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Albert Einstein: “Nobody expected me to lay golden eggs” By Jeffrey Strickland
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A sequel to "Men of Manhattan", Albert Einstein: “Nobody expected me to lay golden eggs” is a new and different look at the man whose genius we often want to emulate. Although... More > this book recognizes his genius, it looks at it in a different light. Einstein, the not so good student, working in a patent office, developed his natural understanding of his world through an “out-of-the-box” curiosity among close friends and confidants, including his wife. His early career was unremarkable in the eyes of academia, and even after publication of his famous four papers in 1905, it was another four years before he was taken seriously as a scientist. We probably know him best for E=mc2, the so-called energy-mass equivalence formula, but much more is contained herein. Though this is not a rigorous mathematical treatment, we could not possibly know Einstein without a glimpse of his math.< Less
LinkedIn Memoirs By Jeffrey Strickland
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LinkedIn operates the world’s largest professional network on the Internet with more than 332 million members in over 200 countries and territories. Dr. Strickland been a LinkedIn Premium... More > member since November 2, 2010. At the time of this publication he has 4,250 followers who view his posts on a regular basis. This book is a collection of his most popular post for the year 2014. I have posted articles on a variety of topics, usually something I am quite passionate about, like professionalism, etiquette, analytical science, leadership, and so on. Post titles include, "LinkedIn Random Acts of Kindness", "Three Things Leaders Must Do", "The Last Full Measure of Devotion", "To Teach or not to Teach", and "If You Hate Probability Theory, You are in Good Company". I enjoy the interaction that I have with over 4,000 connections from 59 countries, if I include the Republic of Texas. This book is for them, but also for those friends and colleagues who do not frequent the network as much as I do.< Less
Quantum Phaith By Jeffrey Strickland
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Typically, scientist try to either prove or disprove Scripture, as it relates the Creation story, then write books about their findings. Quantum Phaith is not one of those books. Instead, the author... More > accepts the Word of God, a priori. Quantum Phaith is a journey through elementary quantum mechanics, chaos theory, number theory, and more. This is also a journey through the author’s faith and what he has discovered about science through it; his faith has been unshaken by science and mathematics. Quantum Phaith examines the Creation account, using the results to support what scientists have discovered about the universe. Their discoveries were already contained in the Bible! Quantum Phaith portrays God as perfect, His creation perfect in every regard. Imperfection was introduced by the Fall or Man. God is all-powerful, all-knowing, transcends time, space, and our understanding. God is order, and you will find that chaos contains unobservable order! Quantum Phaith will make you think. It may change your life.< Less
Predictive Modeling and Analytics By Jeffrey Strickland
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This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and... More > machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)—this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no “by-hand” exercises).< Less
Predictive Analytics using R By Jeffrey Strickland
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This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling. A predictive model is a... More > statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics — it is an advanced book. Some theoretical foundations are laid out but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. You may download R, from a preferred CRAN mirror at http://www.r-project.org/. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this a textbook with problem solving in R—but there are no “by-hand” exercises.< Less
Data Analytics Using Open-Source Tools By Jeffrey Strickland
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This book is about Data Analytics. In that respect, it is like others. What distinguishes it from the rest is the variety of open-source tool applications. This book incorporates the use of R Studio,... More > Python, SAS Studio (University Edition), and KNIME. This book is also about manipulating Big Data. Apache Hadoop on Hortonworks Sandbox is introduced and we manage, move, handle, and transform data using Apache Hive, Apache Spark, MapReduce and TEZ, with terminal shell commands and Ambari. We show you how to set up a virtual machine in Microsoft Azure. We then use the data in later chapters for modeling. We cover Descriptive Modeling and Predictive. The content includes Support Vector Machines, Decision Tree learning, Random Forests, Naïve and Empirical Bayes, Gradient Boosting, Cluster Modeling, Generalized Linear Models, Logistic Regression, and Artificial Neural Networks. Every chapter includes completely worked examples using one or more open-source tools.< Less
Data Science and Analytics for Ordinary People By Jeffrey Strickland
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Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my... More > weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein. Data science and analytics have been used as synonyms on occasion. In reality data science includes data modeling, data mining, data analysis, database architecture and so on. Analytics is what we do to make sense of the data. That is, we take data and turn it into information for business decision makers. This our course implies that we translate our data science jargon into English.< Less