Chevron Left
Retour à Python Data Analysis

Avis et commentaires pour l'étudiant pour Python Data Analysis par Université de Rice

4.7
343 notes
59 avis

À propos du cours

This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We'll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python's support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs. These concepts and skills will help you to further extend your Python programming knowledge and allow you to process more complex data. By the end of the course, you will be comfortable working with tabular data in Python. This will extend your Python programming expertise, enabling you to write a wider range of scripts using Python. This course uses Python 3. While most Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This course uses basic desktop Python development environments, allowing you to run Python programs directly on your computer....

Meilleurs avis

DP

Jul 07, 2018

The instructors use very clear language and explains in a details level that is possible to everyone that has a minimum of computing science knowledge to learn Python.

J

Jan 30, 2019

Important concepts covered - including dictionaries and data structures. Useful in developing a basic foundation and understanding of Python.

Filtrer par :

51 - 58 sur 58 Examens pour Python Data Analysis

par Ankit J

Jun 24, 2019

this is quite difficult and very hard than the last one

par Dev N P

Sep 04, 2019

This course is great one to provide you the fundamental concepts of python scripting.

par Mads J K H

Mar 28, 2018

Could use a bit more explanation on some parts since visualization of e.g. dictionaries can be a bit tedious.

Otherwise good.

par Andrew M

Feb 02, 2018

I feel like this course needs a lot of polish. The practice assignments in particular are riddled with horrible grammatical errors that make it hard to figure out what is being asked of you, especially when the author seems to be trying to be overly concise in their explanations. There's also too much reliance of documentation research. It felt like every assignment just told me to read page after page of documentation and figure out what parts were relevant on my own. Documentation research is obviously an important skill, but telling a student to learn an entirely new skill like color mapping based purely on documentation, and not telling them what parts of the documentation to focus on verges on cruel. I've never felt as frustrated with an assignment as I did with several in this course. The way the assignments were designed also made them very hard to test. The disconnect between the practice assignments and the graded assignments also caused a lot of issues with regards to flow. It feels like two courses were smashed together, one of which was fairly well built, the other of which was very much not.

My comments mostly pertain to the practice assignments. I thought the graded assignments were pretty good.

par Oleh M

Jun 22, 2019

A very sharp combination of the basics on lectures and algorithms in practice.

par Moustafa E E M M N

Dec 13, 2018

the assignments are not clearly illustrated to us. it is very difficult to understand what is required to be done . even it you code it, it is still hard to understand what is to be done.

par Eduardo P

May 25, 2018

Final project is way more difficult than what they explain in the course.

Final project is not only difficult and long but also due to the amount of extra files and lack of explanation on how to start is very frustrating.

Some concepts like "list comprenhentions" are used by instructors in the course, but not explained and instructor just adviced to go to Python.org and learn it there.

par Kristoffer H

Mar 30, 2018

Focuses on Python dictionary skills and not Pandas dataframes.