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Volver a Statistical Data Visualization with Seaborn From UST

Opiniones y comentarios de aprendices correspondientes a Statistical Data Visualization with Seaborn From UST por parte de Coursera Project Network

4.6
estrellas
157 calificaciones
32 reseña

Acerca del Curso

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!...

Principales reseñas

JS
5 de oct. de 2020

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

HA
29 de jun. de 2020

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

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1 - 25 de 32 revisiones para Statistical Data Visualization with Seaborn From UST

por Nagabhairu v k

14 de may. de 2020

Not at all useful

por Yaron K

7 de sep. de 2021

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).

por Suhaimi C

19 de nov. de 2020

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.

por José P P D D S

6 de oct. de 2020

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

por HAY a

30 de jun. de 2020

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

por Aakansha S

22 de abr. de 2020

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

por Punam P

13 de may. de 2020

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

por Jayden P

24 de jun. de 2021

Clean and simple. No issues with this course .

por SUGUNA M

19 de nov. de 2020

Good project based course

por Hitesh J

20 de jul. de 2020

optimal for beginners

por Doss D

14 de jun. de 2020

Thank you very much

por Suresh B K

19 de jun. de 2020

Good experience

por Hector P

13 de sep. de 2020

Great project!

por Adolf Y M

11 de oct. de 2020

all is good

por Priscila A B

7 de abr. de 2021

Perfect!

por amarendra k y

2 de jun. de 2020

Awesome

por Prakhar M

27 de sep. de 2020

Good

por tale p

26 de jun. de 2020

good

por p s

22 de jun. de 2020

Good

por Fhareza A

14 de sep. de 2020

wow

por Jorge G

26 de feb. de 2021

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.

por Alex K

7 de dic. de 2020

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?

por Lilendar R

9 de ago. de 2020

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.

por Sebastian A T H

2 de oct. de 2020

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

por Gayatree D

3 de jun. de 2020

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