Big Data: Understanding How Data Powers Big Business

Wiley #ad - Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy it professionals and executives is how to get this done, and that's where this practical book comes in. Big data: understanding how data Powers Big Business is a complete how-to guide to leveraging big data to drive business value.

Don't miss his invaluable insights and advice. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo.

Big Data: Understanding How Data Powers Big Business #ad - Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Leverage big data to add value to your business Social media analytics, web-tracking, products, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, competition, and markets.


Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results

Wiley #ad - The best-selling author of big Data is back, this time with a unique and in-depth insight into how specific companies use big data. For each company profiled, what problem it solved and the processes put it place to make it practical, as well as the technical details, learn what data was used, challenges and lessons learned from each unique scenario.

This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. Learn how predictive analytics helps amazon, fashion, microsoft and more learn how big data is changing medicine, Target, law enforcement, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, hospitality, LinkedIn, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter .

Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results #ad - Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. From technology, to sport teams, spur innovation, learn the actual strategies and processes being used to learn about customers, media and retail, government agencies and financial institutions, improve manufacturing, improve safety and so much more.

Big data is on the tip of everyone's tongue. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly.


Big Data MBA: Driving Business Strategies with Data Science

Wiley #ad - Business stakeholders no longer need to relinquish control of data and analytics to IT. You'll learn how to exploit new sources of customer, coupled with advanced analytics and data science, and operational data, uncover monetization opportunities, product, to optimize key processes, and create new sources of competitive differentiation.

Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity.

Understand where and how to leverage big data integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use.

Big Data MBA: Driving Business Strategies with Data Science #ad - The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. In fact, they must champion the organization's data collection and analysis efforts.

Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. Big data mba shows you how to implement big data and analytics to make better decisions.


Negotiating Rationally

Free Press #ad - Drawing on their research, the authors show how we are prisoners of our own assumptions. They identify strategies to avoid these pitfalls in negotiating by concentrating on opponents’ behavior and developing the ability to recognize individual limitations and biases. A must read for business professionals.

In negotiating rationally, max Bazerman and Margaret Neale explain how to avoid the pitfalls of irrationality and gain the upper hand in negotiations. For example, to recklessly escalate previous commitments, managers tend to be overconfident, and fail to consider the tactics of the other party. They explain how to think rationally about the choice of reaching an agreement versus reaching an impasse.


Hands-On Recommendation Systems with Python: Start building powerful and personalized, recommendation engines with Python

Packt Publishing #ad - You'll use collaborative filters to make use of customer behavior data, and by the time you're fnished, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.

What you will learnget to grips with the different kinds of recommender systemsmaster data-wrangling techniques using the pandas libraryBuilding an IMDB Top 250 CloneBuild a content based engine to recommend movies based on movie metadataEmploy data-mining techniques used in building recommendersBuild industry-standard collaborative filters using powerful algorithmsBuilding Hybrid Recommenders that incorporate content based and collaborative flteringWho this book is forIf you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you.

Basic knowledge of machine learning techniques will be helpful, but not mandatory. With hands-on recommendation systems with python, learn the tools and techniques required in building various kinds of powerful recommendation systems collaborative, knowledge and content based and deploying them to the webKey FeaturesBuild industry-standard recommender systemsOnly familiarity with Python is requiredNo need to wade through complicated machine learning theory to use this bookBook DescriptionRecommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon.

Hands-On Recommendation Systems with Python: Start building powerful and personalized, recommendation engines with Python #ad - Table of contentsgetting started with recommender systemsmanipulating Data with the Pandas LibraryBuilding an IMDB Top 250 Clone with PandasBuilding Content-Based RecommendersGetting Started with Data Mining TechniquesBuilding Collaborative FiltersHybrid Recommenders. Providing good recommendations, movies, whether it's friends, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.

This book shows you how to do just that.


Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence Marketing Science

Kogan Page #ad - In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.

The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data.

Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence Marketing Science #ad - Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Practical text analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics.


Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations

Harvard Business Review Press #ad - A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could. What’s more, building good charts is quickly becoming a need-to-have skill for managers. If you’re not doing it, other managers are, and they’re getting noticed for it and getting credit for contributing to your company’s success.

In good charts, dataviz maven scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. Dataviz—the new language of businessA good visualization can communicate the nature and potential impact of information and ideas more powerfully than any other form of communication.

For a long time “dataviz” was left to specialists—data scientists and professional designers. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping. This book is much more than a set of static rules for making visualizations.

Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations #ad - It taps into both well-established and cutting-edge research in visual perception and neuroscience, as well as the emerging field of visualization science, to explore why good charts and bad ones create “feelings behind our eyes. Along the way, berinato also includes many engaging vignettes of dataviz pros, illustrating the ideas in practice.

Good charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas.


Text Analysis with R for Students of Literature Quantitative Methods in the Humanities and Social Sciences

Springer #ad - Each chapter builds on the previous as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each chapter concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale.

Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Text analysis with r for students of literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text.

Text Analysis with R for Students of Literature Quantitative Methods in the Humanities and Social Sciences #ad - The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text analysis with r for students of Literature provides a practical introduction to computational text analysis using the open source programming language R.


Modeling Projected or Forecasted Financial Statements Without a Plug! Concepts Simplified

#ad - This book is also helpful for executives and others interested in understanding and modeling financial statements. Modeling projected or forecasted financial statements without a plug - Simplified is for MBA, CFA or undergraduate finance students interested in understanding how to forecast or project financial statements into the future in Microsoft Excel or Google Sheets.

This book will teach you how to build a simple 3 statement financial model. It is a wonderful resource for students or professionals interviewing for jobs in the private equity, investment banking or hedge fund industry because it will teach you how to build a basic 3 statement financial model from scratch in about 2 hours.

This book assumes that the reader is familiar with basic accounting concepts. The reader is not expected to be an expert in Microsoft Excel or Google sheets but has to be reasonably familiar with Microsoft Excel. Once you can confidently build a simple 3 statement financial model, you can add many bells and whistles to reflect the numerous specifics of any projected 3 statement financial model.

Modeling Projected or Forecasted Financial Statements Without a Plug! Concepts Simplified #ad - Modeling projected or forecasted financial statements without a plug - Simplified is based on Senith Mathews’ experience tutoring students and executives in financial modeling over 10 years and building models as a management consultant with Arthur Andersen and Mercer Management Consulting now Oliver Wyman.

For example, balance sheet and cash flow statement, the reader is expected to know the structure of an income statement, the meaning of the term working capital, etc. It does not go into the underlying accounting concepts or rules.


Ethics of Big Data: Balancing Risk and Innovation

O'Reilly Media #ad - What are your organization’s policies for generating and using huge datasets full of personal information? This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity. With this book, partners, you’ll learn how to align your actions with explicit company values and preserve the trust of customers, and stakeholders.

Review your data-handling practices and examine whether they reflect core organizational valuesExpress coherent and consistent positions on your organization’s use of big dataDefine tactical plans to close gaps between values and practices—and discover how to maintain alignment as conditions change over timeMaintain a balance between the benefits of innovation and the risks of unintended consequences.

Ethics of Big Data: Balancing Risk and Innovation #ad - Your use of data can directly affect brand quality and revenue—as Target, Apple, Netflix, and dozens of other companies have discovered. Authors kord davis and doug patterson provide methods and techniques to help your business engage in a transparent and productive ethical inquiry into your current data practices.

Both individuals and organizations have legitimate interests in understanding how data is handled.


A User's Guide to Network Analysis in R Use R!

Springer #ad - Appendices will describe the R network packages and the datasets used in the book. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, network description, data collection and management, visualization, and building and testing statistical models of networks.

As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. An r package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well. Presenting a comprehensive resource for the mastery of network analysis in R, physical,  the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, and health scientists.