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學生對 IBM 提供的 使用 Python 进行数据分析 的評價和反饋

4.7
10,154 個評分
1,432 條評論

課程概述

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....

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RP

Apr 20, 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

May 06, 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.

篩選依據:

951 - 使用 Python 进行数据分析 的 975 個評論(共 1,420 個)

創建者 PALEM A

Mar 10, 2019

good

創建者 Mahesh H

Mar 09, 2019

GOOD

創建者 VISHNULAL M

Mar 09, 2019

nice

創建者 Subrahmanyam

Mar 05, 2019

good

創建者 girish j

Feb 03, 2019

good

創建者 Jimut B P

Sep 22, 2018

nice

創建者 Usman R M

Apr 27, 2019

top

創建者 YANAMADALA P

Apr 04, 2019

gud

創建者 Muhammad T A

Sep 16, 2019

<3

創建者 Juan P

Apr 07, 2019

g

創建者 Gaurav D

Apr 03, 2019

E

創建者 Burouj A

Feb 23, 2019

I had to remove 1 star just because of the fact that a project is not included in this one. Yes, you do have labs but there you are forced to write code in way so that you don't encounter problems later in the notebook.

In a project or an assignment work, you have to play with variables and confusions and errors out of wonderland show up which lead to greater clarification.

The course in itself is great and undoubtedly good in functioning as a prerequisite for Machine Learning and surely I'd recommend it to anyone who asks for an opinion. The explanations are good and much easier to understand along with the visual demonstrations.

I'd advise that after learning anything during this course, look for some database online and play with it yourself(I didn't but had to regret cuz I'd forget the code again and again).

OVERALL : GO AHEAD, it's worth it

創建者 Sid G

Jun 11, 2019

The final assignment for this course is frustrating because it uses Watson Studio instead of the learning environment we've used up to that point in the course. There is registration, learning curve, additional complexity. One of the final questions doesn't accept a file upload. One of the questions asks you to calculate a regplot which takes about 15-20 minutes to complete. Nothing tells you to expect that until I finally found a student comment in the forum. I was so frustrated at one point I was ready to abandon the course because I didn't know what was wrong and couldn't find any help. The issues with the final assignment need to be addressed, but I'm glad I decided to stay with it. The course presented a lot of good material. I recommend the course, but I hope they will address the rough edges.

創建者 Steven T

Oct 28, 2018

Overall a good introductory course. It features several interesting aspects of pandas, numpy and matplotlib. The focus lies on using statistical methods in Python, not on explaining statistics itself, bu a qualititative (short) explanation is given for all the items shown in the course.

A few things could definetly be improved:

Some parts of the videos or their accompanying notebooks have some errors and should be checked.

The course kinda suffers from only testing he students via quiz options. While the structure ad high frequency of the quizzes is to be commended and helps students stay on topic, there should be some final assignment hich consists of actual coding tasks. Not too difficult, but with some of the methods explained each week.

創建者 Piyush L

Jul 09, 2019

The course like every other course in the specialization is a little too fast for me. The videos are way too short (averaging around 5 minutes) and there is way too much stuff in the videos for you to be able to absorb properly. But, the good thing is you'll still learn a lot from this course. Things got really fast mid of 4th week onwards for me, like explaining ridge regression and very complex topics without any proper introduction has left me kind of clueless. But a course should be rated according to how much you really learned and despite of all of the things above that and some more, I still learned a lot from this course so 4 stars for that.

創建者 Nguyen D H A

Aug 03, 2019

A good starting point.

Some of the concepts could have been explained more clearly, I have decent mathematics understanding and sometimes still felt like I was hopping from this to that (regarding the codes). I understand that they're trying to teach many things in basic level, but total video time was about only 1 hour for the whole course... I wouldn't mind watching a little more or getting additional reading materials to get the context & familiarize myself with the codes (I do additional practices on my own so that's fine, but directed-study is always nice, and easier)

The labs were really helpful though, so I'd say go for it!

創建者 Muhammad S H

Mar 17, 2020

I think the course was good, but the complexity level of the labs was a bit high. I mean, the leap in skill level required could have been made more easier. There are many new functions utilized in the labs that we have not been made familiar with. So, a lot of documentation-perusal and sifting of other online resources was required, especially with the Polynomials and Ridge Regression, the last Lab. I think the contents of the last Lab (Model Evaluation & Refinement) should be elaborated on and explained with greater clarity, introducing new functions and code-parts along the way.

創建者 Arnold W E

Feb 22, 2020

One of the few good courses I have had. I learned a lot, used much of it in the labs. The lab for Week 5 was very confusing, as was the final one. The other labs were great, but Week 5 and the final were very disjointed and uneven. There were several things I had hoped they would put in the lab, but there was no first-to-last example lab, which is what I wanted. Without actual instructors (as in live training) this should be expected, and if I had paid for this I would be upset. Problems 7-10 on week 5 are garbage!

Still, one of the best on Coursera, from my limited point of view.

創建者 Imtiaj A C

Apr 18, 2020

Of course I've learnt a great deal about data analysis using python in this course. The course videos were made in a way that even the most difficult topics could one learn very easily. And after-module-labs were great to test the topics learnt.

One thing that stopped me from giving full 5-star was the final assignment. It was way too simple in my opinion. Most of the discussed topics weren't even there. I guess it would be much better to make the final assignment a little bit more thorough and to some extent, more difficult.

創建者 Everett T

Jun 29, 2019

The course is overall very helpful to learn Data Science with Python while it does require foundations for statistics for this module, so it appears difficult to understand some mathmetical concepts for beginners. Thus I suggest some more detail explainations/practices for core parts like model development.

Moreover, there are some mistakes/typos in labs, e.g. Week5's Model Evaluation and Refinement, though most of them are minor. Also some libaries are outdated (discovered thourgh warning outcomes), which may need updating.

創建者 Jonathan K

Mar 08, 2020

Good because provided breadth - teaching lots of different data analytics tools. The cons were that it didn't actually force you to code until the final product, and it also tried to do way too much in one course. I wish it just went more in depth into beginner topics like cross-tabs and linear regression, as opposed to trying to cover introductory stuff as well as beginning machine learning in one course - which caused the course to sacrifice depth - a deep understanding of any given topic.

創建者 Juan M L F

Jan 23, 2020

This course is good if you already have some experience in Python and its structures, or if you have some knowledge in programming. You will learn some basic data manipulation and exploration techniques and also start with some of the model evaluation metrics in order to assess the (regression) models created. Overall good experience. If you already have some knowledge of Python SciKit Learn and Pandas, you could easily cram this course in 2 days (all-in) without too much sweat.

創建者 Sk. T R

Apr 03, 2019

It has been a fantastic experience to have gone through this course materials. Although I found the lecture videos quite quick to the extent that we fail to understand the concept well. But while going through the labs carefully, I was able to get the concepts right. So only because the lab part was well organized, the course was helpful to me. But had it been the lectures alone, then it would have been difficult to grasp all the concepts clearly.

創建者 DI C

Jul 07, 2018

Great course! More hands on and practice, a bit lack of theories, compared with Andrew Ng's ML course. And there are a few typos or mismatch in the course materials that need more attention. However, I especially like the fact the example, i.e. predicting car price, has been revisit and further developed through the 5-week course. Just finished round 1, guess I need to go over it again (maybe again) to grasp more details. Recommend the course!

創建者 Jianxu S

Sep 07, 2019

Overall the course is well written. There are a few typos including in the instructions for final assignment. I feel that a summary is missing for the overall data analysis process and methods. This course is the longest in the series so it takes a lot of effort to get through. I did not have much Python background so it was a bit challenging at the beginning but the material was very helpful in bringing me up to speed.