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Data Science for Business

Data Science for Business
What You Need to Know about Data Mining and Data-Analytic Thinking

by Foster Provost,Tom Fawcett

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2013-07-27
  • Pages : 414
  • ISBN : 144937428X
  • Language : En, Es, Fr & De
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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 Professionals

Data Science for Business Professionals
A Practical Guide for Beginners (English Edition)

by Probyto Data Science and Consulting Pvt. Ltd.

  • Publisher : BPB Publications
  • Release : 2020-05-06
  • Pages : 368
  • ISBN : 9389423287
  • Language : En, Es, Fr & De
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Primer into the multidisciplinary world of Data Science KEY FEATURES - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTION The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset. WHAT WILL YOU LEARN - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FOR This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science. TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business Intelligence 16. Data Visualization Tools 17. Industry Use Case 1 – FormAssist 18. Industry Use Case 2 – PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments

Data Science for Business and Decision Making

Data Science for Business and Decision Making
A Book

by Luiz Paulo Fávero,Patrícia Belfiore

  • Publisher : Academic Press
  • Release : 2019-04-11
  • Pages : 1240
  • ISBN : 0128112174
  • Language : En, Es, Fr & De
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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

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions
A Book

by Matt Taddy

  • Publisher : McGraw-Hill Education
  • Release : 2019-08-23
  • Pages : 352
  • ISBN : 9781260452778
  • Language : En, Es, Fr & De
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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

Data Science for Business

Data Science for Business
A Book

by Foster Provost

  • Publisher : Unknown Publisher
  • Release : 2013
  • Pages : 129
  • ISBN : 9781449374273
  • Language : En, Es, Fr & De
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Data Science For Dummies

Data Science For Dummies
A Book

by Lillian Pierson

  • Publisher : John Wiley & Sons
  • Release : 2021-09-15
  • Pages : 432
  • ISBN : 1119811554
  • Language : En, Es, Fr & De
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Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

Data Science for Business

Data Science for Business
Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners

by Herbert Jones

  • Publisher : Createspace Independent Publishing Platform
  • Release : 2018-09-26
  • Pages : 100
  • ISBN : 9781727618570
  • Language : En, Es, Fr & De
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Do you want to learn about data science but aren't in the mood to read a boring textbook? Data science has a huge impact on how companies conduct business, and those who don't learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers. And it is all done by collecting and sorting through a large amount of information, so you have the right sources behind you when you make a major decision. In this guidebook, you will discover more about data science and how to get started in this field. This book will discuss the following topics: What is data science? How Big Data works and why it is so important How to do an explorative data analysis Working with data mining How to mine text to get the data Some amazing machine learning algorithms to help with data science How to do data modeling Data visualization How to use data science to help your business grow Tips to help you get started with data science And much, much more! So if you are ready to get started with data science, click "add to cart"!

Data Science for Marketing Analytics

Data Science for Marketing Analytics
A Practical Guide to Forming a Killer Marketing Strategy Through Data Analysis with Python

by Mirza Rahim Baig,Gururajan Govindan,Vishwesh Ravi Shrimali

  • Publisher : Packt Publishing
  • Release : 2021-09-07
  • Pages : 636
  • ISBN : 9781800560475
  • Language : En, Es, Fr & De
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Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key Features: Use data analytics and machine learning in a sales and marketing context Gain insights from data to make better business decisions Build your experience and confidence with realistic hands-on practice Book Description: Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What You Will Learn: Load, clean, and explore sales and marketing data using pandas Form and test hypotheses using real data sets and analytics tools Visualize patterns in customer behavior using Matplotlib Use advanced machine learning models like random forest and SVM Use various unsupervised learning algorithms for customer segmentation Use supervised learning techniques for sales prediction Evaluate and compare different models to get the best outcomes Optimize models with hyperparameter tuning and SMOTE Who this book is for: This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.

Data Science

Data Science
The Ultimate Guide to Data Analytics, Data Mining, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, Big Data for Business and Machine Learning for Beginners

by Herbert Jones

  • Publisher : Unknown Publisher
  • Release : 2020-01-03
  • Pages : 134
  • ISBN : 9781647483043
  • Language : En, Es, Fr & De
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2 comprehensive manuscripts in 1 book Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying

Analytics and Data Science

Analytics and Data Science
Advances in Research and Pedagogy

by Amit V. Deokar,Ashish Gupta,Lakshmi S. Iyer,Mary C. Jones

  • Publisher : Springer
  • Release : 2017-10-05
  • Pages : 297
  • ISBN : 3319580973
  • Language : En, Es, Fr & De
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This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science.

Data Science and Digital Business

Data Science and Digital Business
A Book

by Fausto Pedro García Márquez,Benjamin Lev

  • Publisher : Springer
  • Release : 2019-01-11
  • Pages : 321
  • ISBN : 9783319956503
  • Language : En, Es, Fr & De
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This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

Data Science for Marketing Analytics

Data Science for Marketing Analytics
Achieve your marketing goals with the data analytics power of Python

by Tommy Blanchard,Debasish Behera,Pranshu Bhatnagar

  • Publisher : Packt Publishing Ltd
  • Release : 2019-03-30
  • Pages : 420
  • ISBN : 1789952107
  • Language : En, Es, Fr & De
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Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.

Data Science for Business

Data Science for Business
A Definitive Guide to Data Mining, Data Warehousing, Data Analytics, Modelling, Visualization, Regression Analysis

by Stefan Campos

  • Publisher : Stefan Campos
  • Release : 2021-04-17
  • Pages : 148
  • ISBN : 9781802280166
  • Language : En, Es, Fr & De
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55% OFF for Bookstores! NOW at $15.74 instead of $34.99! Your customers will never stop using this awesome book!

Big Data MBA

Big Data MBA
Driving Business Strategies with Data Science

by Bill Schmarzo

  • Publisher : John Wiley & Sons
  • Release : 2015-12-21
  • Pages : 312
  • ISBN : 1119181119
  • Language : En, Es, Fr & De
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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. 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. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. 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. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. 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 shows you how to implement big data and analytics to make better decisions.

Data Science for Business

Data Science for Business
The Complete Guide to Using Data Analytics and Data Mining in Business

by J. F. Thompson

  • Publisher : Createspace Independent Publishing Platform
  • Release : 2018-04-04
  • Pages : 48
  • ISBN : 9781987513332
  • Language : En, Es, Fr & De
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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.. Get Your Copy Right Now!

Predictive Analytics

Predictive Analytics
Data Mining, Machine Learning and Data Science for Practitioners

by Dursun Delen

  • Publisher : FT Press Analytics
  • Release : 2020-10-30
  • Pages : 350
  • ISBN : 9780136738510
  • Language : En, Es, Fr & De
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In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for students. Using predictive analytics techniques, students can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web mining, and for sentiment analysis Integration with cutting-edge Big Data approaches Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors.

Step Up for Leadership in Enterprise Data Science & Artificial Intelligence with Big Data

Step Up for Leadership in Enterprise Data Science & Artificial Intelligence with Big Data
Illustrations with R & Python

by Shitalkumar R Sukhdeve

  • Publisher : Independently Published
  • Release : 2020-11-27
  • Pages : 304
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Review: "I would recommend this book to all prospective data scientists - as well as those software professionals who choose to transfer or migrate to the domain of data science. It is a useful addition to the body of work already available to guide project managers of data science projects." Lt Col (Dr) Rajesh Kapur (Retd), AI Investor, Asst. Prof. TIMSCDR, Hyderabad, India "It's a masterpiece of work for the aspiring leaders of data science and AI. It's also a guide for executives and investors to get maximum value from their investment in AI. Beginners in data science can also get the most out of this book.", Jay Ojha, Business intelligence and analytics manager, HCL Infosystem LtdWhy should you read this book? 87% of data science project fails to make to production in enterprises. Only 50% is the data leadership success rate. Is it not surprising to know when data science and AI are in the top trend? If you are looking for a career in data science or looking for leadership, these insights may disturb you. Don't worry, "Step up for Leadership in Enterprise Data Science & Artificial Intelligence with Big Data." will -Burst the myths around data science, AI & big data-Presents the real business scenarios -Take you on the journey of data science, AI & big data even if you are an ultimate beginner.-Introduce you to the essential skills of success in this field -Develop a leadership mindset by cutting edge methodologies & strategies-Make you aware of technical trends around it-Develop technical skills with R, Python, Machine learning with big data as well as business skills-Reduce failure possibility and increase the chance of success by covering the 360 degrees view of the field. Each day counts. So as your steps. Step up immediately and begin your journey to your dreams of data science and AI.

Process Mining

Process Mining
Data Science in Action

by Wil M. P. van der Aalst

  • Publisher : Springer
  • Release : 2016-04-15
  • Pages : 467
  • ISBN : 3662498510
  • Language : En, Es, Fr & De
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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.

Data Science for Business

Data Science for Business
What You Need to Know about Data Mining and Data-Analytic Thinking

by Foster Provost,Tom Fawcett

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2013-07-27
  • Pages : 414
  • ISBN : 1449374298
  • Language : En, Es, Fr & De
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Annotation This broad, deep, but not-too-technical guide introduces you to 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. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.

Data Science for Business With R

Data Science for Business With R
A Book

by Jeffrey S. Saltz,Jeffrey M. Stanton

  • Publisher : SAGE Publications
  • Release : 2021-02-14
  • Pages : 424
  • ISBN : 1544370466
  • Language : En, Es, Fr & De
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Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field.