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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
16,180 classificações

Sobre o 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....

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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2051 — 2075 de 2,443 Avaliações para o Análise de dados com Python

por Welamaza A M

18 de jun de 2022

Well taught and is challenging enough to keep one motivated

por Abhay S

12 de mai de 2020

Quiz sections are very simple in comparison to the lessons.

por Brijesh O

6 de mai de 2020

Final assignment could be better thought out. very simple.

por Rahul S

18 de jul de 2019

Very Helpful course..and very good contents..learnt alot..

por Narayanaswamy N

25 de mai de 2019

First go for module 8 - Machine Learning and come to this.

por Ajay M

7 de mar de 2020

The Non-Graded Online Assignment need more practice cases

por Filiberto H

17 de jan de 2020

Very difficult if you don't have some statistics bases

por Serena R T

22 de nov de 2019

A tough course yet interesting. Like the lab exercises

por Ran D

4 de jan de 2019

The question jumped up in the video is quite annoying.

por John A

20 de jul de 2021

Quite disappointed with the material in the course.

por Umasankar M

1 de ago de 2020

Need more model development examples will be helpful

por Themba M

11 de jun de 2020

Explanation of lab steps has a room for improvement.

por Andres E S G

11 de jan de 2020

It could have a little more theory about statistics.

por Adesua A D

4 de nov de 2019

My first course on coursera and its very informative

por Alexandru S

3 de jun de 2019

A lot of information, it is at times hard to follow.

por Boru R

6 de set de 2020

good course, but final assignment is way too simple

por siu t

19 de jul de 2020

Week 4 was too hard, while other modules were okay.

por Pham T S

13 de jun de 2020

Very good course for learning about buidling models

por Neelam S

3 de jan de 2020

Examples should contain more codes used frequently.

por ZJ Y

1 de out de 2019

it might need updating according to the new version

por Eirini K

20 de mai de 2020

Quite good to begin with, but not going in depth.

por Hui Z

26 de set de 2019

Good resource to have a knowledge of pandas, etc.

por Deepratna A

24 de jun de 2019

Time and topic depth are not proportional at all.

por Patricia W

23 de ago de 2020

I thought it should be a little more assistance.

por khaled C

22 de abr de 2020

There are some little mistakes in the notebooks.