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Voltar para Understanding and Visualizing Data with Python

Comentários e feedback de alunos de Understanding and Visualizing Data with Python da instituição Universidade de Michigan

4.7
estrelas
2,292 classificações
484 avaliações

Sobre o curso

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling. At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera....

Melhores avaliações

AT

21 de mai de 2020

Excellent course materials, especially the videos, with content that is thoughtfully composed and carefully edited. Very good python training, great instructors, and overall great learning experience.

VV

2 de ago de 2020

Great course to learn the basics! The supplementary material in Jupyter notebooks is extremely valuable. Really appreciate the PhD students who took the time to explain even the simplest of codes :)

Filtrar por:

351 — 375 de 486 Avaliações para o Understanding and Visualizing Data with Python

por KAYDAN P R

30 de jun de 2020

awssem

por J. B

15 de mar de 2022

Nice.

por Frank S Y R

17 de jan de 2019

Nice!

por Hugo S A

24 de mai de 2021

fun!

por Durga S

16 de abr de 2021

Good

por Chang L

31 de ago de 2020

good

por GUNDA S K G

4 de mar de 2020

good

por ATHIPATLA S N

25 de fev de 2020

nice

por BODIREDDI S

23 de fev de 2020

nice

por PUPPALA B A

21 de fev de 2020

GOOD

por PEDASINGU T K

24 de fev de 2020

gud

por Debasis D

12 de mai de 2021

.

por Jerrold

7 de out de 2020

There are two main fields of study in this course which forms the foundation for the specialization: statistical theory, and programming with python data analysis packages. I learned so much about statistics and visualization that would have taken months to learn in university, I gained a lot of experience and knowledge from this course. I have a decent background in Jupyter notebook from university yet I still learned many new things and got an excellent chance to practice programming in the python packages. The course offered excellent optional practices and gave us several extremely insightful and educational analysis reports done in JN that were related to the module of the week for us to download.

I recommend you have a datacamp subscription to have access to some extra notes regarding programming in the packages particularly Pandas to get the most out of this course by attempting all the optional programming practices.

por Luis D R T

26 de out de 2019

I loved several things, first that gives you an overview, useful, clear and fun of several basic statistical concepts such as measures of central tendency, different forms of graphic representation, and one of the most important at least for me (already that neither in school nor I would have ever thought about) the types of sampling that exist, because in school there is usually something called simple random sampling and we develop statistical techniques for it, almost completely ignoring the other types of sampling that are really common in real life and that when we face them we don't panic, I know that this is an easy level and I appreciate that in some way, but I would have expected a more difficult course that would have made the concepts really stay in me because I would be thinking about them continuously and how to apply them to the tasks that are presented week by week

por Duy B B

24 de mai de 2021

I am happy to share that I have just finished the course Understanding and Visualizing Data with Python from the University of Michigan.It is a great and useful course for my career as a Data Analyst. Specifically, through the course, we can explore some interesting topics like where data come from, study design, data management, and exploring and visualizing data. Besides, the lecturers also have a python lab exercise that we can practice coding after the theory session. Most of the data set is from NHANES (National Health and Nutrition Exam Survey), therefore the statistical results are quite clear and understandable.P/S: I am free to share more about this course and another course that I have finished. Please do not hesitate to connect me. Have a nice day !!!!

por Matteo L

4 de abr de 2020

I think the content here is great and gives you a good overview for understanding and visualizing data without getting into the mathematics. Week 4 is absolutely great in terms of how the information is conveyed by Mr. West who is an excellent teacher in my opinion. I do think, however, that the quizzes and notebook assignments could be a little bit more challenging and I would have loved to have answers to the "more practice" notebooks. I think it would have been great for those notebooks to have been part of the assignments, adding to the difficulty of the course.

por Iver B

13 de jan de 2019

Good introduction to basic statistical methods with an emphasis on working with surveys, and a good introduction to basic statistical techniques with core Python, numpy, matplotlib, seaborn and statsmodels. Instructors and presentations are excellent, very clear. I would give it five stars if it were more interactive, i.e. with more in-video quizzes, and practice quizzes between videos. Also, I wish I had take this course before I did the Applied Data Science with Python specialization, also on Coursera, but, alas, it wasn't available then.

por Eric W

27 de fev de 2021

The course in general is great for overview of basic statistics and how to display the descriptive part using charts in Python. The latter part is something new I learnt from this course. However, I rated four because there are still rooms for improvement. First, week-3 course (probability vs non-probability sampling etc.) is a bit too long and repetitive. Second, while the peer-graded assessment is interesting, I don't like to wait for my work to be reviewed, hence delaying the overall completion of my participation.

por steven h

28 de abr de 2020

The course could be improved with more quizzes to apply what lectures cover. A lot of useful information is presented, but there was not enough opportunity for us to apply it. Also, the course should present more examples of statistical concepts. At times, it felt as if I was just listening to an audiobook. Statistics can be better understood by applying concepts and visualizing. Week 4, in particular, felt very rushed. There was a lot of "this will be addressed later", which diminished the relevance.

por Kuan-Chih W

28 de jun de 2020

It is a fairly good course for statistics introduction. However, the explanation on how to apply with python libraries is not well-organized. Learners must have a well understanding of numpy, pandas, Matplotlib and Seaborn on their own in advance, because the TAs just read through the code without explaining why in details.

The statistics concepts lecturer is very good, but the TA didn't describe python libraries or modules selection and application concept.

por Rahul P

9 de ago de 2020

Best course for Statistic to understand from Datascience POV. If you're a software developer or from computer science, then this course is good to go into to understand the statistics for DataScience.

Deducting one start for :- 1. Week 4 was too much lectures and less assignment. Felt rushed.

2. The pythonic technical tutorial was not explained much technically and hence i had to spent a lot of time on different course to learn them.

por Arfaa S

28 de set de 2020

I am so happy to learn about data visualization through python. This course gives you a good amount of insight on how to visualize data through python and helps you understand the graphs and statistics behind it. I am very thankful to the team who have made up this course as it has been very helpful to me and other students like me to be much more confident with our knowledge in Python and Statistics.

por Teodoro N I D

6 de dez de 2020

The course is pretty technical and there are some gaps in the information that is presented (that will probably be addressed in later courses in the specialization), but it certainly whets one's appetite to learn more about statistics. I also appreciated the opportunities provided to apply the code that was learned and to see some of the theories in action. A worthwhile take overall.

por Carlos A P B

28 de jun de 2022

This course is an interesting course that shows how to use some tools in python to visualize data with python. Furthermore, it explains some of the essential aspects of data concerning statistics and probability. However, I consider that the focus and the form that the last week is managed is lacking. Moreover, some of the code is written in deprecated functions and should be updated.

por Divya R

23 de mai de 2020

This is a tactfully curated course for getting your legs wet in statistics with python, I personally was not completely comfortable with the statistical explanation and had to refer to multiple sources but hey a splendid job in other crucial things such as working around a code!! 10/10 recommend!! I wish to connect with the tutors via Linkedin