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Kursteilnehmer-Bewertung und -Feedback für Basic Data Processing and Visualization von University of California San Diego

103 Bewertungen
29 Bewertungen

Über den Kurs

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....



Aug 03, 2020

It was a good Data Visualization course. I really liked it. It's a good beginner course to start with Data Visualization.


Jun 09, 2020

Great course to start with programming for business analytics.

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1 - 25 von 29 Bewertungen für Basic Data Processing and Visualization

von 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.

von 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.

von 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.

von Yuvraj J

Aug 03, 2020

It was a good Data Visualization course. I really liked it. It's a good beginner course to start with Data Visualization.

von Oleksandr D

Jun 09, 2020

Great course to start with programming for business analytics.

von Mohd Z A

Jun 30, 2019

Excellent to start your career in machine learning!!!

von Monali p

Jun 12, 2020

Great one! Made me learn from scratch.

von umair

Aug 24, 2019

Great course for an absolute beginner!

von Zakir U S

Jun 24, 2019

Over all a great course for beginner

von Tiago F

Nov 11, 2019

Very Good to start learning Python

von Cambron T D

May 22, 2019

Great first class in this series.

von Oriol P M

Aug 12, 2019

Excellent and interesting course

von Hemanth C

Apr 17, 2020

Perfect Course


Jul 30, 2020


von Carlos P Z V

Jun 29, 2020


von 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.

von J N B P

May 29, 2020

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

von Stan

Feb 28, 2020

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

von Xuejie Z

Jan 25, 2020

nice basic python course

von João F

Jul 28, 2020

Good course.

von 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.

von 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.

von 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.

von 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.

von Kotronis A

Nov 30, 2019

very subjective assignments