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Вернуться к Introduction to Data Science in Python

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

Оценки: 25,157
Рецензии: 5,616

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:

3951–3975 из 5,564 отзывов о курсе Introduction to Data Science in Python

автор: Jithesh K B

9 февр. 2020 г.

Very good course and I learned all the new stuff like pandas, numpy and hypothesis testing etc. Assignments were very good to concrete my understandings.

автор: Jose P

14 нояб. 2018 г.

Good introductory course, challenging to basic level and forcing to use new technics and think out of the box. Recommended for first contact with Python.

автор: John A M

24 июля 2017 г.

Maybe 3.5 stars, the material is good, but unless you are active in the forums (and others are as well), the assignments can be frustrating to complete.

автор: Michał K

3 июня 2017 г.

Very well prepared. 4 stars because it only scratches the surface of data wrangling with Pandas. I'd love to see more comprehensive course about Pandas.

автор: Raghav B

21 окт. 2020 г.

Some of the assignments were very hard compared to what what our instructor teaching,

Our instructor teached us well but the assignments very quite hard

автор: Rashmi k

29 апр. 2020 г.

I feel, lessons are very brief. Should have some better coverage of topics. Assignments are good to challange your mind. Overall it was good learning .

автор: Patrick L

13 мая 2018 г.

Very good introduction to data science with python (numpy, pandas).

Only negative aspect is that the assignment questions are sometimes not unambigious.

автор: Aritro S R

15 июля 2017 г.

The lectures can include anecdotes & more relevant examples to make it more interesting. The notebokks & assignments are very well designed. Thank you!

автор: 21PGDM-IB87

17 июля 2021 г.

Good course material, covered every topic in depth. A bit fast paced so need to give due time to each video lecture as they contain a lot of material.

автор: Jorge E

16 окт. 2020 г.

Buen curso introductorio con Pandas. Me sirvio para conocer la forma de limpieza de fuentes de datos y su union para luego hacer calculos estadisticos

автор: Ahmed A E

24 сент. 2020 г.

it was very exciting but so fast and requires a good background in Python. I think it is not suitable for people from for example C or C++ background.

автор: Shivam R

16 июля 2020 г.

I think the course Assignment is difficult and the course theory is not up to assignment level, I found difficulty in solving nearly every assignment.

автор: Joe P

19 февр. 2018 г.

Good overview of fundamentals. Could do with more information on last few parts that feature quite heavily in the assessment (e.g. statistical tests).

автор: Subbaiah M A

7 мая 2020 г.

Gave me an insight of how Data Cleansing, Eliminating Noises and running Statistical Analysis can be done and it gave me an exposure to Data Science.

автор: Vishal S

16 дек. 2017 г.

This course is much more useful for someone who wants to get a touch of data science but one should have some basics knowledge of python programming.

автор: Sandip K D

2 мая 2020 г.

Great Course. The assignments are challenging and this gives you an idea of how difficult it is to clean and perform calculations on real life data.

автор: Jun X

17 нояб. 2019 г.

The submission of last assignment is troubsome due to system design. Takes time to solve it. But it gave me a lesson how should I write robust codes

автор: eric g

9 янв. 2017 г.

Requires most projects to be completed without the videos help. Need to research and find answers elsewhere. Also need to have good sense of python.

автор: Yuhuan Z

30 янв. 2020 г.

Very challenging course. But you can really learn something by solving challenges, right? It depends on how much you want to learn from the course.

автор: Chris P

20 дек. 2018 г.

Very good course. This was probably a little too advanced for me but that's because I jumped too far ahead in my educational journey. Good content!

автор: Jeremy K

20 авг. 2017 г.

The videos should prepare newer Python learners better to complete the assignments. Moreover, some questions are very vague and unnecessarily long.

автор: Abhijit D

31 мая 2020 г.

The course gives me an overview of Data science and Data visualization. However more explanation on the statistical topics would be more helpful .

автор: Haldankar S N

3 мая 2020 г.

Overall this is good introductory course but the assignment questions should be more clear, as there are lots of doubts at each step of assignment

автор: Naveen K

17 янв. 2019 г.

Please improve the autograder .Its annoying to be not graded for correct answers. Otherwise the course is perfect. Loved it !! Thank you very much