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

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Оценки: 25,648
Рецензии: 5,715

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

YY

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.

PK

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

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3651–3675 из 5,665 отзывов о курсе Introduction to Data Science in Python

автор: Darshil P

26 июня 2020 г.

Got to learn new concepts of pandas and numpy libraries. The course is very informative, and provides a good amount of basic knowledge, through which one can proceed in the field of data science. Things I didn't understand well, were the ttest_ind library and the matplot library. The lectures should be improved on these concepts.

автор: Priyadarshini D

9 авг. 2020 г.

Videos could include more information. Instructions in assignment 4 were not sufficient. Had a lot of difficulties in assignment 4 as I could not understand the requirements in question correctly. Questions could have been more detailed and stepwise.

Links to some datafiles have problems. Could not easily download some datasets.

автор: Nichole E

5 мая 2020 г.

Great course for a Python beginner or someone who just wants a refresher! The video lectures were pointed but not too in depth, allowing the learner to explore a bit within the assignments to get even more information from the interwebs, which is what just about every programmer does anyway when they forget syntax or something.

автор: andrea a

10 сент. 2018 г.

Interesting course, learn new approaches in using Pandas libraries. One remark, the evaluation of the assignments takes too long, for the last assignment all the answer took 2 minutes to run on my personal laptop while it took 15 minutes when submitting it. I spent a lot of time just waiting for the assignment to be evaluated.

автор: jeffrey m

5 февр. 2017 г.

Very interesting subject and data science skills are definitely currently in demand. The only thing that bugged me was that the grader didn't give much feedback on anything you did wrong. On the flip side the mentors did a GREAT job (kudos to you guys). The discussion forums were vital and they were really well moderated.

автор: Frederick Z

12 нояб. 2017 г.

content is good, and exercise is very relevant for learning. only problem is the exercise is very hard and cannot be finished only with content covered in the lecture, you need to do extensive self learning and take a lot of time outside the classroom. for those without python experience before it is a steep learning curve.

автор: Heriberto E

3 нояб. 2020 г.

Update your Pandas library and the explanations for all projects. It is very frustrating because you have to deal with both: 1) lack of clarity in the text and 2) missing features for the library we are using. Don't get me wrong... I learned a lot, but it kinda feels not enough when compared to the unnecessary frustration.

автор: Leidy D

6 авг. 2018 г.

I found the material of the class very useful for someone that has no experience using Python in data science topics. However, I feel some topics are given too fast. I also found some issues with the autograder that do not have any implication with the goals of the class but that are important in order to get the grade.

автор: Peter L

8 июля 2018 г.

The class is interesting, but I don't feel like the lectures were good preparation for the assignments. I understand that it takes some reading or study beyond the lectures, but there weren't any other materials provided. The Pandas documentation is big and it's not helpful to just go read through all of the docs blindly.

автор: Marc M M

28 нояб. 2017 г.

course is very good to learn the python basic pandas, numpy libraries.

I would prefer to have a reference book associated to the course so we reference to it, it will be great for future while coming back and read what we learned.

The grading system consumed most of my time to make sure to have my answer graded correctly.

автор: Pineapple P

21 авг. 2018 г.

It's a great course and during the process I learned a lot about python pandas applied to data processing. The only downside I see is that the lectures does not prepare you well enough for the homework at the end of each week. I had to spend tons of time googling about pandas in order to finish the homework each week.

автор: Wallace T

7 авг. 2018 г.

The auto-grader is sometimes frustrating. I know the course staff is providing the code to let learners to find the errors, but it's in the forum and I only know it after several rounds of frustrations.

Please mark those checking codes in the question too! It helps provide a happy learning experience to every learner!

автор: Vitor C

4 нояб. 2017 г.

Course is well structured. Most of the concepts are given quite quickly in the video lessons which is good since each learn is free to decide whether she/he wants to do further research in a specific content. Assignments are quite difficult and consume more time then estimated, but you actually get to learn the thing.

автор: 宸宇 田

30 янв. 2018 г.

this is my first course in Cousera, and i am a novice in Python. I think this course is really impressive. I learned how to use Python to process data quickly. I really recommend this course if you are interested in Python and Data Science. I decide to learn the machine learning course next, I love doc.Christopher!

автор: Sathya K

24 авг. 2017 г.

I like this course very much. I would rename the topic like data manipulation using python. It is definitely not just an introduction program to Data science. The questions are very challenging especially from week 3. But, I am very confident on taking any problems for data science to manipulate it successfully.

автор: Venkatesan K

14 дек. 2019 г.

The assignments were really interesting and challenging. But the lectures could have explained the syntax a bit more clearer. I was forced to look at the documentation for every single line of code during the lecture. The pace should be reduced. Otherwise its really a good course and you can really learn a lot.

автор: Rohan C

22 апр. 2022 г.

In the begining, there were practice questions at the end of each video. As I progressed, I obseved that only assignments are there. I would request you to provide more questions so that we could better insights and get more hands-on experience. It will help us to build strong problem-solving skills.

Thank You.

автор: Joan P M

17 янв. 2022 г.

B​uen curso para acabar de apuntalar los conocimientos en Pandas y lectura de datos. Hace falta un conocimiento previo de dicha librería. La introducción de Regex es de vital importancia para este curso. No le doy 5 estrellas porque hay muchos momentos donde se tira demasiado de Google para acabar las tareas.

автор: Harsh J

18 апр. 2020 г.

Great course. Well explained, and the assignments challenge and push you to explore by yourself. Sometimes however the system that reviews your assignment can be frustrating as it expects the output of your answers in a particular format/way. I guess but that forces you to modify your code in that manner to.

автор: J

27 апр. 2017 г.

Content was generally well covered. One improvement could have been slightly better examples of how there are multiple different ways to solve the same problems; the instructor would often allude to this but would just advise the student to read through the pandas docs, which could be non-intuitive at times.

автор: MARCO S M H

9 янв. 2021 г.

El ejercicio de programación de la semana 4 de la sección 1, donde se hace trabajar con datos de estados, ciudades y equipos de deportes, me parece poco útil, demasiado manual y muy lento. Considero que es un muy mal ejercicio. Todos los demás estuvieron muy acorde a los problemas y la materia que se enseña.

автор: Haining Z

16 апр. 2020 г.

I learned a lot, but the assignments really could have been explain better. It was not clear to me what type of format/output I should give at multiple times. What I did was keep submitting while adjusting the output until it was right. A hypothetical screenshot showing what the result looks like would help.

автор: Joshua Z

29 мая 2017 г.

Good course; a great intro to relevant concepts with guidance towards realistic tasks. Only negative is that sometimes problems are unclear (maybe its just me) and spent too much time trying to get outputs exactly right to pass auto-grader. Good experience, and I plan on continuing with the specialization.

автор: Monika D

12 июня 2020 г.

I liked assignments they actually helped me to learn.Also book - Python for Data Analysis , 2nd Edition - Wes McKinney was great learning. I would just like to add in that if the videos can have better course material for Week3 and Week4 as assignments were bit higher level than what was included in videos.

автор: Harsh B

28 сент. 2019 г.

This course was nicely curated. I learned about the Python lambda functions,dates ant time,objects and map() in week 1.In week 2 and 3 I learned about pandas.In week 4 I learned about the distribution and Hypothesis testing in Python.In video quizzes cleared the doubts regarding fundamentals of the topic.