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
data science for business
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Data Science For Business: The Complete Guide To Using Data Analytics and Data Mining in Business I want to thank you and congratulate you for downloading the book, “Data Science For Business: The Complete Guide to Using Data Analytics and Data Mining in Business.” How do you define the success of a company? It could be by the number of employees or level of employee satisfaction. Perhaps the size of the customer base is a measure of success or the annual sales numbers. How does management play a role in the operational success of the business? How critical is it to have a data scientist to help determine what’s important? Is fiscal responsibility a factor of success? To determine what makes a business successful, it is important to have the necessary data about these various factors. You might be looking to get a better grasp of data analytics so as to use in your own business. Alternatively, you might be looking for more information on the field so as to launch a career as a data analyst. Well, this book can certainly help you out in both ways. Here Is A Preview Of What You'll Learn... Data Analytics Explained Methodologies of Data Analytics Importance of Data Data Science and Data Analytics Foundation of Data Analytics Data Gathering and Mining Connecting Data Analytics to the Outcomes of a Business And Much Much More.. Hurry! Get Your Copy Right Now!
Basic data science explained Explore the field of data science, and the way to analyze big and small data. This technical book goes over the main aspects of analyzing data correctly by using various strategies you need to implement in order to get results that are precise and beneficial. Learn about: Modeling data and visualization. The three V's of big data and what to do with them. Software recommendations and applications. Machine algorithms and interesting side notes regarding them. Rules, infrastructure, adaptation, and other techniques. Perception and cognition basics that apply to data. Efficient uses of regression, database querying, machine learning, and data warehousing. Curious yet? Then don't wait and start reading, so you don't have to remain in the dark. Save yourself the time and learn from what worked for me. I will see you in the first chapter!
Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here's what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there--let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.
Data Science For Business: How To Use Data Analytics and Data Mining in Business, Big Data For Business What defines the success of a business? Is it the number of people employed by the firm? Is it the sales turnover of the business? Is it the strength of the customer base of the firm? Does employee satisfaction play a role in the success of business operations? How does management factor into the overall operational success? How critical is the role of a data scientist in this process? Finally, does fiscal responsibility play any role in the success of any business? Data has currently become part and parcel of our everyday lives. It provides us with hidden facts and in-depth meaning through scientific experiments and developing algorithms, making use of all the available knowledge of acquired domains. When it comes to data, there is no longer a shortage. There may even be an excess of data if you consider the traffic going through social media, real-time market feeds, transaction details, and elsewhere. The volume of data available for use in the finance sector is almost explosive. Its variety has also expanded, and even the velocity at which the data becomes accessible has sharply risen. This scenario can either take organizations to heights unknown before or leave them dumbfounded from the feeling of overwhelm caused by the data influx. Given that organizations are in business to succeed, they have learned that the best way to utilize this flood of data is to engage data scientists. A data scientist is a guru who takes the data, explores it from all possible angles, and makes inferences that ultimately help him or her make very informed discoveries. Here Is A Preview Of What You'll Learn... Data Science Explained How to Undertake Data Science The Mastery of Data Science Art Techniques to Apply in Data Science Visualizing the Data Application of Big Data in Data Science How to Use Data Science Appropriately And Much Much More.. Get Your Copy Right Now!
This new edition sees the inclusion of 70% new material, including eight new case studies, that brings this best selling title up to date with the many advances made in the field since its original publication. In the text all the methods described are either computational or of a statistical modelling nature; complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of both students and industry professionals.
★This book includes 2 Manuscripts★ 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.
Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs
5,600 Exam Prep questions and answers. Ebooks, Textbooks, Courses, Books Simplified as questions and answers by Rico Publications. Very effective study tools especially when you only have a limited amount of time. They work with your textbook or without a textbook and can help you to review and learn essential terms, people, places, events, and key concepts.
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.