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學生對 加州大学圣地亚哥分校 提供的 Basic Data Processing and Visualization 的評價和反饋

4.3
95 個評分
26 條評論

課程概述

This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization....

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1 - Basic Data Processing and Visualization 的 25 個評論(共 26 個)

創建者 Carl W

Apr 27, 2019

The course is easy to follow, well organized, and assumes very little background. It effectively demonstrates the power of Python in large data applications and provides insights and guidance on which tools are best used.

創建者 Xi L

Jan 10, 2020

I learned a good deal from the course. I am satisfied with the content of the course.

The problem I encountered with this course is on the grading of the final project. The format is by using peer-review. But you need to have 3-peers to review your submission. I submitted my 3 weeks ahead of the final deadline of submission but still it was not reviewed by 3 peers. So there was no score on my final project. That does not seem fair.

創建者 Clarence E Y

Aug 24, 2019

This course enables students to learn intermediate level skills in data wrangling, data exploration, and visualization. The final project requires selecting a topic of personal interest and constructing a complete project work flow. By doing this, areas of weakness in data wrangling, cleaning/QA, data exploration, and visualization may to uncovered and addressed. The result is to build greater skills and confidence.

創建者 Alex D

Jun 09, 2020

Great course to start with programming for business analytics.

創建者 Mohd Z A

Jun 30, 2019

Excellent to start your career in machine learning!!!

創建者 Monali p

Jun 12, 2020

Great one! Made me learn from scratch.

創建者 umair

Aug 24, 2019

Great course for an absolute beginner!

創建者 Zakir U S

Jun 24, 2019

Over all a great course for beginner

創建者 Tiago F

Nov 11, 2019

Very Good to start learning Python

創建者 Cambron T D

May 22, 2019

Great first class in this series.

創建者 Oriol P M

Aug 12, 2019

Excellent and interesting course

創建者 Hemanth C

Apr 17, 2020

Perfect Course

創建者 Carlos P Z V

Jun 29, 2020

Great!

創建者 Nguyen T

Jun 13, 2020

This course is pretty good. Both instructors explains concepts well and the Python demonstrations show that they use Python a lot in their everyday lives, but some of the lectures videos have a lot of repetition because the instructors misread a line or forgot to bring up a concept, so it slows the momentum and flow of the explanation. Was a retake of the video really difficult? There are also long periods of silence that can be rather weird, why was this not edited out? The rating should be a 3.5 stars out of 5 but there isn't 3.5 so I give it a 4 here.

創建者 J N B P

May 29, 2020

A really good course to learn data preprocessing before implementing the machine learning module.

創建者 Stan

Feb 28, 2020

Pretty easy to start with, especially with a background in CS.

創建者 Xuejie Z

Jan 25, 2020

nice basic python course

創建者 Sebastian S

Jun 22, 2019

The positives: I liked the design of the final project, and how users were encouraged to 'get out there' and find some interesting open source data sets. The lectures were well structured with good narratives and good examples.

The negatives: I would have liked a bit more focus on actual visualization libraries like matplotlib and maybe seaborn. When covering the data types (date, string, boolean etc.), it might be worth adding an extra week or so were these things are done with the help of the standard library pandas. I feel like this is what people will end up doing anyway bc there are so little alternatives in python to do processing, so a course on data processing should ideally cover that library.

創建者 Ioana B

Oct 11, 2019

The information learned in this course is very useful, for a beginner in data science. It is a very good introduction in working with python, extracting data-sets, defining features and plotting graphics.

What I didn't like at all is the engagement. Finishing the course was not satisfactory at all for me - even if I submitted my project on time, I didn't receive 3 reviews and I found the grading system very subjective. Knowing this, I would think twice about paying for this experience - what I learned can be found in free tutorials too, and only for the interaction with other users I don't think it is worth the price.

創建者 Luciano G D

May 08, 2020

I have to say this is a great course. I should rate like 5 stars. But the coursera way to assess the final projects is not correct. Your final score can't be decreased if you don't have any feedback about the reason. This is not a fair scoring system.

創建者 Jonas J T

Aug 23, 2019

Quick intro to data processing. More material on numpy and pandas would have been nice. Im still trying to figure out why the specialization mentions "Design Thinking". At least in this course...not a single design thinking concept was mentioned.

創建者 Kotronis A

Nov 30, 2019

very subjective assignments

創建者 Paul E J

Jul 03, 2019

This is not a Python introduction, but the authors approach it as if it were. Even the most basic data scientist will not calculate averages in the way described here. We'd use pandas or similar to get not just means, but other summary stats as well. For a Python course, I could understand doing it the way shown here. But not for data science.

創建者 Luiz V K M

Apr 04, 2020

it's not a intermediate level course, it's a really basic one

創建者 Davide C

Jun 18, 2019

The test scripts make no sense.