Low scores indicate text that is complicated to understand. As you can see, academic or technical articles that are difficult to read receive low Flesch readability scores, while simpler texts receive Flesch reading scores on the high end.
This is an intermediate Java course, and we will build on your prior knowledge. The first number, Flesch-Kincaid reading ease, is based on a ranking scale of 0-100, and the higher your score, the better. The program you will build throughout this course allows its user to manage, manipulate and reason about large sets of textual data. The formula for FleschKincaid grade level is: 0.39 × (total words total sentences) + 11.8 × (total syllables total words) - 15.59. Thus, the lowest grade level, -3.40, represents the best readable content. 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. It's quite different from the reading score as a text gets better with a lower score than a high score. You will explain how these data structures make programs more efficient and flexible.
In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables.
#Flesch readability scale lowest software#
We recommend this course to learners who have previous experience in software development or a background in computer science, and in particular, we recommend that you have taken the first course in this specialization (which also requires some previous experience with Java). Efficiency is critical, but how do we achieve it, and how do we even measure it? 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.
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