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Python을 사용한 데이터 분석(으)로 돌아가기

IBM의 Python을 사용한 데이터 분석 학습자 리뷰 및 피드백

4.7
별점
15,077개의 평가
2,271개의 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

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.

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.

필터링 기준:

Python을 사용한 데이터 분석의 2,276개 리뷰 중 76~100

교육 기관: Denis M

2020년 9월 21일

A very comfortably created course - no stress at all. However all that you can get is become familiar with the data analysis tools. May be that's the point.

교육 기관: Ruchir V

2018년 12월 19일

I think few more practical exercises or at least references of the same would help better understand the overall fundamentals.

교육 기관: Rebecca V

2019년 3월 5일

Material covered is useful, but there are a lot of typos and mistakes in the lecture slides and labs.

교육 기관: Rene P

2019년 3월 24일

There could be links to functiones libraries in the lab for a fast check of a function if needed.

교육 기관: Ugur S O

2020년 12월 21일

I think the quizzes can be in the format of programming required questions.

교육 기관: Charles C

2019년 2월 5일

Some mistakes/ typos in the exercises and slides, but great overall

교육 기관: Yogish T G

2019년 3월 30일

An assignment should have been included

교육 기관: Miguel E M

2020년 4월 15일

There where some typos in the labs that could confuse most learners. I didn't feel like the course prepared people for real applications. The final project was quite hard because of this .

But it does give you a wide vision on hoy pandas work and some basic but apparently often used tools.

I see this course as a complement to a more detailed data analysis resource or perhaps as simply as an introductory view.

교육 기관: Jaime V C S

2019년 2월 22일

Hello,

in this course there were some errors on the slides, and some quite complicated topics (almost every time related to statistics) was given in a very over-viewed way. Also, some of the python codes were not explained very well, with some terms of them seem to be kind of arbitrary for those who are beginners in the language. My impression is that this course should be longer and more detailed.

교육 기관: arda

2018년 11월 20일

Overall I benefitted the course material as a beginner in python and data analysis. The questions were too trivial but maybe that helped me remain engaged with the course and complete it in a short time frame. There were some bugs, typos and minor quality issues that did not really effect my overall experience.

교육 기관: Katarina P

2019년 6월 27일

Many typos in videos, stats explained on a very rudimentary way (and often inaccurate), Watson environment is awful as it takes ages for some simple regression plots to be made, it freezes and the interface is not user-friendly, yet we have to use it.

교육 기관: Sadanand B

2019년 2월 7일

Seems like there are quite a few errors in the labs that confuse the heck out of a student. The labs need to be fixed else the material becomes useless.

교육 기관: Ravindra D

2020년 5월 11일

Course content does not give proper understanding of the different approaches. For the person who is not from mathematics background it is confusing.

교육 기관: Bhuvaneswari V

2019년 3월 9일

The statistics background needed for the course need to be better explained. or at least reference to related learning materials to be given

교육 기관: Russell K

2020년 4월 26일

Too many errors in the lab examples can be rather confusing.

Also, the Seaborn code was not working in IBM Watson Studio

교육 기관: Mariam H

2020년 5월 2일

Great course but some of the concepts are not explained very well. I got lost towards the end but overall i like it.

교육 기관: Andre L

2019년 3월 10일

Lot of information, but offered in a very choppy manner. Was hard to follow, will need to review many many times

교육 기관: Abdulaziz A

2020년 4월 11일

the course content is excellent but some Technical issues occurred in doing the lab exercises

교육 기관: Chau N N H

2020년 1월 29일

The lesson need more explanations on Polynomial Regression, Pipeline, Ridge Regression.

교육 기관: Fayja H

2021년 1월 19일

too much content all at once

교육 기관: Alex H

2019년 10월 4일

Begins relatively clear. The practice labs were coherent and straightforward.

Around Week 4, things started to get convoluted. Small things, things that you don't notice at first.

Week 5 was where it really started to fall apart. You could tell whoever made this course lost interest or just did not have the capacity to teach the information effectively.

A great example of the lack of understanding or knowledge of how Coursera works is something you can view yourself.

Week 6 is the Final Project

Week 7 is one statement about your certificate.

Usually in most courses, the final project will be in end of the final week. That week having multiple modules that you have to complete leading up to the final. It was worrying for me as I thought the approach to this was on accident, but it seems likely that it was just due to ignorance.

Just as well, the Final Project was botched, the software and questions were depreciated and even written wrong by the creator. And when you would upload your pictures in the end to show you had worked out the problem, one of the upload buttons was missing in lieu of the letter "Y"....

Y indeed. Y was the ending of this course so terrible? A little more investment in the people you are teaching would go a long way. Very disappointed.

교육 기관: Philip P

2021년 1월 9일

Course lacks thorough rigor or genuine assessment.

Labs are training on copy/paste and using the Shift+Enter command in the Jupyter notebook.

Assessments are multiple choice. No assessments on ability to write scripts to undertake data analysis to seek solutions.

교육 기관: Brandon S

2021년 1월 7일

Again, the use of the IBM cloud is a useless buffering of site traffic for your own products and does not provide anything for the course. Little to no 'challenge' questions that push the student to go beyond the hand held procedure of the labs.

교육 기관: Elvijs M

2020년 4월 18일

The course makes you aware of some Data Analysis techniques, but you learn very little. The explanations are very superficial. And since nearly all the code is are already there, you are not forced to think about the concepts and methods.

교육 기관: Utkarsh S

2020년 6월 25일

The course was quite good until Week 3 but after that it was poorly structured. A lot of concepts were randomly introduced without proper explanation in Week 4 and Week 5, thereby killing the fun of learning.