Chevron Left
Вернуться к Basic Data Processing and Visualization

Отзывы учащихся о курсе Basic Data Processing and Visualization от партнера Калифорнийский университет в Сан-Диего

Оценки: 168
Рецензии: 48

О курсе

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....

Лучшие рецензии

16 сент. 2020 г.

This course is more rewarding than I thought. The instructors give step by step explanation of the process also the syllabus of the course is just perfect, Highly recommended.

19 нояб. 2020 г.

Goes into great detail on ways to actually use the code in sophisticated and useful ways. I feel like this course has started me on building a great python toolkit.

Фильтр по:

26–48 из 48 отзывов о курсе Basic Data Processing and Visualization

автор: Carlos P Z V

29 июня 2020 г.


автор: Nguyen T

13 июня 2020 г.

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.

автор: Apoorv G

17 янв. 2021 г.

If someone wants to make their carrier in Data science,It is one fundamental course towards it.

The course is good with engaging assignments,quizzes and projects.

автор: J N B P

29 мая 2020 г.

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

автор: Lasal J

4 мар. 2021 г.

I wish the lectures are a bit more engaging. But content-wise it is good.

автор: Stan

28 февр. 2020 г.

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

автор: AKASH S

20 мая 2021 г.

topics were clearly explained ...

автор: Xuejie Z

24 янв. 2020 г.

nice basic python course

автор: João F

28 июля 2020 г.

Good course.

автор: Sebastian S

22 июня 2019 г.

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.

автор: Ioana B

11 окт. 2019 г.

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.

автор: Luciano G D

8 мая 2020 г.

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.

автор: Jonas J T

22 авг. 2019 г.

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.

автор: Martin L

22 сент. 2020 г.

This course overall is good but it really doesn't use the latest data manipulating library (Pandas). That needs to be added as that is what almost every Data Scientist in my company uses.

автор: Lam C V D

23 авг. 2020 г.

Several important Data Science library like Pandas are not taught at all, codes are written in long winded matter when there are better coding ways to do

автор: Kotronis A

30 нояб. 2019 г.

very subjective assignments

автор: Olivia Z

3 нояб. 2020 г.

The notes are not very clear and no body is answering the students' questions.

автор: Luiz V K M

4 апр. 2020 г.

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

автор: Davide C

18 июня 2019 г.

The test scripts make no sense.

автор: Zihaohan S

27 сент. 2021 г.

n​ot very user-friendly. why they cannot link the data set directly? I find asking users to download the files are annoying and totally unnecessary.

автор: Lucas O

15 мая 2020 г.

No clear instruction on how to download datasets. If we can't download the dataset, how are we suppose to proceed with the class.

автор: Matthias K

14 сент. 2020 г.

I need to unenroll as I did not apply FA