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.
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
автор: Carla F•
18 июня 2020 г.
Um curso intenso e bastante prazeroso. Gostei de todas as etapas, os videos funcionam bem e estão construidos numa base introdutória, mas o desafio é pesquisar e pesquisar. Muito interessante mesmo!
автор: Pravesh G•
2 мар. 2020 г.
the course is designed very well. It covers data manipulation topics very well. It has excellent assignments which help in understanding the course concepts more better
автор: Ofir R•
25 июля 2019 г.
Frankly, I did not watch the lessons at all, although they seem good.
The assignments were really great !
Challenging and very rewarding.
Really recommend the course !
автор: Mengru Z•
15 мар. 2021 г.
Very interesting course to guide you through from the basics of pandas. Teaching staff is of great help throughout the learning process, with speedy replies.
автор: Pavan A•
28 сент. 2020 г.
Great course that teaches about how to process data in Python. The lectures are very code-based and the programming assignments help you learn new methods.
автор: Krishna M•
12 мая 2019 г.
Excellent course with assignments, But some elaborated videos on topics could help much better in solving the assignments in time.
20 апр. 2021 г.
Prof Brooks, Yusuf and Jimi have worked out engaging but also demanding applied course on the subject. Thank you very much
автор: Teck F•
24 февр. 2021 г.
Discussion forums are very useful. Exam questions are challenging for beginners but doable. Good teaching.
автор: Daniel F L V•
5 мар. 2021 г.
Excellent course! The professor is super clear and the content was really well organized!
автор: Li Y•
10 мар. 2020 г.
Very helpful and practical course, great intro to data science.
автор: Hảo T K•
5 нояб. 2020 г.
This is the only course worth in the specialization
автор: Sumit K B•
5 мар. 2019 г.
Great course to bulild strong base on Pandas.
автор: Mohammad T N•
21 сент. 2020 г.
автор: John R•
13 авг. 2018 г.
It took me a while, but I finally figured out the problem with this class. The lectures provide some good information, but only rarely do they go into WHY a particular action/method/approach is used or WHY it will be important later. I had to do my own deep dives into available documentation to figure out how most of the functionality covered in lectures really works. This is not necessarily a flaw in the class, but it does mean the suggested time commitment for each week of class is significantly underestimated.
The assignments, while interesting, have the same issue as the lectures. Most of the time is spent using of Google to look up Pandas and Numpy functions or methods, or if we really get stuck, to see if someone on StackOverflow already addressed any questions you might have.
Put simply, the only different between this class and learning from a book is the class sets deadlines for the students to meet in the form of graded assignments.
Of course, the setting of deadlines is an excellent way to stimulate learning, and this is why I will continue on with the Data Science specialization.
автор: Stephen L•
17 июня 2020 г.
The course will teach you basics of the Pandas library, which is an essential skill. It also gets involved with some issues related to data cleaning, which is also essential, but felt a little like
There is very little peer-to-peer learning because there are no practice sets that peers can talk over, only assignments which Coursera's Honor Code naturally prohibits discussing. Hence, the learner never sees optimized code for solving real-world problems. I'm pretty sure I would have learned more if this course had provided more practice problems for learner discussions. For example, very inefficient iterations can be used to solved problems that should be solved in better ways with Pandas. I know that sometimes I was doing it right, but I think sometimes I wasn't and it would have been nice to see better code.
30 янв. 2022 г.
+The assignments are quite challenging and test learning properly. (took me 12+hrs to finish assignments for week3/4)
+The videos were informative.
-A lot of very common functions, methods and constructs aren't given in the video and there is no supplementary reading material. You have to rely on other webistes/stackoverflow for a lot of your learning.
-A few parts in the videos felt rushed, many useful constructs skipped in some topics.
-Explanations of some fundamental concepts was missing.
OVERALL IMPRESSION : Take this course if you are willing to do a lot of self-learning outside of the course and already have some degree of proficency with python.
автор: Marika C•
31 дек. 2020 г.
This course was really helpful for me to gain some knowledge and strengthen the old one, especially I liked the recommended book. The lecturer touched the absolutely useful topics and made me to read and practice beside this course. But, I'll be honest, assignments were really hard for me, maybe because of lack of my experience in Python, but, anyway, I had to search on Google and sometimes find answers of questions, analysis it and than write them on my own.
My recommendation would be to make the course more comprehensive and thorough.
автор: Nattawat B•
2 апр. 2019 г.
This course is very tough. For those who have just learned how to code python will take up to 8 hours for each assignment. The auto-grader required an exactly solution for the answer and sometimes the answer is corrected but you it give you wrong and you have no idea why it is wring just because the type of return value are different!
Apart from those things, you will learned and accomplished alot from this course.
автор: James C•
24 нояб. 2020 г.
More challenging material than the introductory courses. Requires reading in the text book, reviewing the exercises in parallel in Jupyter Labs, and reviewing the lectures. The assignments occasionally had ambiguity which cost some time in solution. In general moved my understanding of Pandas greatly forward. Will start using Pandas in work as opposed to Excel.
автор: Deleted A•
29 нояб. 2020 г.
Assignment 4 is worded so badly and the code that was placed by the course author is so misleading (the return function the course author wrote in themselves returns two numbers only for the hidden test to come back and tell you need just one). That it is the reason I am knocking a star of its course.
автор: Low Y•
5 сент. 2020 г.
Very helpful for beginner in Python to build up a solid understanding and practical experience in pandas and NumPy library for querying, merging, grouping, and aggregating data frame.
However, the old version of python library in the auto-grader brings some difficulties for grading assignments.
автор: Shushant G•
28 дек. 2020 г.
The course is Excellent for new learners to start in the field of data science.
The Only reason I'm not giving this course a 5-star rating because it's a bit fast course. I mean from one week to another, things change a bit too much. Although best would be for me to give it a 4.5 star...
автор: Willber d S N•
21 мар. 2019 г.
Great Course!! You learn alot about Python for data analytics. It is very hard for someone that is beginning to programming. But there are a lot of recourses on internet that can help you. I recomend this course for all that need learning data manipulation with python.
автор: Waseem A•
5 мая 2019 г.
The course is good but it gets challenging in doing assignments since you have to a lot of learning at your own , video lectures cover a limited domain of weekly projects. over all this course will help you learn new stuff.
автор: YASH R L•
4 дек. 2020 г.
Tag for this course should change Intermediate to Advanced level. Course is pretty good with challenging assignments but prerequisite, define in course are not match appropriate. Please, make changes above mention.