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jugulum r common data sense professionals projects 2021

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Textbook in PDF format Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections to determine strategy and marketing. Data scientists take data, analyze it and create models to help solve problems. You may have heard of companies having data management teams, or Chief Information Officers (CIO) or Chief Analytics Officers (CAO), etc. These are all people that work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Though an advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic statistical analysis software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data- related challenges. The goal for this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study -- it illustrates how the various topics discussed can be applied. Essentially, this book helps traditional business people to solve data related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity. Foreword Preface Acknowledgments Author The Meeting of Manju and Jim Understanding the Problem Phase 1 Problem Definition Goal Setting Organizational Cohesion Measurement Chapter 3 Analyzing the Problem and Collecting Data Phase 2 Deep Dive Analysis Data Identification and Collection Understanding the Risk and Uncertainty Risk and Uncertainty in Data Measurement Error Risk and Uncertainty Due to the Existence of Variation Risk and Uncertainty in Prediction, Diagnosis, and Decision-Making Risk and Uncertainty in Analytics Process Execution Risk and Uncertainty Due to Incomplete Information Risk and Uncertainty Due to Procrastination Creating and Analyzing Models Phase 3 Data Analysis and Model Selection Characteristics of Successful Analytics Different Types of Analytics Outcome Analysis Individualized Analytics for Eat Healthy Problem Project Structure Data Science Project Structure Six Sigma Process-Oriented Approach Data Science Stories Case Example 1: Proactive Detection and Diagnosis of Overall Health Case Example 2: Improving Customer Satisfaction by Building a Predictive Model Concept Review Concept Review Phase 1: Understanding the Problem Phase 2: Analyzing the Problem and Collecting Data Phase 3: Creating and Analyzing Models Project Structure Manju and Jim’s Concluding Meeting References Index

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jugulum r common data sense professionals projects 2021