Voltar para Análise de dados com Python

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13,289 classificações

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1,948 avaliações

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....

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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por Ning C

•12 de Mai de 2020

Clear structure and message delivering. I have learnt a lot from this course within a short time. Teaching assistants answer questions in each weeke's forum also with good clarity and patience. Although some mistakes, cannnot obscure the splendor of the jade. :) Looking forward to a better version after the improvement on typos.

por Mats B F

•1 de Mai de 2020

You can consider some more explanations on how the training and testing codes are linked together and what explicitly the Python codes does. This was the elements I struggled to understand. But this was the only part that also was new to me. All in all, the material was well explained and the course was very interesting.

por José F M V

•7 de Out de 2020

I just have an issue with some minor bugs with the Coursera web app. I don't know if they are specific of this course or as a whole. For example, when clicking in next assignment sometimes it jumps two assignments. Or when you type too fast the system just writes the last letter. Other than that is pretty ok.

por Jeff L

•17 de Jun de 2020

great lectures and projects. on data analysis topic, IBM has chopped contents into many small courses, which make student confused and hard to find which one to take. IBM should consolidate them into 4 or 5 courses that are focused, heavy weighted, so that students can build rock solid knowledge and skills.

por Roy v E

•23 de Abr de 2020

The courses are very good to get familiar with Data Science and what it essentially is. I would have like practise examples with answers after each chapter to practise it in but that is just how I learn. Overall I learned a lot about new resources and how to do certain things in the Data Science world.

por Ranjeeta R

•25 de Mar de 2020

I liked the course. Highly recommended for someone who is looking for coding experience for data Analysis using python. Please practice the lab that will make you confident. Only thing which bothered me is the final assignment review. It was not correctly reviewed. I lost 4 marks. Hope this helps!

por Joseph O

•11 de Mar de 2019

a few discrepancies here and there, please see the comments in the discussions. Other than that, very good! This course was more difficult than the others, and so i guess this is why employers prefer potential employees hold a PhD, or at least maintain a high algebraic/calculus/statistical aptitude

por sumit c

•6 de Jun de 2019

The underlying basics of Data Analysis with Python were deeply conveyed. Simple examples and easy to operate commands were greatly described. I would suggest everyone take this course whether or not they know to code. It is always great fun to learn new concepts and Coursera makes it possible.

por Susan A

•6 de Mai de 2020

Some of the Regression-model and Plotting topics that were tested on the Peer-Graded Assignment should have gotten a little more time in the videos. The best solution to this would be to put the "Data Visualization with Python" course BEFORE this one, as it devotes more time to these topics.

por John B

•9 de Set de 2019

Contained some simple grammatical errors, as well as some syntax typos in some of the modules. The most relevant thing I would criticize is the lack of depth with describing certain topics ion the modules as they can be very complex. I recommend studying the section notebooks thoroughly.

por Michael K

•28 de Abr de 2019

There is a lot to unpack in this course. If you have a statistics background, this may seem kind of trivial, but for the rest of us it is loaded with ways to view data. My only criticism would be that it sometimes skims across an advanced topic without really giving a general overview.

por Irving B

•11 de Out de 2018

This course gives a very clear view of the tools used to find the best way to analyze data when looking for the best model to predict target values. The use of Jupyter Notebooks to run code for the data analysis is very useful and enables the student to experiment on his own for options.

por yimingguo

•24 de Mar de 2019

I have start this course without knowing any Python code. I made it through but with a lot of rock with all the code. like a For loop or simple Python code. I suggest to study basic Python code then start this course but this course did push me a lot on Python code learning with Youtube

por Jurriaan A M

•11 de Fev de 2021

Only 1 thing i miss in this course : some extra reading material because especially the last subjects here are a bit tricky to comprehend in full. Presentation overall is great, the labs are really helpfull as they are packed with excercises AND extra info. So yeah : take this course!

por Lauren J

•7 de Mai de 2019

This was a good course, but didn't have as much labwork as I would have liked. There were a lot of labs, but they were mostly already completed by the instructors - more of a read-along than actually doing work yourself. That said, it was a valuable course and don't regret taking it.

por Nicole L

•4 de Out de 2020

This was a very challenging course. i don’t think I had any business choosing an intermediate level course because I have no experience in Data Analytics so I am a beginner. It was very interesting to see how statistics and math concepts were applied though and I did learn a lot.

por Leandro P

•9 de Out de 2020

Great course to help us understand more about Python libraries. Just marked as 4 stars because I wish we had a better conclusion, showing us how to explain the charts and values to a meaningful insight for decision making. There could also be more dataset examples for training.

por Benjamin S

•17 de Jan de 2020

The course teaches an incredible amount of information in a relatively short time. The downside to this is that users don't get enough practice within the course on the data analysis methods and functions taught. Additionally, there are a lot of typos that need to be fixed.

por Brett H

•3 de Ago de 2020

I think the breadth of content in the course was a bit too wide. More modules, and Python content, focused on exploratory data analysis could've been expounded upon, instead of so quickly moving into predictive analytics. Nonetheless, I did gain value from the course.

por Mukul B

•9 de Nov de 2018

This module is loaded with concepts. Even though they are introduced in a logical sequence, it gets a little overbearing and tend to lose the relevance in the context of car price prediction. At least, now I am aware of the techniques, methods and python's capability.

por Luis O L E F

•14 de Nov de 2019

Good introductory course. Even though it is an introduction, the course would benefit a lot from including a bit more of theory, even as optional material. For example, including theory about ridge regression, instead of just mentioning how to implement it in Python.

por Miguel C V

•21 de Jun de 2020

It is a great course. The one thing I believe could be better, is to deepen the scope of the mathematical concepts. Indeed, it is a course that assumes knowledge in that area, but it would be great to include links to papers or articles that explain those concepts.

por Venkata S S G

•28 de Jan de 2020

Content was decent. Do ungraded labs provided as practice exercises if you want much exposure and and free flow of code while using the data analysis libraries. Overall, the course is helpful for an intro and intermediate level. Will definitely work as a refresher.

por Juan V P

•16 de Abr de 2019

I think that you missed more detailed explanations on how to analyze the results, especially for those of us who are not mathematicians or with advanced knowledge of statistics. But, is a fact that In the end it was the course i've enjoyed the most. This is awesome

por Beylard P

•25 de Mar de 2020

Great notebooks and clear content except two points :

1 - polynomial regression and pipelines have not been enough thorough and detailed. Quite complicated to aprehend

2 - final assignment question 8 - nothing to do. answers were already in the downloaded notebook

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