Ask Measure Learn


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Ask, Measure, Learn


Ask, Measure, Learn

Author: Lutz Finger

language: en

Publisher: "O'Reilly Media, Inc."

Release Date: 2014-01-23


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You can measure practically anything in the age of social media, but if you don’t know what you’re looking for, collecting mountains of data won’t yield a grain of insight. This non-technical guide shows you how to extract significant business value from big data with Ask-Measure-Learn, a system that helps you ask the right questions, measure the right data, and then learn from the results. Authors Lutz Finger and Soumitra Dutta originally devised this system to help governments and NGOs sift through volumes of data. With this book, these two experts provide business managers and analysts with a high-level overview of the Ask-Measure-Learn system, and demonstrate specific ways to apply social media analytics to marketing, sales, public relations, and customer management, using examples and case studies.

The Lean Startup


The Lean Startup

Author: Eric Ries

language: en

Publisher: Crown Currency

Release Date: 2011-09-13


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Most startups fail. But many of those failures are preventable. The Lean Startup is a new approach being adopted across the globe, changing the way companies are built and new products are launched. Eric Ries defines a startup as an organization dedicated to creating something new under conditions of extreme uncertainty. This is just as true for one person in a garage or a group of seasoned professionals in a Fortune 500 boardroom. What they have in common is a mission to penetrate that fog of uncertainty to discover a successful path to a sustainable business. The Lean Startup approach fosters companies that are both more capital efficient and that leverage human creativity more effectively. Inspired by lessons from lean manufacturing, it relies on “validated learning,” rapid scientific experimentation, as well as a number of counter-intuitive practices that shorten product development cycles, measure actual progress without resorting to vanity metrics, and learn what customers really want. It enables a company to shift directions with agility, altering plans inch by inch, minute by minute. Rather than wasting time creating elaborate business plans, The Lean Startup offers entrepreneurs—in companies of all sizes—a way to test their vision continuously, to adapt and adjust before it’s too late. Ries provides a scientific approach to creating and managing successful startups in a age when companies need to innovate more than ever.

Python: Advanced Predictive Analytics


Python: Advanced Predictive Analytics

Author: Joseph Babcock

language: en

Publisher: Packt Publishing Ltd

Release Date: 2017-12-27


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Gain practical insights by exploiting data in your business to build advanced predictive modeling applications About This Book A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you. What You Will Learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python Style and approach This course aims to create a smooth learning path that will teach you how to effectively perform predictive analytics using Python. Through this comprehensive course, you'll learn the basics of predictive analytics and progress to predictive modeling in the modern world.