Download PDF DATA STRUCTURES AND PROBLEM SOLVING USING C++ Second Edition by MARK ALLEN WElSS
Sinopsis
In C++ we can declare and use arrays in two basic ways. The primitive method is to use the built-in array. The alternative is to use a vector. The syntax for both methods is more or less the same; however, the vector is much easier and slightly safer to use than the primitive array and is preferred for most applications. The major philosophical difference between the two is that the vector behaves as a first-class type (even though it is implemented in a library), whereas the primitive array is a second-class type. Similarly, C++ provides primitive strings (which are simply primitive arrays of char) and the muchpreferred string. In this section we examine what is meant by first-class and second-class types and show you how to use the vector and string
Content
- Arrays, Pointers, and Structures
- Objects and Classes
- Templates
- Inheritance
- Design Patterns
- Algorithm Analysis
- The Standard Template Library
- Recursion
- Sorting Algorithms
- Randomization
- Fun and Games
- Stacks and Compilers
- Utilities
- Simulation
- Graphs and Paths
- Stacks and Queues
- Linked Lists
- Trees
- Binary Search Trees
- Hash Tables
- A Priority Queue: The Binary Heap
- Splay Trees
- Merging Priority Queues
- The Disjoint Set Class
Download PDF Computer Science and Data Analysis Series Exploratory Data Analysis with MATLAB by Wendy L. Martinez
Sinopsis
This book is divided into two main sections: pattern discovery and graphical EDA. We first cover linear and nonlinear dimensionality reduction because sometimes structure is discovered or can only be discovered with fewer dimensions or features. We include some classical techniques such as principal component analysis, factor analysis, and multidimensional scaling, as well as some of the more recent computationally intensive methods like self-organizing maps, locally linear embedding, isometric feature mapping, and generative topographic maps.
Searching the data for insights and information is fundamental to EDA. So, we describe several methods that ‘tour’ the data looking for interesting structure (holes, outliers, clusters, etc.). These are variants of the grand tour and projection pursuit that try to look at the data set in many 2-D or 3-D views in the hope of discovering something interesting and informative. Clustering or unsupervised learning is a standard tool in EDA and data mining. These methods look for groups or clusters, and some of the issues that must be addressed involve determining the number of clusters and the validity or strength of the clusters. Here we cover some of the classical methods such as hierarchical clustering and k-means. We also devote an entire chapter to a newer technique called model-based clustering that includes a way to determine the number of clusters and to assess the resulting clusters.
Evaluating the relationship between variables is an important subject in data analysis. We do not cover the standard regression methodology; it is assumed that the reader already understands that subject. Instead, we include a chapter on scatterplot smoothing techniques such as loess. The second section of the book discusses many of the standard techniques of visualization for EDA. The reader will note, however, that graphical techniques, by necessity, are used throughout the book to illustrate ideas and concepts.
In this section, we provide some classic, as well as some novel ways of visualizing the results of the cluster process, such as dendrograms, treemaps,rectangle plots, and ReClus. These visualization techniques can be used to assess the output from the various clustering algorithms that were covered in the first section of the book. Distribution shapes can tell us important things about the underlying phenomena that produced the data. We will look at ways to determine the shape of the distribution by using boxplots, bagplots, q-q plots, histograms, and others.
Content
- Introduction to Exploratory Data Analysis
- Dimensionality Reduction - Linear Methods
- Dimensionality Reduction - Nonlinear Methods
- Data Tours
- Finding Clusters
- Model-Based Clustering
- Smoothing Scatterplots
- Visualizing Clusters
- Distribution Shapes
- Multivariate Visualization
- Proximity Measures
- Software Resources for EDA
- Introduction to MATLAB
Download PDF Web Crawling and Data Mining with Apache Nutch Perform web crawling and apply data mining in your application by Zakir Laliwala
Sinopsis
Apache Nutch is a very robust and scalable tool for web crawling; it can be integrated with the scripting language Python for web crawling. You can use it whenever your application contains huge data and you want to apply crawling on your data.
This chapter covers the introduction to Apache Nutch and its installation, and also guides you on crawling, parsing, and creating plugins with Apache Nutch. It will start from the basics of how to install Apache Nutch and then will gradually take you to the crawling of a website and creating your own plugin.
Content
- Introduction to Apache Nutch
- Installing and configuring Apache Nutch
- Crawling your website using the crawl script
- Crawling the Web, the CrawlDb, and URL filters
- Parsing and parse filters
- The Apache Nutch plugin
- Understanding the Nutch Plugin architecture
- Deployment, Sharding, and AJAX Solr with Apache Nutch
- Deployment of Apache Solr
- Sharding using Apache Solr
- Working with AJAX Solr
- Integration of Apache Nutch with Apache Hadoop and Eclipse
- Integrating Apache Nutch with Apache Hadoop
- Configuring Apache Nutch with Eclipse
- Apache Nutch with Gora, Accumulo, and MySQL
- Introduction to Apache Accumulo
- Introduction to Apache Gora
- Use of Apache Gora
- Integration of Apache Nutch with Apache Accumulo
- Integration of Apache Nutch with MySQL
Download PDF Data Structures and Abstractions with Java™ Third Edition by Frank M. Carrano
Sinopsis
this morning, you went to the back of a line to wait for the cashier. The line organized people chronologically. The first person in the line was the first to be served and to leave the line.
Eventually, you reached the front of the line and left the store with a bag containing your purchases. The items in the bag were in no particular order, and some of them were the same.
Do you see a stack of books or a pile of papers on your desk? It’s easy to look at or remove the top item of the stack or to add a new item to the top of the stack. The items in a stack also are organized chronologically, with the item added most recently on top and the item added first on
the bottom.
At your desk, you see your to-do list. Each entry in the list has a position that might or might not be important to you. You may have written them either as you thought of them, in their order of importance, or in alphabetical order. You decide the order; the list simply provides places for your entries.
Your dictionary is an alphabetical list of words and their definitions. You search for a word and get its definition. If your dictionary is printed, the alphabetical organization helps you to locate a word quickly. If your dictionary is computerized, its alphabetical organization is hidden, but it still speeds the search.
Speaking of your computer, you have organized your files into folders, or directories. Each folder contains several other folders or files. This type of organization is hierarchical. If you drew a picture of it, you would get something like a family tree or a chart of a company’s internal departments. These data organizations are similar and are called trees.
Finally, notice the road map that you are using to plan your weekend trip. The diagram of roads and towns shows you how to get from one place to another. Often, several ways are possible. One way might be shorter, another faster. The road map has an organization known as a graph.
Content
- Bags
- Bag Implementations That Use Arrays
- A Bag Implementation That Links Data
- The Efficiency of Algorithms
- Stacks
- Stack Implementations
- Recursion
- An Introduction to Sorting
- Faster Sorting Methods
- Queues, Deques, and Priority Queues
- Queue, Deque, and Priority Queue Implementations
- Lists
- List Implementations That Use Arrays
- A List Implementation That Links Data
- Iterators
- Sorted Lists
- Inheritance and Lists
- Searching
- Dictionaries
- Dictionary Implementations
- Introducing Hashing
- Hashing as a Dictionary Implementation
- Trees
- Tree Implementations
- A Binary Search Tree Implementation
- A Heap Implementation
- Balanced Search Trees
- Graphs
- Mutable, Immutable, and Cloneable Objects
Download Data Smart : Using Data Science to Transform Information into Insight By John W. Foreman
Sinopsis
To an extent, data science is synonymous with or related to terms like business analytics, operations research, business intelligence, competitive intelligence, data analysis and modeling, and knowledge extraction (also called knowledge discovery in databases or KDD). It’s just a new spin on something that people have been doing for a long time.
There’s been a shift in technology since the heyday of those other terms. Advancements in hardware and software have made it easy and inexpensive to collect, store, and analyze large amounts of data whether that be sales and marketing data, HTTP requests from your website, customer support data, and so on. Small businesses and nonprofi ts can now engage in the kind of analytics that were previously the purview of large enterprises. Of course, while data science is used as a catch-all buzzword for analytics today, data science is most often associated with data mining techniques such as artifi cial intelligence, clustering, and outlier detection. Thanks to the cheap technology-enabled proliferation of transactional business data, these computational techniques have gained a foothold in business in recent years where previously they were too cumbersome to use in production settings.
In this book, I’m going to take a broad view of data science. Here’s the definition I’ll work from:
Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products.
This is a business-centric defi nition. It’s about a usable and valuable end product derived from data. Why? Because I’m not in this for research purposes or because I think data has aesthetic merit. I do data science to help my organization function better and create value; if you’re reading this, I suspect you’re after something similar.
With that defi nition in mind, this book will cover mainstay analytics techniques such as optimization, forecasting, and simulation, as well as more “hot” topics such as artifi cial intelligence, network graphs, clustering, and outlier detection.
Some of these techniques are as old as World War II. Others were introduced in the last 5 years. And you’ll see that age has no bearing on diffi culty or usefulness. All these techniques—whether or not they’re currently the rage—are equally useful in the right business context. And that’s why you need to understand how they work, how to choose the right technique for the right problem, and how to prototype with them. There are a lot of folks out there who understand one or two of these techniques, but the rest aren’t on their radar. If all I had in my toolbox was a hammer, I’d probably try to solve every problem by smackingit real hard. Not unlike my two-year-old. Better to have a few other tools at your disposal.
Content
- Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask
- Cluster Analysis Part I: Using K-Means to Segment Your Customer Base
- Naïve Bayes and the Incredible Lightness of Being an Idiot
- Optimization Modeling: Because That “Fresh Squeezed” Orange Juice Ain’t Gonna Blend Itself
- Cluster Analysis Part II: Network Graphs and Community Detection
- The Granddaddy of Supervised Artifi cial Intelligence—Regression
- Ensemble Models: A Whole Lot of Bad Pizza
- Forecasting: Breathe Easy; You Can’t Win
- Outlier Detection: Just Because They’re Odd Doesn’t Mean They’re Unimportant
- Moving from Spreadsheets into R
Download Data Storage Networking Real World Skills for the CompTIA Storage +™ Certification and Beyond By Poulton, Nigel
Sinopsis
As one of the three pillars of IT infrastructure (computing, networking, and storage), storage is fundamental to just about every IT infrastructure ever built. This makes managing storage an essential skill for everyone involved in IT infrastructure, from systems administrators to infrastructure architects.
This book was written so that you can gain the required storage knowledge from which to build a successful IT career. Understanding how disk drives, flash memory, and storage arrays work is vital to understanding how to design, implement, administer, and troubleshoot storage environments. The book also includes many real-world examples to help clarify concepts and show how they should be applied. Finally, the book covers all the objectives listed in the CompTIA Storage+ exam (SGO-001). So after reading this book, you will have all the knowledge required to both pass the exam and implement storage in the real world.
We dedicate entire chapters to topics such as cloud and converged infrastructure, which are reshaping the IT world. Software-defined storage is also addressed in various chapters. This book is written with the assumption that you have very little knowledge of storage and want to get up to speed as quickly as possible. Great effort was made so that fundamentals would not be tedious for any reader who feels they already have a good grasp of the basics but want to deepen their knowledge. Covering the basics in a succinct manner also allows such readers to quickly refresh their knowledge and potentially clarify any areas where they were previously uncertain.
Content
- Storage Primer
- Storage Devices
- Storage Arrays
- RAID: Keeping Your Data Safe
- Fibre Channel SAN
- iSCSI SAN
- Files, NAS, and Objects
- Replication Technologies
- Storage Virtualization
- Capacity Optimization Technologies
- Backup and Recovery
- Storage Management
- The Wider Data Center Neighborhood
- Converged Networking
- Cloud Storage
- The CompTIA Storage+ Exam
- About the Additional Study Tools