Explore
For Enterprise
Join for Free
Log In

Artes e Humanas
Negócios
Ciência da Computação
Ciência de Dados
Tecnologia da informação
da saúdesaúde
Matemática e Lógica
Desenvolvimento Pessoal
Ciência e Engenharia Física
Ciências Sociais
Línguas
Notas
Certificados

Explore todo o Coursera

Navegar
Pesquisar
For Enterprise
Entrar
Inscreva-se gratuitamente

Core: Introduction to the Flesch Readability Score

Loading...

Data Structures and Performance

Universidade da Califórnia, San Diego

4.8 (1,584 classificações) | 52K alunos inscritos

Curso 4 de 4 no Programação orientada a objetos em Java Programa de cursos integrados

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables. You will explain how these data structures make programs more efficient and flexible. You will apply asymptotic Big-O analysis to describe the performance of algorithms and evaluate which strategy to use for efficient data retrieval, addition of new data, deletion of elements, and/or memory usage. The program you will build throughout this course allows its user to manage, manipulate and reason about large sets of textual data. This course is designed around the same video series as in our first course in this specialization, including explanations of core content, learner videos, student and engineer testimonials, and support videos -- to better allow you to choose your own path through the course!

Visualizar o programa do curso

Habilidades que você aprenderá

Trees (Data Structures), Data Structure, Linked List, Binary Tree

Avaliações

4.8 (1,584 classificações)

5 stars
1,317 ratings
4 stars
230 ratings
3 stars
28 ratings
2 stars
5 ratings
1 star
4 ratings

AE

Dec 06, 2016

Very solid review of data structures. In fact, I learned a lot of new things along the way like how to benchmark test Java code and how to properly use JUnit and other unit testing frameworks.

YK

Jan 11, 2016

I really liked this course and the way it was taught. Although I am a CS major, I really learnt a lot of other stuff from this course apart from learning the core content of Data Structures.

Na lição

Working with Strings

This week we're going to dive into the course programming project. In the first lesson you'll learn about Strings and Regular Expressions, and in the programming assignment this week you'll apply that knowledge to adding functionality to your text editor so that it can measure the "readability" of text by calculating something called the "Flesch Readability Score". This course is focused on building code that not only does interesting things, but also finishes them quickly. So, let's get started building some code!

Core: Introduction to the Flesch Readability Score5:44

Core: Basics of Strings in Java6:21

Core: Working with Strings in Java8:44

Support: For-Each Loops, part 1 of 51:50

Support: For-Each Loops, part 2 of 54:37

Support: For-Each Loops, part 3 of 54:31

Support: For-Each Loops, part 4 of 59:52

Support: For-Each Loops, part 5 of 52:01

Core: Introduction to Regular Expressions10:29

When I struggled: Regular expressions1:44

Concept Challenge: Regular Expressions9:10

Support: More with Regular Expressions8:50

Ministrado por

Christine Alvarado
Associate Teaching Professor
Mia Minnes
Assistant Teaching Professor
Leo Porter
Assistant Teaching Professor

Transcrição

[MUSIC]

All right, in this course, the project you're gonna be developing is

that cool text editor that you saw the demo of.

We're gonna get started here in this lesson talking about the first thing

you'll add to that text editor,

which is the ability to determine the readability of a piece of text.

So let's talk about what that means.

By the end of this video you'll be able to use the Flesch Index Score

in order to measure the readability of a piece of text

in order to incorporate it into your project.

So let's try to take a look at what we mean by text readability.

So let's take a look at this piece of text.

This is a piece of text from the US Tax Code document.

The document that describes how the United States taxes its citizens.

And this is largely understood to be one of the most confusing and

hard to understand documents around.

So I pulled a piece of text from that document and that piece of text says,

there is hereby imposed on the taxable income of every individual other than

a surviving spouse as defined in section 2(a) or the head of a household as

defined in section 2(b) who is not a married individual (as defined

in section 7703) a tax determined in accordance with the following table.

And then there's a table that follows.

Now I don't know about you, but that's pretty difficult to understand.

And in fact, that's one of the easier passages from this document to understand.

The first section I tried to pull, I spent 20 minutes trying to understand it, and

I couldn't understand it so I had to get a simpler piece of text.

So this is one of the simpler pieces of text from that document.

It's still pretty complicated.

So, I'm confused, let's simplify this text a little bit.

Here's my re-write of what that piece of text just said.

Basically it says, if you're single, you've never lost your spouse, and you're

not the head of a household, then you pay taxes according to the following table.

And again the same table would follow.

Much easier to understand.

So we have these two pieces of text.

The piece on top is relatively difficult to understand,

the piece on the bottom is relatively easy to understand.

I've taken a few liberties in simplifying the text.

It's not exactly the same with the same precision as the piece of text on top, but

they mean basically the same thing.

And the question we wanna ask now is,

how can we quantify the difference between these two pieces of text?

So what we're going to do in this video, as well as on your project,

is you're going to use something called the Flesch readability score

in order to allow the computer to quantify how readable a piece of text is.

You can see the formula for the Flesch readability score here.

And the Flesch readability score was developed by

an author named Rudolf Flesch.

And what it looks like is the following.

So the idea behind the score is that you have this constant out front,

this 206.858.

And then, from that constant, you're going to subtract two different terms.

And that's going to basically lower the potential; Flesch score that you can get.

So in order to understand how this formula works,

we need to look at those two terms that we're going to be subtracting from that

constant to understand what a high score means, and what a low score means.

So let's first think about what makes this formula high.

So what makes this formula high, since we're subtracting two terms,

is if these two terms we're subtracting off are relatively low.

So what does it mean?

When are these two terms relatively low?

Well, they're relatively low if there are few words per sentence,

then that ratio is going to be low.

And few syllables per word, then that second ratio is going to be low.

So, in other words, if we're dealing with short sentences and short words,

those two terms will be low leading to a very high Flesch score.

So now intuitively we can see that a high Flesch score, and in practice above 90,

leads to a very easy to read piece of text.

On the other hand, if we have those two terms be high,

in other words we have a lot of words per sentence, and

a lot of syllables per word we're gonna end up subtracting off a lot

from our initial constant and end up with a very low Flesch score.

So a Flesch score of below 30 is a piece of text that's very difficult to read.

And that again makes sense.

If you have long sentences with long words the text is gonna be more confusing.

Now then we have these constants out in front of those two ratios

that basically just weight those two terms accordingly.

And those are just determined by Flesch to match our intuition about how text is easy

or difficult to read.

And in this case it basically means that longer words

make text a little more difficult to read than longer sentences.

So more syllables are bad basically.

So, if we apply that to their two pieces of text, we can see that the piece of text

on top, that confusing text, has a very low Flesch score, 12.6.

And that corresponds to basically a post graduate reading level, which everyone

basically agrees, you need to be a lawyer in order to understand this document.

And that piece of text on the bottom, my paraphrase of it that's got a much higher

Flesch score of 65.8, and that's about a 10th grade reading level.

So my six year old maybe wouldn't be able to read this and understand it.

But people with secondary education should do a pretty good job of

understanding what that sentence means.

So, how does this relate to what we're about to talk about?

Well, in order to calculate the Flesch score for text, what do we need to know?

We need to know the number of words in the piece of text.

We need to know the number of sentences.

And we need to know the number of syllables.

Now if we imagine that our piece of text is just represented as one big long string

in Java, we're going to need to be able to do

manipulation with those strings in order to calculate that score.

And that's what we're gonna look at in the next video.

Explore nosso catálogo

Registre-se gratuitamente e obtenha recomendações, atualizações e ofertas personalizadas.

O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.

© 2019 Coursera Inc. Todos os direitos reservados.

Baixar na App Store

Baixar no Google Play

Coursera
Sobre
Liderança
Carreiras
Lista de cursos
Certificados
Certificados MasterTrack™
Notas
For Enterprise
For Government

Comunidade
Learners
Partners
Developers
Beta Testers
Translators

Conectar
Blog
Facebook
LinkedIn
Twitter
Instagram
YouTube
Tech Blog

Mais
Termos
Privacidade
Ajuda
Acessibilidade
Imprensa
Contato
Diretório
Afiliados