package. a bit more focused and less slapped together than the previous courses. also the lab load and have no glittches, aleast for me, unlike other courses. overall good

von Enrico G

9. Apr. 2022

Very nice and practical course. It gives you the tools to perform a regression analysis on a dataset. Perhaps, I might have focus a little more on the mathematical theory behind some method, like correlation, p-value...

von Rajan G

16. Juni 2020

This course helped me a lot in solving my basics about data cleaning, Visualization, Techniques for getting better result and most important how we can judge whether a model is good or not. Thanks for this great course.

von asher b

12. Nov. 2018

this course finally gets to some key Python functions for data analysis. Some of this may be difficult without a basic stats background. Only knock is there are MULTIPLE typos in the slides and labs. Needs to get fixed.

von Miranda C

23. Juli 2020

This course went fairly well, I just hope that the information will be repeated in the next course in the certificate program (IBM Data Science certificate) as I don't feel like the information has really sunk in . . .

von Ankit S C

15. Jan. 2020

The Model Training and Evaluation weeks could have been more elaborate. Instead of just telling to do something, it would've been better to explain why we are doing it and how is it working internally, at a high level.

von Mario A T

28. Feb. 2020

Tuve problemas con crear la cuenta en IBM cloud con mi correo personal primario , no pude encontrar soporte ni orientación de que hacer , me toco ingresar con otro correo , no se porque no fue posible con el mio

von Junior N

18. Aug. 2019

This course is pretty good and give a good introduction to data analysis with python. However, there is a problem in the course's methodology : functions are given without any introduction...just implementations.

von Glison M

9. Aug. 2020

The course was good in introducing Pandas, Numpy and Sci-kit to beginners. Adding more graded programming activities would be a great addition to this course, as there is only one graded programming assignment.

von Orsolya N

26. Juni 2020

Certain parts were too fast, and there were some technical issues with the labs at times, but there's always the possibility to look up the blurry parts online. Overall it was interesting and well put together.

von Kyle H

25. Feb. 2020

This definitely could be more project based than it was, and focus more on applying coding skills than just reading them and watching videos about them, but it's a great overview of some useful techniques.

von Keerthi S

3. Nov. 2019

The final assignment had some errors in submission with some questions not allowing for upload of the answers (Question 3, i.e.). Did not feel great about this error. Otherwise, great course - very useful.

von Mantra B

3. Nov. 2019

Overall a great course. All essential Data Analysis processes are covered in this course. A small nitpick is that Week 5 material was a little less in depth. Moore examples in videos will be a great help!