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Python을 사용한 데이터 분석(으)로 돌아가기

IBM 기술 네트워크의 Python을 사용한 데이터 분석 학습자 리뷰 및 피드백

4.7
별점
15,264개의 평가
2,303개의 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 2,309개 리뷰 중 2076~2100

교육 기관: Vimal O

2021년 11월 9일

On overall IBM data science professional certificate track: P​ros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

교육 기관: Carsten K

2020년 3월 11일

Great coverage of topic, but unfortunately comes with several imprecise (or even planely wrong) explanations in the videos. Video quality (style of presentation) is ok, but sometimes missing things are slightly missaligned or questions show up before the topic/sentence is finished - could use some polishing. The hands-on labs are great though - if the notebooks open or the servers are reachable.

교육 기관: Felix S

2019년 7월 1일

Material to learn data analysis was very good but had quite a few bugs. It was very annoying to review the assignment of a peer because it is not possible to zoom into the screenshot. Furthermore did I need to flag a person because he had copied screenshots and his notebook was empty or only with screenshots but I was still required to review a second person to complete the course.

교육 기관: Jackson V

2019년 6월 5일

Not as impressed with this course as the previous courses. My main complaints were:

-Seemed to be some gaps between the lectures and labs

-Some lectures seemed rushed through w/ simple questions, and did not prepare well for the lab

-Pre-written code in labs would produce errors

-Spelling mistakes (i.e. the week 5 "Quizz")

-No final project to conclude and summarize up our learning

교육 기관: Chioma J E

2019년 4월 9일

The course was not detailed enough. I think the instructor assumed that people taking the course would know a lot about Regression, Correlation and some other statistical functions, that it was hard to understand or follow at times. Maybe consider 'dumbing' down down the statistical functions so that newbies can also follow.

Overall interesting course. Thank you.

교육 기관: Kam S H

2021년 1월 23일

First 2 weeks were fine for beginners, but after week 3 where all new different syntax and concepts like seaborn, visualization, Regression models etc etc were thrown in, it got way too advanced for beginners especially when there insufficient and effective practices available to hone the knowledge. Have to spent most of the time self-learning on other websites.

교육 기관: Nikhil B

2019년 2월 25일

This is an excellent course for beginners in the data analysis and data science fields as it explains deep technical concepts in layman terms along with the Python code for the same. However, not a perfect course for someone wanting to go into conceptual depth or wanting to expand their knowledge of analysis in Python beyond use of standard packages.

교육 기관: Fares A G

2020년 3월 18일

Needs to rely less on the cognitive class platform, just host the ipynb files externally as the labs are inaccessible alot of the time. Course only covers regression models, I would've liked to see SVM, KNN and other algorithms. However the course excels in explaining the relevant maths related to regression and regression evaluation

교육 기관: Mbongeni N M

2018년 9월 9일

It was educational, but when you pass a quiz, there should be an option to get answers to the questions you got wrong. And the practice exercises were filled with mistakes, particularly week 5. And the instructor was not responding to students' questions for week 5, which was one of the most challenging weeks. That was annoying.

교육 기관: Yariv Z

2020년 5월 23일

A lot of un addresses subjects. Many mistakes both in the videos and in the labs.

Overall after viewing all the videos again and summarizing for my self everything, I felt a lot better with the material but I think the course is not organized. I also think that it should get into some mathematical subjects more thoroughly.

교육 기관: Brisa A

2019년 6월 28일

A lot of errors make the course confusing. Also, the assigments and labs are "too easy"... it is clearly shown in the videos that there is much more to be done, but the course only demands you do about 50% of what is taught. How are we supposed to really learn without practice?? Give us real and demanding projects!

교육 기관: Antonio P

2019년 3월 5일

The content was good, but there were numerous mistakes and inconsistencies (i.e. a chart would show a red line as a training set but the write-up would say the red line was a testing set). Also, I would have preferred to have shorter and more lab activities. The lab activities were too few and each was too long.

교육 기관: Vyacheslav I

2019년 11월 15일

Grammatical mistakes, low quality videos, low quality slides and videos. Labs are okay, though no in-depth clarifications and explanations are given. Like "to do this you write this". Options? Explanations? What for? It's too much. Just remember how we wrote these lines and copy-paste them in you code later.

교육 기관: Hemanth S

2020년 5월 4일

Course is a bit too short and way too fast paced for what it is trying to convey! Of course people will be able to complete the course without problems but, have to re-visit and brush knowledge on these a lot more. Anyways, it is a bit of confidence booster. You feel like you learnt a new course.

교육 기관: Rakshita S

2020년 7월 26일

The reason I am giving a three to this course because compared to rest it was a bit fast-paced. Also, I feel we need a prerequisite of statistics before starting this course which was not mentioned anywhere.

Guess it is time for a lot of practice. Wish there were more assignments as well.

교육 기관: Fernando M M E

2021년 10월 23일

I am doing this course as part of the IBM Data Analyst Certificate and even it was the 7th course I take I don't feel it was well explained. The videos pass very fast and the explanations are insufficient to understand what happen in the labs. I think there is place for improvement.

교육 기관: Sisir K

2019년 2월 15일

Highly technical and complex in nature. Difficult for people just starting out with data science. The hands-on labs are more useful than the videos themselves. The quizzes in between videos felt a bit too easy and mostly comprised of examples (as questions) in the videos themselves.

교육 기관: Jingyi Y

2022년 4월 16일

The final assignment is terrible. I've spent a long time setting up the environment because the online notebook is not available. And some questions are hard to find what they are really aimming for. And instruction is actually bad, at least compared to the course.

교육 기관: Raghav N

2018년 9월 14일

This course is definitely very helpful to people who are passionate about Data science and have basic to intermediate understanding of Python but this course can be much better if it includes coding assignments rather than quiz submission. It was a great experience.

교육 기관: Roberto B

2019년 7월 10일

I'm not convinced that this is a great way to learn, I just feel there needs to be a better way of learning this than the approach this course takes, I kind of learned the python commands but I'm not sure I understand how to apply them in the real world. We'll see

교육 기관: Toan L T

2018년 10월 23일

Decent videos on Data Analysis techniques.

But the labs are poorly constructed: typos, inconstant question and solution, un-commented code and under-explained lab result.

It's a shame since the labs in other courses in this series are very high-quality.

교육 기관: Raj K

2018년 7월 6일

It would be great course for beginner to have idea about different steps involve in data science job. I would recommend to go with this course. I just took 3 days to complete this course and you can do in 2 days also. Depending on your speed.

교육 기관: Damian D

2019년 2월 13일

There are some mistakes in the course (wrong transcryptions, missing cells in LAB).

The material is quite difficult and more explanation / exercises would be needed.

There is no assignment at the end of the course which I consider as minus.

교육 기관: Luciano P

2021년 5월 2일

Good topics, but video instructions not clear enought. I had to go search on Internet for the topics. Sorry.

Maybe they were too simplified for videos. The subjects needed more exploration.

Anyway, it was a good starting point.

교육 기관: Filipe S M G

2019년 8월 24일

Good introductory course on Data Analysus with Python. Since the course is short, the functions and concepts are explained very quickly. There are also many mistakes in the slides, notebooks and even in the final assignment.