By Warren Sande, Carter Sande
A mild yet thorough advent to the area of desktop programming, it's written in language a 12-year-old can stick with, yet someone who desires to how one can application a working laptop or computer can use it. Even adults. Written by means of Warren Sande and his son, Carter, and reviewed by way of specialist educators, this booklet is kid-tested and parent-approved.
About This Book
Learn to speak for your computing device in its personal language! no matter if you must create a online game, begin a company, or clear up an incredible challenge, step one is studying to put in writing your individual courses. Programming is a enjoyable problem, and it's effortless to get started!
This up to date and revised variation of hi global! introduces the area of machine programming in a transparent and interesting variety. Written via Warren Sande and his son, Carter, it really is choked with examples that would get you considering and studying. Reviewed via expert educators, this e-book is kid-tested and parent-approved. You don't want to know something approximately programming to exploit the publication, simply the fundamentals of utilizing a working laptop or computer. if you happen to can begin a application and shop a dossier, you'll be off and running!
Purchase of the print publication features a unfastened book in PDF, Kindle, and ePub codecs from Manning Publications.
• Explains ideas in transparent language
• plenty of images, cartoons, and enjoyable examples
• entire set of perform questions and exercises
• Illustrated in complete color
• Hello World! makes use of Python, a programming language designed to be effortless to benefit. utilizing enjoyable examples, it brings to existence recommendations of computing— looping, judgements, enter and output, facts buildings, graphics—and many more.
About the Authors
Warren Sande is an digital structures engineer who makes use of Python either as a "do anything" scripting language and for you to educate desktops and programming. Carter Sande is a highschool pupil who's captivated with know-how. whilst he's no longer solving his school's community and assisting his classmates get better misplaced homework, he loves to experience his motorcycle and write unfashionable video games.
Table of Contents
1. Getting Started
2. take into account This: reminiscence and Variables
3. easy Math
4. sorts of Data
6. GUIs—Graphical person Interfaces
7. judgements, Decisions
8. Loop the Loop
9. only for You—Comments
10. video game Time
11. Nested and Variable Loops
12. gathering issues Together—Lists and Dictionaries
17. Sprites and Collision Detection
18. a brand new type of Input—Events
20. extra GUIs
21. Print Formatting and Strings
22. dossier enter and Output
23. Take a Chance—Randomness
24. machine Simulations
25. Skier Explained
26. Python Battle
27. What's subsequent?
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Additional resources for Hello World!: Computer Programming for Kids and Other Beginners (2nd Edition)
Probability plot for the data set shown in Figure 2-9. distribution function ((x − μ)/σ ) with mean μ and standard deviation σ : yi = xi − μ σ only if data is Gaussian Here, yi is the value of the cumulative distribution function corresponding to the data point xi ; in other words, yi is the quantile of the point xi . Now comes the trick. We apply the inverse of the Gaussian distribution function to both sides of the equation: −1 (yi ) = xi − μ σ With a little bit of algebra, this becomes xi = μ + σ −1 (yi ) In other words, if we plot the values in the data set as a function of −1 (yi ), then they should fall onto a straight line with slope σ and zero intercept μ.
So far, I have always assumed that we want to compare an empirical data set against a theoretical distribution. But there may also be situations where we want to compare two empirical data sets against each other—for example, to find out whether they were drawn from the same family of distributions (without having to specify the family explicitly). The process is easiest to understand when both data sets we want to compare contain the same number of points. You sort both sets and then align the points from both data sets that have the same rank (once sorted).
Do many data points lie far away from the central group of points), or are most of the points—with the possible exception of individual outliers—confined to a restricted region? • If there are clusters, how many are there? Is there only one, or are there several? Approximately where are the clusters located, and how large are they—both in terms of spread and in terms of the number of data points belonging to each cluster? • Are the clusters possibly superimposed on some form of unstructured background, or does the entire data set consist only of the clustered data points?