Data Analytics: Become A Master In Data AnalyticsAnalyzing data is not easy, due to the fact that you have to figure out which type of data analytics you are going to use, as well as defeat the challenges that you will come up against when it comes to analyzing data.With this book, it is our goal to show you the easiest way to work with data analytics and how you are going to avoid some of the challenges and risks that you will be putting yourself up against when you are working with data.You will realize that analyzing data is not the easiest thing in the world. However, it is going to get easier the more that you practice. Just guarantee that you are taking the time to practice and do not put too much pressure on yourself.In this book, you are going to learn:The risks of data analytics The types of data analytics that are out there in the world What the decision tree is The benefits of using data analytics Real world examples that will show you how you are going to be able to take this knowledge and apply it to your everyday life.Data analysis happens no matter what line of work you are in, and it is my hope that with this book, you are able to learn everything that pushes you further in your knowledge of data analysis!Get Your Copy Today!

You're intelligent, right? So you've already figured out that Business Intelligence can be pretty valuable in making the right decisions about your business. But you’ve heard at least a dozen definitions of what it is, and heard of at least that many BI tools. Where do you start? \nBusiness Intelligence For Dummies makes BI understandable! It takes you step by step through the technologies and the alphabet soup, so you can choose the right technology and implement a successful BI environment. You'll see how the applications and technologies work together to access, analyze, and present data that you can use to make better decisions about your products, customers, competitors, and more. You’ll find out how to: \nUnderstand the principles and practical elements of BI Determine what your business needs Compare different approaches to BI Build a solid BI architecture and roadmap Design, develop, and deploy your BI plan Relate BI to data warehousing, ERP, CRM, and e-commerce Analyze emerging trends and developing BI tools to see what else may be useful \nWhether you’re the business owner or the person charged with developing and implementing a BI strategy, checking out Business Intelligence For Dummies is a good business decision.

Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. \nBig Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! \n\nOffers common use cases to help you get started Covers details on modeling, k-means clustering, and more Includes information on structuring your data Provides tips on outlining business goals and approaches \nThe future starts today with the help of Predictive Analytics For Dummies.

Become a master of data analysis, modeling, and spreadsheet use with BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 6E! This popular quantitative methods text helps you maximize your success with its proven teach-by-example approach, student-friendly writing style, and complete Excel 2016 integration. (It is also compatible with Excel 2013, 2010, and 2007.) The text devotes three online chapters to advanced statistical analysis. Chapters on data mining and importing data into Excel emphasize tools commonly used under the Business Analytics umbrella -- including Microsoft Excel's "Power BI" suite. Up-to-date problem sets and cases demonstrate how chapter concepts relate to real-world practice. In addition, the Companion Website includes data and solutions files, PowerPoint slides, SolverTable for sensitivity analysis, and the Palisade DecisionTools Suite (@RISK, BigPicture, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver).

Less than 0.5 per cent of all data is currently analysed and used. However, business leaders and managers cannot afford to be unconcerned or sceptical about data. Data is revolutionizing the way we work and it is the companies that view data as a strategic asset that will survive and thrive. Bernard Marr's Data Strategy is a must-have guide to creating a robust data strategy. Explaining how to identify your strategic data needs, what methods to use to collect the data and, most importantly, how to translate your data into organizational insights for improved business decision-making and performance, this is essential reading for anyone aiming to leverage the value of their business data and gain competitive advantage.\nPacked with case studies and real-world examples, advice on how to build data competencies in an organization and crucial coverage of how to ensure your data doesn't become a liability, Data Strategy will equip any organization with the tools and strategies it needs to profit from big data, analytics and the Internet of Things.

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

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

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Master business modeling and analysis techniques with Microsoft Excel 2016, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands on, scenario-focused guide helps you use Excel’s newest tools to ask the right questions and get accurate, actionable answers. This edition adds 150+ new problems with solutions, plus a chapter of basic spreadsheet models to make sure you’re fully up to speed. Solve real business problems with Excel—and build your competitive advantage \nQuickly transition from Excel basics to sophisticated analytics Summarize data by using PivotTables and Descriptive Statistics Use Excel trend curves, multiple regression, and exponential smoothing Master advanced functions such as OFFSET and INDIRECT Delve into key financial, statistical, and time functions Leverage the new charts in Excel 2016 (including box and whisker and waterfall charts) Make charts more effective by using Power View Tame complex optimizations by using Excel Solver Run Monte Carlo simulations on stock prices and bidding models Work with the AGGREGATE function and table slicers Create PivotTables from data in different worksheets or workbooks Learn about basic probability and Bayes’ Theorem Automate repetitive tasks by using macros

Marc Andreesen once said that "markets that don't exist don't care how smart you are." Whether you're a startup founder trying to disrupt an industry, or an intrapreneur trying to provoke change from within, your biggest risk is building something nobody wants.Lean Analytics can help. By measuring and analyzing as you grow, you can validate whether a problem is real, find the right customers, and decide what to build, how to monetize it, and how to spread the word. Focusing on the One Metric That Matters to your business right now gives you the focus you need to move ahead--and the discipline to know when to change course.Written by Alistair Croll (Coradiant, CloudOps, Startupfest) and Ben Yoskovitz (Year One Labs, GoInstant), the book lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an invaluable, practical guide for Lean Startup practitioners everywhere.