Chevron Left
Voltar para Análise de dados com Python

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

4.7
estrelas
15,111 classificações
2,276 avaliações

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.

Filtrar por:

1926 — 1950 de 2,282 Avaliações para o Análise de dados com Python

por John P

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 Selina 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.

por Malege T M

26 de ago de 2019

A thoroughly impactful and well presented course

por Sachin M

29 de jun de 2021

Need more details other-wise very good course.

por Hussain T

30 de abr de 2019

a good course but its not going deep in things

por Serdar M

16 de nov de 2018

would be better if there were more exercises.

por SARAVANAN M

18 de ago de 2021

Not very easy to understand the coding part.

por Myrlene C

15 de mar de 2021

Great Course.. It went a bit too fast though

por Shashank V M

9 de abr de 2020

Simple as compared to real world challenges.

por GSR S

20 de set de 2019

Good Lab examples and thorough explanations.

por Ali N

17 de jul de 2019

It was a good course. The labs were helpful.