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Learner Reviews & Feedback for Statistical Data Visualization with Seaborn by Coursera Project Network

4.7
stars
130 ratings
26 reviews

About the Course

Welcome to this project-based course on Statistical Data Visualization with Seaborn. 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. It is also 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. We will use the results from our exploratory data analysis (EDA) in the previous project, Breast Cancer Diagnosis – Exploratory Data Analysis to: drop correlated features, implement feature selection and 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. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Top reviews

JS

Oct 06, 2020

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

HA

Jun 30, 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 of 25 Reviews for Statistical Data Visualization with Seaborn

By NAGABHAIRU V K

May 15, 2020

Not at all useful

By José P P D D S

Oct 06, 2020

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

By HAY a

Jun 30, 2020

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

By Aakansha S

Apr 22, 2020

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

By Punam P

May 13, 2020

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

By Hitesh J

Jul 20, 2020

optimal for beginners

By Doss D

Jun 14, 2020

Thank you very much

By Suresh B K

Jun 20, 2020

Good experience

By Hector P

Sep 13, 2020

Great project!

By Adolf Y M

Oct 11, 2020

all is good

By amarendra k y

Jun 02, 2020

Awesome

By prakhar m

Sep 27, 2020

Good

By tale p

Jun 26, 2020

good

By p s

Jun 23, 2020

Good

By Fhareza A

Sep 14, 2020

wow

By Lilendar R

Aug 10, 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.

By Sebastian A T H

Oct 02, 2020

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

By Gayatree D

Jun 03, 2020

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

By Bala S

Jun 12, 2020

The course is really good but i feel it would be even more good if there was more explanation.

By Zahrotul N I

Oct 24, 2020

Thank you for the lesson but I hope it can be much longer for the explanation.

By Juste N

Jul 06, 2020

Great project, would have been better with a larger dataset in my opinion.

By Pavithra K

Aug 01, 2020

project was good but i suggest u to have basic sklearn, ml practice .

By Dishant T

May 19, 2020

With more explanation, this could have been better by miles.

By Long N

Aug 28, 2020

This course was not designed well

By aithagoni m

Jul 30, 2020

good