Data Analytics For Businesses 2019 Master Data Science With Optimised Marketing Strategies Using Data Mining Algorithms Artificial Intelligence Machine Learning Predictive Modelling And More
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Data Analytics for Businesses 2019: Master Data Science with Optimised Marketing Strategies using Data Mining Algorithms (Artificial Intelligence, Machine Learning, Predictive Modelling and more)
" Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximise YOUR business. Yes, you have customers that love your product. However, you're having trouble finding new customers and captivating their attention. You realized your also losing customers, and you have no clue what you can do to prevent this from happening. How do I stand out in a crowd of businesses? How do I target my perfect client and make them choose ME? If this sounds like you, Data Analytics for Businesses if the guide you need. This book will walk you through the fundamental principles of data science and how to apply the "data analytic mindset" when approaching your business. You will learn how to extract valuable insights from data sources you ALREADY HAVE, and make informed business decisions to help you achieve your goals. With real world examples of how to apply data analytics to your business, this book does what others fail to do. Break the process down step by step, so you can optimize unique parts of your business; such as improving customer loyalty or reducing churn. This guide also helps you understand the many data-mining techniques in use today. Discover the value of applied data science for business decision-making. You'll learn how to think data-analytically, and make connections between data sources to unveil insights you've never imagined. In this book you will learn: * Why every company should be leveraging Data Analytics * The difference between Big Data, Data Science and Data Analytics. * How to achieve your goals by applying data-analytical thinking to your business * The recommended data mining techniques for each of your business goals. * The most important thing to remember when extracting knowledge from your data. * How to use data analytics to improve brand loyalty and customer experience. * How to hire the best data scientist, and more. If you are overwhelmed by this whole new topic of data analytics, don't be. This guide is designed for beginners, with all the guidance you need to understand the fundamentals of harnessing data analytics for your business. So even if you have never heard about data analytics until today, I promise we will walk through this step- by-step. By the end of this, you'll be able to think analytically and make informed business decisions. This book illustrates how EASY it is to find success by just applying a few principles. So stop reading this description, and start reading Data Analytics for Businesses instead. Scroll up, and CLICK BUY now! "
Data Science for Business 2019 (2 BOOKS In 1)
★☆★ This book includes 2 Manuscripts: Data Analytics for Businesses 2019 + Machine Learning for Beginners 2019.★☆★ Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximize YOUR business. Yes, you have customers that love your product. However, you're having trouble finding new customers and captivating their attention. You realized you're also losing customers, and you have no clue what you can do to prevent this from happening. How do I stand out in a crowd of businesses? How do I target my perfect client and make them choose ME? If this sounds like you, Data Analytics for Businesses if the guide you need. This book will walk you through the fundamental principles of data science and how to apply the "data-analytic mindset" when approaching your business. You will learn how to extract valuable insights from data sources you ALREADY HAVE, and make informed business decisions to help you achieve your goals. With real-world examples of how to apply data analytics to your business, this book does what others fail to do. Break the process down step by step, so you can optimize unique parts of your business; such as improving customer loyalty or reducing churn. This guide also helps you understand the many data-mining techniques in use today. Discover the value of applied data science for business decision-making. You'll learn how to think data-analytically and make connections between data sources to unveil insights you've never imagined. In this book you will learn: Why every company should be leveraging Data Analytics The difference between Big Data, Data Science and Data Analytics How to achieve your goals by applying data-analytical thinking to your business The recommended data mining techniques for each of your business goals The most important thing to remember when extracting knowledge from your data How to use data analytics to improve brand loyalty and customer experience How to hire the best data scientist, and more. If you are overwhelmed by this whole new topic of data analytics, don't be. This guide is designed for beginners, with all the guidance you need to understand the fundamentals of harnessing data analytics for your business. So even if you have never heard about data analytics until today, I promise we will walk through this step-by-step. By the end of this, you'll be able to think analytically and make informed business decisions. This book illustrates how EASY it is to find success by just applying a few principles. So stop reading this description, and start reading Data Analytics for Businesses instead. Scroll up, and CLICK BUY now!
Methods for Petroleum Well Optimization
Author: Rasool Khosravanian
language: en
Publisher: Gulf Professional Publishing
Release Date: 2021-09-22
Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning andbig data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesiveresource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methodsfor Petroleum Well Optimization: Automation and Data Solutions gives today's engineers and researchers real-time data solutionsspecific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches frommathematical to real-time data solutions through technological advances. Topics include digital well planning and construction,moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms,complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-basedreasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R,Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guidefor researchers and oil and gas engineers to take scientifically based approaches to solving real field problems. - Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter - Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, suchas digital well planning and construction through to automatic systems - Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysissoftware more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization