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Вернуться к Statistical Data Visualization with Seaborn From UST

Отзывы учащихся о курсе Statistical Data Visualization with Seaborn From UST от партнера Coursera Project Network

4.7
звезд
Оценки: 159
Рецензии: 32

О курсе

Welcome to this Guided Project on Statistical Data Visualization with Seaborn, From UST. For more than 20 years, UST has worked side by side with the world’s best companies to make a real impact through transformation. Powered by technology, inspired by people and led by their purpose, they partner with clients from design to operation. With this Guided Project from UST, you can quickly build in-demand job skills and expand your career opportunities in the Data Science field. Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox as well as a powerful tool to identify problems in analyses and for illustrating results. In this project, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) data set. Using the exploratory data analysis (EDA) results from the Breast Cancer Diagnosis – Exploratory Data Analysis Guided Project, you will practice dropping correlated features, implement feature selection and utilize several feature extraction methods including; feature selection with correlation, univariate feature selection, recursive feature elimination, principal component analysis (PCA) and tree based feature selection methods. Lastly, we will build a boosted decision tree classifier with XGBoost to classify tumors as either malignant or benign. By the end of this Guided Project, you should feel more confident about working with data, creating visualizations for data analysis, and have practiced several methods which apply to a Data Scientist’s role. Let's get started!...

Лучшие рецензии

JS
5 окт. 2020 г.

A machine learning perspective on seaborn capacity, dealing with plots of common results when removing features or selecting important features from dataset

HA
29 июня 2020 г.

Great course for a beginner to be equipped with data science tools and feature selection methods for machine learning!

Фильтр по:

1–25 из 32 отзывов о курсе Statistical Data Visualization with Seaborn From UST

автор: Nagabhairu v k

14 мая 2020 г.

Not at all useful

автор: Yaron K

7 сент. 2021 г.

Shows an example of feature selection using sklearn SelectKBest and RFECV, xgboost plot_importance, and dimensionality reduction using PCA. With seaborn visualizations of EDA and results of running xgboost ML.

The completed notebook is included in the resources, so you can concentrate on learning (rather than on improving your typing skills).

автор: Suhaimi C

19 нояб. 2020 г.

Awesome guided project. Good overview and interesting subject. I learned a lot using python and seaborn for statistical data visualization. Thanks much for offering this guided project. Highly recommend it to take part 1 first, then this part 2.

автор: José P P D D S

6 окт. 2020 г.

A machine learning perspective on seaborn capacity, dealing with plots of common results when removing features or selecting important features from dataset

автор: HAY a

30 июня 2020 г.

Great course for a beginner to be equipped with data science tools and feature selection methods for machine learning!

автор: Aakansha S

22 апр. 2020 г.

Thankyou Sir , for explaining in a very simple way it helps me alot!

автор: Punam P

13 мая 2020 г.

Thanks for the course..Nice work and helpful project..

автор: Jayden P

24 июня 2021 г.

Clean and simple. No issues with this course .

автор: SUGUNA M

19 нояб. 2020 г.

Good project based course

автор: Hitesh J

20 июля 2020 г.

optimal for beginners

автор: Doss D

14 июня 2020 г.

Thank you very much

автор: Suresh B K

19 июня 2020 г.

Good experience

автор: Hector P

13 сент. 2020 г.

Great project!

автор: Adolf Y M

11 окт. 2020 г.

all is good

автор: Pris A

7 апр. 2021 г.

Perfect!

автор: amarendra k y

2 июня 2020 г.

Awesome

автор: Prakhar M

27 сент. 2020 г.

Good

автор: tale p

26 июня 2020 г.

good

автор: p s

22 июня 2020 г.

Good

автор: Fhareza A

14 сент. 2020 г.

wow

автор: Jorge G

26 февр. 2021 г.

I do not recommend taking this type of course, take one and pass it, however after a few days I have tried to review the material, and my surprise is that it asks me to pay again to be able to review the material. Of course coursera gives me a small discount for having already paid it previously. It is very easy to download the videos and difficult to get hold of the material, but with ingenuity it is possible. Then I recommend uploading them to YouTube and keeping them private for when they want to consult (they avoid legal problems and can share with friends), then they can request a refund.

автор: Alex K

7 дек. 2020 г.

Good instructor, nice bite sized course design and hands on approach. Only thing is the complexity: I probably lack a bit of the theoretical understanding which makes it a little mystifying what is going on, particularly in the second part of the course. At the same time, if I did have the required background I imagine it might be a little basic?

автор: Lilendar R

9 авг. 2020 г.

I think the quizs are very easy, it has to have atleast 10 questions. Beause as we are provided with the jupyter notebook we are understanding everything in detail and expecting some good no of questions in the quiz.

автор: Sebastian A T H

2 окт. 2020 г.

Un excelente curso para profundizar en habilidades prácticas tanto en temas de seaborn como en sklearn

автор: Gayatree D

3 июня 2020 г.

The course was really nice however, I faced little issues while connecting to the rhyme desktop.