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Voltar para Análise de dados com Python

Comentários e feedback de alunos de Análise de dados com Python da instituição IBM Skills Network

4.7
estrelas
15,272 classificações

Sobre o 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....

Melhores avaliações

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 mai 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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2201 — 2225 de 2,310 Avaliações para o Análise de dados com Python

por Ashwin G

26 de abr de 2019

Too fast and could have included more examples.

por Gerhard E

12 de fev de 2019

Copy of videos, not a fan of tools used in labs

por Aditya D J

8 de mar de 2022

Nice, but I can't try IBM Cloud Trial for free

por Yasmin A

3 de fev de 2020

Un cours riche et adéquat pour les débutants

por Hiro H

27 de nov de 2019

Very nice course. It gives you what you need

por Brian S

29 de mar de 2020

Notebooks are sloppy, with typos and errors

por Anjali

6 de abr de 2022

I am not able to download my certificate.

por Fariha M

28 de set de 2020

The course didn't seem challenging to me.

por Sachin L

26 de set de 2019

More examples and detailed explanation

por Nilanjana

12 de jul de 2019

More examples and code examples needed

por Hamed A

8 de abr de 2019

The course needs a final assignment!

por piyush d

6 de dez de 2019

exercises could have been better.

por Jyoti M

26 de mar de 2020

I felt it was too fast to grasp.

por Baptiste M

2 de nov de 2019

Final assignment is quite messy

por Murat A

21 de abr de 2021

could not access the labs.

por Yuanyuan J

17 de jan de 2019

Not clear on the last part

por Ahmad H

8 de jun de 2019

This course is very tough

por conan s

20 de dez de 2019

Lots of technical issues

por David V R

17 de jun de 2019

Exams should be harder

por Riddhima S

8 de jul de 2019

la lala la la laa aaa

por Daniel S

8 de fev de 2019

Not easy to follow.

por Allan G G

10 de mai de 2022

Muy poco practico

por TATOU D T

27 de set de 2021

très bon cours

por Vidya R

16 de abr de 2019

Very Math!

por James H

29 de abr de 2020

Definitely not one of my favorite courses in the Data Science Certificate series. There were times I was ready to give up the pursuit of the certificate altogether during this class... There should have been a prerequisite for this course of the statistical tools and methods that would be covered in here... Sure I could program these things after this class, but i still dont understand why I would choose to use one over another? This is one of those classes where you walk away feeling more confused than when you went in... Also there were a lot of mistakes, typos, and obsolete things in the labwork - some reported and acknowledged months ago, but still not fixed in the lab (video I can understand, but not the labs)