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Avis et commentaires pour l'étudiant pour Basic Data Processing and Visualization par Université de Californie à San Diego

4.4
61 notes
14 avis

À propos du cours

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 - 14 sur 14 Examens pour Basic Data Processing and Visualization

par 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.

par Zakir U S

Jun 24, 2019

Over all a great course for beginner

par Mohd Z A

Jun 30, 2019

Excellent to start your career in machine learning!!!

par Oriol P M

Aug 12, 2019

Excellent and interesting course

par Cambron T D

May 22, 2019

Great first class in this series.

par 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.

par umair

Aug 24, 2019

Great course for an absolute beginner!

par Tiago F

Nov 11, 2019

Very Good to start learning Python

par 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.

par 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.

par 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.

par Kotronis A

Nov 30, 2019

very subjective assignments

par Davide C

Jun 18, 2019

The test scripts make no sense.

par 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.