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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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15,077 calificaciones
2,271 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

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.

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.

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201 - 225 de 2,276 revisiones para Análisis de datos con Python

por Jafed E G

6 de jul. de 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

por V.S.S. T

15 de may. de 2020

Very helpful and useful course especially for beginners who are willing to gain knowledge on Data Analysis. I would recommend starting with this course for those who are interested in mastering their skills in Data Science later on.

por Zwanga R

27 de ene. de 2022

The course was very interesting and exciting. The videos were well presented and laid out in a way that is easy to follow and eager to learn follow. Assignments and exercises made us quite interactive and I really enjoyed that.

por Sayak P

13 de may. de 2020

This course is based mostly on very basic concepts , it's good for those lacking the slightest knowledge in the field of statistics , but yes for beginners it's pretty fine.I loved the presentation of those labs,quite useful

por Phil L

31 de may. de 2020

Really good course. More challenging than I expected. I expect it will take a few applications of the concepts to get them down 100%. Very good presentation of material. The labs were critical to learning the concepts.

por QUAN Z

25 de feb. de 2019

The course perfectly fits those who has some knowledge on python and want to do data analysis with it. It explains how professionals would process data, build model with the data, and use the model to solve a real problem.

por Min T A

18 de mar. de 2021

This course covers exploratory data analysis and even furthers onto machine learning with some key statistical introduction such as Linear Regression, Correlation, P-value, F-score, etc, explained in its most clear form.

por La D Q

25 de sep. de 2021

A very interesting and informative course. I have access to the foundational knowledge in using Python for Data Analysis. The labs are helpful as well. I know I can apply the lesson I've learned in real life, real work.

por Pranav K J

10 de may. de 2020

Very good course and well designed , so that a new person also can understand it very well. They way it is taught is admirable. I will recommend, the aspiring data science Engineer, must take this course specialisation!

por Jonathan I O

14 de jul. de 2019

This course provides a robust walk-through in the use of python for data analysis. The labs ensure the theories taught are put into practice through hands-on projects that further reinforces skills learned. I loved it!

por Aman S

26 de mar. de 2020

A very detailed course. The hands-on exercises were really good and I got to learn a lot of things from this wonderful course. Thanks to all the instructors for their hard work in putting together such a course for us.

por Katja K

25 de may. de 2022

Really helpful course to learn the basics of pandas and scikit-learn. The videos were easy to follow and very clear, and they were complemented well with the lab notebooks and quizzes. Thank you to the organizers! :)

por Paul A

24 de sep. de 2020

I think this course is the highlight of the Applied Data Science specialization. I learned so much and gave me the tools to learn more on my own. It was really engaging and I never had a dull moment in this Course.

por Roseline A

9 de jul. de 2020

This is a great course. I went away with so much knowledge on modelling and model testing. The labs are also very well structured and not just a repetition of the class presentation. I recommend this course highly.

por Ferenc F P

26 de feb. de 2019

The beginning of the course helps you understanding how you can manage your data with python. In the end linear regression, and ridge regression is also introduced. Good course for those not familiar in this field.

por Wilfredo A

30 de ago. de 2018

Excellente content and very didactic laboratory. There is a lot of information in the course and at the same time it encourages me to investigate and further develop, particularly in Model Evaluation and Refinement

por Brian B

5 de dic. de 2020

Very "meaty" hands-on work with doing some data wrangling, exploratory analysis and models with single linear, multiple linear, and polynomial regression fits. I took several hundreds of lines of code in my notes.

por Ashutosh P

29 de abr. de 2019

Thank you so much for creating this is great learning and useful course that I got for Data Analytics.This course is very beneficial for all to enhance the knowledge about data analysis with Python.Thank you sir.

por Nilo V

28 de jun. de 2020

I find the course well organized and the lab sessions made use of relevant instruction that I can use for my daily work. The final assignment could be more challenging. Overall a very helpful learning experience.

por RAM K A

20 de abr. de 2020

Excellent explanation about the topics and helpful examples. Course requires reading outside the course module for better understanding. Will be helpful, if you could run the program on the window and explain us.

por Aditya A M

22 de may. de 2022

Extremely helpful course for those who are new to the terms of machine learning concepts are well explained and sufficient practice taken in Lab modules and the last peer-graded assignment helped me learn a lot.

por Daniel L

13 de oct. de 2019

A rotating three-dimensional plot may be added. Its very easy and practical to complement the analysis presented. Otherwise the course is very complete.

Also, the pipe explanation may be improved a little bit.

por Seemant T

24 de abr. de 2020

I am reviewing this course after the completing it. It was a good learning experience!!

The entire course is based of the student interaction and requires basic knowledge of Python and its Libraries.

ThankYou..

por vrushabh l

7 de may. de 2020

Very good course to begin with in the field of Data Science. The analysis of data is very important before we start implementing predictive models on the data, which has been covered very well in the course.

por Gerson C H

5 de feb. de 2020

Its a great introduction a clear explanation about Machine Learning, generation of linear regression models and all the things to do before to the analysis front the data. I'm very excited and gratefullnes.