Principles of Data Science

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Author: Sinan Ozdemir

Publisher: Packt Publishing Ltd

ISBN: 1785888927

Category: Computers

Page: 388

View: 4802

Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.

Principles of Strategic Data Science

Creating value from data, big and small

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Author: Dr Peter Prevos

Publisher: Packt Publishing Ltd

ISBN: 1838985506

Category: Computers

Page: 104

View: 4121

Take the strategic and systematic approach to analyze data to solve business problems Key Features Gain detailed information about the theory of data science Augment your coding knowledge with practical data science techniques for efficient data analysis Learn practical ways to strategically and systematically use data Book Description Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you’ll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data. What you will learn Get familiar with the five most important steps of data science Use the Conway diagram to visualize the technical skills of the data science team Understand the limitations of data science from a mathematical and ethical perspective Get a quick overview of machine learning Gain insight into the purpose of using data science in your work Understand the role of data science managers and their expectations Who this book is for This book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.

Feature Engineering for Machine Learning

Principles and Techniques for Data Scientists

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Author: Alice Zheng,Amanda Casari

Publisher: "O'Reilly Media, Inc."

ISBN: 1491953195

Category: Computers

Page: 218

View: 9333

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

Data Science for Business

What You Need to Know about Data Mining and Data-Analytic Thinking

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Author: Foster Provost,Tom Fawcett

Publisher: "O'Reilly Media, Inc."

ISBN: 144937428X

Category: Computers

Page: 414

View: 529

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Principles of Managerial Statistics and Data Science

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Author: Roberto Rivera

Publisher: John Wiley & Sons

ISBN: 1119486416

Category: Mathematics

Page: 672

View: 9641

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

Practical Statistics for Data Scientists

50 Essential Concepts

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Author: Peter Bruce,Andrew Bruce

Publisher: "O'Reilly Media, Inc."

ISBN: 1491952911

Category: Computers

Page: 318

View: 4397

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Principles of Data Mining

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Author: David J. Hand,Professor in the Department of Statistics David J Hand,Heikki Mannila,Padhraic Smyth

Publisher: MIT Press

ISBN: 9780262082907

Category: Computers

Page: 546

View: 3012

Measuremente and Data. Visualizing and Exploring Data. Data Analysis and Uncertainty. A Systematic Overview of Data Mining Algorithms. Models and Patterns. Score Functions for Data Mining Algorithms. Serach and Optimization Methods. Descriptive Modeling. Predictive Modeling for Classification. Predictive Modeling for Regression. Data Organization and Databases. Finding Patterns and Rules. Retrieval by Content.

Algorithms for Data Science

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Author: Brian Steele,John Chandler,Swarna Reddy

Publisher: Springer

ISBN: 3319457977

Category: Computers

Page: 430

View: 1240

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

Principles of Data Wrangling

Practical Techniques for Data Preparation

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Author: Tye Rattenbury

Publisher: "O'Reilly Media, Inc."

ISBN: 1491938897

Category:

Page: N.A

View: 7147

A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst's time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors--time, granularity, scope, and structure--that you need to consider as you begin to work with data. You'll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today's data-driven organizations. Appreciate the importance--and the satisfaction--of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis

Hands-On Machine Learning for Cybersecurity

Safeguard your system by making your machines intelligent using the Python ecosystem

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Author: Soma Halder,Sinan Ozdemir

Publisher: Packt Publishing Ltd

ISBN: 178899096X

Category: Computers

Page: 318

View: 8364

Get into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn Use machine learning algorithms with complex datasets to implement cybersecurity concepts Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes Use TensorFlow in the cybersecurity domain and implement real-world examples Learn how machine learning and Python can be used in complex cyber issues Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book