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Отзывы учащихся о курсе Introduction to Data Science in Python от партнера Мичиганский университет

Оценки: 25,173
Рецензии: 5,620

О курсе

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python....

Лучшие рецензии

9 мая 2020 г.

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans

28 сент. 2021 г.

This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.

Фильтр по:

3701–3725 из 5,566 отзывов о курсе Introduction to Data Science in Python

автор: Isabel O

18 янв. 2018 г.

Good explanations, well structured. However, I wish the weekly content would have prepared better for the assignments. If I have to add another 3h per week to find the right advice on stack overflow, that must be stated somewhere so I can plan ahead.

автор: Carlos A R R

5 июля 2017 г.

Very good course with a lot of material and challenging assignments. Gave it only 4 stars because in some assignments is more difficult to agree with the autograder than to get the correct answer (e.g., data type mismatch between float and float64).

автор: Shailesh K

9 сент. 2021 г.

​Excellent explanatory videos and lot is covered in just four week of course but it definitely need good grasp of pandas and numpy libraries for passing the assignnments. I definitely recommend this course as it will push you to learn and do more.

автор: Abhishek R

16 мар. 2018 г.

The content is really not good for novices. But the challenges I faced during assessments, did a lot of help. I can now understand the topics much better and at the least I am able to plan to clean the data and work on it much better than earlier.

автор: Nitesh R S

21 мар. 2017 г.

The course content is great. However, basic knowledge of python programming language is required. Since I didn't know python coding rules, I really struggled for a while. Maybe, basics of python in additional resources will help a lot of learners.

автор: Ahmad S

21 окт. 2018 г.

In my view, It is recommended to study a book on Python data analysis tool-kit panda and numpy. The course video quality is very good, instructor voice is clear and load. I highly recommended to take this course who wants to be a Data Scientist.

автор: VINEETH S C

4 авг. 2018 г.

This course would be recommended for anyone who has got good python skills already and want to do data analysis using python at a quick pace. Overall, It is fun doing the assignments and thinking more in a pandas way rather than in pythonic way.

автор: vishal r

5 мая 2020 г.

The projects were extremely helpful to learn various concepts . The videos could have been a little more descriptive to equip us with a little more knowledge so as to tackle the projects with a little ease .apart from that it was really great

автор: Lee A

15 янв. 2019 г.

Class was good, and the information was presented in a way that was straightforward to understand and apply. I wish the exercise autograder provided more feedback. Sometimes the lack of actionable information made it difficult to solve problems.

автор: wondertweet

16 апр. 2018 г.

The course is a good introduction for data science and helps me learn pandas and numpy. You may not learn how to use these tools only form this online lesson, but you can be aware of what you need to know if you would like to work on this field.

автор: MONIKA C

28 дек. 2019 г.

Good, comprehensive course. At the end you are ready to get and clean data and make some simple analysis on your own. But you need a lot of effort to do programming assignments. Save double amount of time than it is estimated for this course.

автор: Xiaojie Z

26 нояб. 2018 г.

This course provides good material. The assignments could be very difficult because the instructions were not clear. In real life, you would have a chance to clarify while it is difficult to do so in coursera.

Overall, it is a good course.

автор: Darren S

21 нояб. 2016 г.

The course was great - the lectures were clear and simple; I learned a great deal! However, instructions for the assessments could have been clearer, and there were a few issues with the autograder (though I'm sure those will be weeded out).

автор: Richard B

21 авг. 2018 г.

Generally good - with challenging assignments, explanation could be better and grading unit tests more flexible.

Some of the content is rushed - but it is comprehensive and you need to have some python programming behind you before you start.

автор: Remo L

2 мар. 2019 г.

it's a good, but challenging course as Introduction to Data Science. It will require the student to read pandas documentation and/or search for help on Stack Overflow. If you're stuck, there is usually good help on the course forums though.

автор: Qian H

25 июня 2017 г.

Nice course but the assignment is too hard for the beginner, and the assignment and the course materials are not match enough. We have to do a lot self-study when doing the homework. But the skills I learnt from the course is really useful.

автор: Matt M C

3 мая 2017 г.

This course worked better as a guide than it did as a course. I learned very little from the lectures and had to do most of my learning on my own. One of the assignment even explicitly told students they would have to go learn on their own.

автор: Jeffrey L

2 сент. 2018 г.

Pretty descent course, however it felt like most of the course consisted of the assignments and self-learning rather than instruction. That's probably ok given the content was more oriented around tools and less around concepts and ideas.

автор: grant

15 сент. 2019 г.

most of this course is really great. but I do think the assignment doesn't match the course very well. To finish the programming assignment, I have to go through a lot of materials and documentations. I hope the teacher could cover more.

автор: Muhammad M A

17 сент. 2020 г.

Excellent course to get you started with dealing with data. You'll learn a lot about Python, Numpy, and Pandas, and the best part is that you'll get your hands dirty dealing with real-world data and you'll also solve real-world problems

автор: Jhoan S G S

28 июня 2020 г.

Muy buen curso, sin embargo deberían actualizar la versión de pandas y demás librerías en los libretas y assigments.

Very good course, however they should update the version of pandas and other libraries in the notebooks and assigments.

автор: Cheng H Z

21 сент. 2019 г.

Good contents, but very poor grading system. Had to guess what was expected. For instance, get_list_of_university_towns (week 4 assignment), I set the indexes on both columns, which led to failure. After I removed the index, it passed.

автор: Trevor A

5 июня 2017 г.

Great introductory course on data science in python. However, the final projects diverts significantly from the course material, requiring vast amounts of self-study for someone new to Python. Definitely learned a lot in this course.

автор: Michal M

19 дек. 2016 г.

This was a good course and quite challenging. It requires a lot of self learning if you are not familiar with pandas. I wish some of the technics that were required to solve the assignments were better explained in the course material.

автор: Philipp H

1 мар. 2017 г.

This python is mostly an introduction to the Python Pandas API. It teaches everything by example, but at very fast pace, and the videos are really just preparations for the assignments, which can easily take more time than specified.