Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover:
1. 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia
2. 2. Options for Julia IDEs
3. 3. Programming structures and functions
4. 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing
5. 5. Data visualization and some simple yet powerful statistics for data exploration purposes
6. 6. Dimensionality reduction and feature evaluation
7. 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines)
8. 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights.Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in PythonLearn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases
Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.
By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.
Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?
Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype.
But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.
Each chapter will cover a different technique in a spreadsheet so you can follow along:Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language
You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.
Do you want to get started with creating your own vehicles, maps, landscapes, and tools that you can use in the game and share with the Farming Simulator community? Then this is the resource for you! With the help of Jason van Gumster, you'll get up and running on everything you need to master 3D modeling and simulation—and have fun while doing it! Inside, you'll find out how to create and edit maps, start using the material panel, customize your mods by adding texture, use the correct file-naming conventions, test your mod in single and multiplayer modes, get a grip on using Vehicle XML, and so much more.
There's no denying that Farming Simulator players love modding—and now there's a trusted, friendly resource to help you take your modding skills to the next level and get even more out of your game. Written in plain English and packed with tons of step-by-step explanations, Farming Simulator Modding For Dummies is a great way to learn the ropes of 3D modeling with the tools available to you in the game. In no time, you'll be wowing your fellow gamesters—and yourself—with custom, kick-butt mods. So what are you waiting for?Includes an easy-to-follow introduction to using the GIANTS 3D modeling tools Explains how to export models to Blender, Maya, 3DS Max, or FBX Provides tips for using the correct image format for textures Details how to use Photoshop and Audacity to create custom mods for Farming Simulator
Whether you're one of the legions of rabid fans of the popular Farming Simulator game or just someone who wants to learn the basics of 3D modeling and animation, you'll find everything you need in this handy guide.