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Python을 사용한 데이터 분석(으)로 돌아가기

IBM의 Python을 사용한 데이터 분석 학습자 리뷰 및 피드백

4.7
별점
12,988개의 평가
1,898개의 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 1,879개 리뷰 중 1326~1350

교육 기관: Jess M

2019년 2월 27일

Covers a lot of content very quickly with not enough opportunities to practice using and applying the code. Having lots of quizzes is good for testing passive knowledge, but more active hands-on application in labs would be most welcome. Useful content, but I am going to go take an intro to Python course so that I can actually follow and use what is presented here.

교육 기관: Sanjay R

2020년 4월 3일

The course videos were excellent! The final project did a good job in covering the course material. However, the support to the course was unacceptable. I never got a response to any of my questions after posting them twice and waiting for a day. I then just decided to submit my project without waiting for a response since I felt my wait will be in vain.

교육 기관: Jeremiah T

2020년 4월 16일

This is a well organized class and consistent with the rest of the course series so far. One improvement could be to reinforce the concepts more such that we can create our own projects and decide what we need to do. At this point we're just performing methods for the class, but I don't yet feel comfortable starting my own project using these methods.

교육 기관: Benyaphorn P

2020년 11월 9일

The overall modules were great. But a few comments, I think I am supposed to get more score for my final assignment. The reviewer did not grade me fairly, even though my answers were correct and matched the rubric. I do not seriously mind the issue. But to be honest, this is kinda annoying and your team should care about how to handle it.

교육 기관: Jaime A G P

2020년 2월 22일

Es un curso introductorio, realmente no es complejo, solo se trata de entender las bases del análisis de datos. Sí, es cierto que los videos y los laboratorios tiene algunos errores (que si has pagado por el curso no serían aceptables en ningún momento). Es básicamente una introducción para saber como se trabajan en el análisis de datos.

교육 기관: Eugene T B

2019년 9월 2일

Some of the course skates over pretty difficult information really quick and then gives you challenges that haven't really been that well explained, so some self-research is required. The assignments are also pretty copy/paste + modify a couple of variable names so you have to put in the effort to really get good value out of the course.

교육 기관: Anuradha B O

2018년 7월 14일

The course is very interesting and concise, it has a very logical flow. The best parts about the course are quiz embedded in the lectures and detailed lab assignments. However, there are few errors in the lab and assignments, which need to be rectified. Otherwise, it would have been 5star from me. Thank You for desiging this course.

교육 기관: Megan

2019년 12월 18일

Easy to understand and grasp for a beginner. Good refresher for those who have some basics of programming down. Typos in the reference codes here and there but no major problems. Other than that the Watson interface is alright to work with however there will be some lagging some times. I enjoyed the process of learning this course.

교육 기관: Ning C

2020년 5월 12일

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.

교육 기관: Mats B F

2020년 5월 1일

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.

교육 기관: José F M V

2020년 10월 7일

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.

교육 기관: Jeff L

2020년 6월 17일

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.

교육 기관: Roy v E

2020년 4월 23일

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.

교육 기관: Ranjeeta R

2020년 3월 25일

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!

교육 기관: Joseph O

2019년 3월 11일

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

교육 기관: sumit c

2019년 6월 6일

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.

교육 기관: Susan A

2020년 5월 6일

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.

교육 기관: John B

2019년 9월 9일

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.

교육 기관: Michael K

2019년 4월 28일

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.

교육 기관: Irving B

2018년 10월 11일

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.

교육 기관: yimingguo

2019년 3월 24일

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

교육 기관: Jurriaan A M

2021년 2월 11일

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!

교육 기관: Lauren J

2019년 5월 7일

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.

교육 기관: Nicole L

2020년 10월 4일

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.

교육 기관: Leandro P

2020년 10월 9일

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.