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Python을 사용한 데이터 분석(으)로 돌아가기

IBM의 Python을 사용한 데이터 분석 학습자 리뷰 및 피드백

4.7
별점
12,959개의 평가
1,893개의 리뷰

강좌 소개

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....

최상위 리뷰

SC
2020년 5월 5일

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.

RP
2019년 4월 19일

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.

필터링 기준:

Python을 사용한 데이터 분석의 1,872개 리뷰 중 1826~1850

교육 기관: Brahmrysti A B

2020년 6월 2일

A lot of mistakes here. Clearly rushed and not given the care and attention it needed. Some assignments REQUIRE you to go to the discussion board to figure out what the author intended and why your code isnt working.

교육 기관: Ashish D

2019년 12월 22일

Does the job of a good introduction.

Very limited and restrictive practice and assinments.

For a true learning experience one needs to do a lot of external research and work to show a measureable benifit.

교육 기관: Steve H

2020년 5월 19일

The content is good but there are a lot of mistakes and typos in the material. The peer review is extremely vulnerable to errors - only one person reviewed my assignment and gave me the wrong mark.

교육 기관: D W

2019년 8월 17일

Useful course but riddled with typos & inconsistent questions and answers. Needs a proper review by someone (probably not the people answering on the forums, who didn't seem especially clued up).

교육 기관: Aaron C

2020년 7월 15일

The videos really are not very engaging (relative to any other course that I have completed here on Coursera). The concepts are not explained very thoroughly. Thanks anyway guys.

교육 기관: Berkay T

2019년 9월 27일

Too much content, so less practice. This course doesn't teach anything that you can make use of in the long term. It only gives an idea on what you have to work on in the future.

교육 기관: Sheen D

2019년 8월 11일

This is by far the worst course in the specialization. So many mistakes in the lab session, including unclear instruction, or syntax is not uniform across each module, and etc.

교육 기관: Michael M

2019년 12월 20일

The IBM Developer Skills Network (at labs.cognitiveclass.ai) is very slow and doesn't work most of the time.

It doesn't allow to finish the course properly.

교육 기관: Ismael S

2019년 6월 2일

Content is thrown to the student with too much information and videos of only 3-4 minutes. Too much to absorb and too little to practice properly

교육 기관: Malcom L

2019년 1월 11일

more hands on, project based/game based learning. Mindlessly watching videos and regurgitating code in the labs can not be the only way.

교육 기관: Santanu B

2019년 4월 16일

Not a great course. Sometimes it is too fast and the explanations are very short. More hands on exercises would have been more helpful.

교육 기관: Rajesh W

2018년 10월 17일

There are plenty of mistakes in the videos and in the lab session as well. Hope you guys can clear out those.

교육 기관: Wayne W M

2019년 10월 2일

This was a very challenging course. Some concepts were difficult to grasp and required additional research

교육 기관: Mark F

2020년 4월 8일

Frustrating when the peer reviewer doesn't actually understand Python and deducts marks for correct code.

교육 기관: Hunter I

2020년 4월 17일

Leaned some, but not a whole lot of real-world application, I recommend people take Python courses more

교육 기관: Ashwin D

2020년 4월 29일

Not enough hands on problems, including variety and volume. Expected more from an IBM program.

교육 기관: Nathaniel S

2020년 3월 29일

Don't spend your money on IBM Data Science Cert. Course labs are full of bugs and not working.

교육 기관: Edwin S J

2019년 5월 25일

Suddenly introduced complex codes and statistical functions. Videos were way too fast.

교육 기관: Somak D

2018년 10월 30일

moderators do not respond to questions raised in forum. leading to incomplete learning

교육 기관: Jen E

2019년 7월 31일

So many problems with the lessons and the final project.

교육 기관: Rasmi D

2019년 6월 18일

very high level... topics not covered in depth

교육 기관: Q B M

2020년 8월 8일

some commands are not fully explained.

교육 기관: Hakki K

2020년 7월 9일

Hi,

I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".

Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)

Course 1: approximately 9 hours to complete

Course 2: approximately 16 hours to complete

Course 3: approximately 9 hours to complete

Course 4: approximately 22 hours to complete

Course 5: approximately 14 hours to complete

Course 6: approximately 16 hours to complete

Course 7: approximately 16 hours to complete

Course 8: approximately 20 hours to complete

Course 9: approximately 47 hours to complete

This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.

(*): https://www.coursera.org/professional-certificates/ibm-data-science?utm_source=gg&utm_medium=sem&campaignid=1876641588&utm_content=10-IBM-Data-Science-US&adgroupid=70740725700&device=c&keyword=ibm%20data%20science%20professional%20certificate%20coursera&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=347453133242&hide_mobile_promo&gclid=Cj0KCQjw0Mb3BRCaARIsAPSNGpWPrZDik6-Ne30To7vg20jGReHOKi4AbvstRfSbFxqA-6ZMrPn1gDAaAiMGEALw_wcB

교육 기관: Thamarak

2020년 8월 22일

This course is too hard. This should be go on more slowly and explain more about meaning of each value described. The course is not for beginner and not for a person who doesn't have enough statistics background.