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| degree in business analytics: How to Start a Business Analyst Career Laura Brandenburg, 2015-01-02 You may be wondering if business analysis is the right career choice, debating if you have what it takes to be successful as a business analyst, or looking for tips to maximize your business analysis opportunities. With the average salary for a business analyst in the United States reaching above $90,000 per year, more talented, experienced professionals are pursuing business analysis careers than ever before. But the path is not clear cut. No degree will guarantee you will start in a business analyst role. What's more, few junior-level business analyst jobs exist. Yet every year professionals with experience in other occupations move directly into mid-level and even senior-level business analyst roles. My promise to you is that this book will help you find your best path forward into a business analyst career. More than that, you will know exactly what to do next to expand your business analysis opportunities. |
| degree in business analytics: A First Course in Machine Learning Simon Rogers, Mark Girolami, 2016-10-14 Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ |
| degree in business analytics: Seven Steps to Mastering Business Analysis Barbara A. Carkenord, 2009 This book provides a how to approach to mastering business analysis work. It will help build the skill sets of new analysts and all those currently doing analysis work, from project managers to project team members such as systems analysts, product managers and business development professionals, to the experienced business analyst. It also covers the tasks and knowledge areas for the new 2008 v.2 of The Guide to the Business Analysis Body of Knowledge (BABOK) and will help prepare business analysts for the HBA CBAP certification exam.--BOOK JACKET. |
| degree in business analytics: Big Data Analytics in Cybersecurity Onur Savas, Julia Deng, 2017-09-18 Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators. Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include: Network forensics Threat analysis Vulnerability assessment Visualization Cyber training. In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined. The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research. |
| degree in business analytics: Business Analytics for Managers Gert Laursen, Jesper Thorlund, 2010-07-13 While business analytics sounds like a complex subject, this book provides a clear and non-intimidating overview of the topic. Following its advice will ensure that your organization knows the analytics it needs to succeed, and uses them in the service of key strategies and business processes. You too can go beyond reporting!—Thomas H. Davenport, President's Distinguished Professor of IT and Management, Babson College; coauthor, Analytics at Work: Smarter Decisions, Better Results Deliver the right decision support to the right people at the right time Filled with examples and forward-thinking guidance from renowned BA leaders Gert Laursen and Jesper Thorlund, Business Analytics for Managers offers powerful techniques for making increasingly advanced use of information in order to survive any market conditions. Take a look inside and find: Proven guidance on developing an information strategy Tips for supporting your company's ability to innovate in the future by using analytics Practical insights for planning and implementing BA How to use information as a strategic asset Why BA is the next stepping-stone for companies in the information age today Discussion on BA's ever-increasing role Improve your business's decision making. Align your business processes with your business's objectives. Drive your company into a prosperous future. Taking BA from buzzword to enormous value-maker, Business Analytics for Managers helps you do it all with workable solutions that will add tremendous value to your business. |
| degree in business analytics: An Introduction to Business Analytics Ger Koole, 2019 Business Analytics (BA) is about turning data into decisions. This book covers the full range of BA topics, including statistics, machine learning and optimization, in a way that makes them accessible to a broader audience. Decision makers will gain enough insight into the subject to have meaningful discussions with machine learning specialists, and those starting out as data scientists will benefit from an overview of the field and take their first steps as business analytics specialist. Through this book and the various exercises included, you will be equipped with an understanding of BA, while learning R, a popular tool for statistics and machine learning. |
| degree in business analytics: R for Business Analytics A Ohri, 2012-09-14 This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications. Includes interviews with corporate users of R, and easy-to-use examples. |
| degree in business analytics: Managers Not MBAs Henry Mintzberg, 2005-06-02 In this sweeping critique of how managers are educated and how, as a consequence, management is practiced, Henry Mintzberg offers thoughtful and controversial ideas for reforming both. “The MBA trains the wrong people in the wrong ways with the wrong consequences,” Mintzberg writes. “Using the classroom to help develop people already practicing management is a fine idea, but pretending to create managers out of people who have never managed is a sham.” Leaders cannot be created in a classroom. They arise in context. But people who already practice management can significantly improve their effectiveness given the opportunity to learn thoughtfully from their own experience. Mintzberg calls for a more engaging approach to managing and a more reflective approach to management education. He also outlines how business schools can become true schools of management. |
| degree in business analytics: Predictive Business Analytics Lawrence Maisel, Gary Cokins, 2013-09-26 Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. |
| degree in business analytics: Business Analytics Using R - A Practical Approach Umesh R Hodeghatta, Umesha Nayak, 2016-12-27 Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals. |
| degree in business analytics: Business Intelligence and Analytics in Small and Medium Enterprises Pedro Novo Melo, Carolina Machado, 2019-11-26 Technological developments in recent years have been tremendous. This evolution is visible in companies through technological equipment, computerized procedures, and management practices associated with technologies. One of the management practices that is visible is related to business intelligence and analytics (BI&A). Concepts such as data warehousing, key performance indicators (KPIs), data mining, and dashboards are changing the business arena. This book aims to promote research related to these new trends that open up a new field of research in the small and medium enterprises (SMEs) area. Features Focuses on the more recent research findings occurring in the fields of BI&A Conveys how companies in the developed world are facing today's technological challenges Shares knowledge and insights on an international scale Provides different options and strategies to manage competitive organizations Addresses several dimensions of BI&A in favor of SMEs |
| degree in business analytics: Business Analytics James Abdey, 2023-11-25 Through a unique combination of data visualisation and analytics (both theoretical and applied), this ground-breaking textbook provides you with the expertise to analyse, interpret and communicate data with confidence, to inform real-world decision making. |
| degree in business analytics: Business Analytics Richard Vidgen, Sam Kirshner, Felix Tan, 2019-09-28 This exciting new textbook offers an accessible, business-focused overview of the key theoretical concepts underpinning modern data analytics. It provides engaging and practical advice on using the key software tools, including SAS Visual Analytics, R and DataRobot, that are used in organisations to help make effective data-driven decisions. Combining theory with hands-on practical examples, this essential text includes cutting edge coverage of new areas of interest including social media analytics, design thinking and the ethical implications of using big data. A wealth of learning features including exercises, cases, online resources and data sets help students to develop analytic problem-solving skills. With its management perspective on analytics and its coverage of a range of popular software tools, this is an ideal essential text for upper-level undergraduate, postgraduate and MBA students. It is also ideal for practitioners wanting to understand the broader organisational context of big data analysis and to engage critically with the tools and techniques of business analytics. Accompanying online resources for this title can be found at bloomsburyonlineresources.com/business-analytics. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost. |
| degree in business analytics: Introduction to Business Analytics Using Simulation Jonathan P. Pinder, 2022-02-06 Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. - Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making - Explains the processes needed to develop, report and analyze business data - Describes how to use and apply business analytics software - Offers expanded coverage on the value and application of prescriptive analytics - Includes a wealth of illustrative exercises that are newly organized by difficulty level - Winner of the 2017 Textbook and Academic Authors Association's (TAA) Most Promising New Textbook Award in the prior edition |
| degree in business analytics: Business Analytics for Professionals Alp Ustundag, Emre Cevikcan, Omer Faruk Beyca, 2022-05-09 This book explains concepts and techniques for business analytics and demonstrate them on real life applications for managers and practitioners. It illustrates how machine learning and optimization techniques can be used to implement intelligent business automation systems. The book examines business problems concerning supply chain, marketing & CRM, financial, manufacturing and human resources functions and supplies solutions in Python. |
| degree in business analytics: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 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. |
| degree in business analytics: Practical Business Analytics Using SAS Shailendra Kadre, Venkat Reddy Konasani, 2015-02-07 Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life. Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes: More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries. Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course. Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before. |
| degree in business analytics: Data Mining and Business Analytics with R Johannes Ledolter, 2013-05-28 Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences. |
| degree in business analytics: The Great Cloud Migration Michael C. Daconta, 2013 - Learn how to migrate your applications to the cloud! - Learn how to overcome your senior management's concerns about Cloud Security and Interoperability! - Learn how to explain cloud computing, big data and linked data to your organization! - Learn how to develop a robust Cloud Implementation Strategy! - Learn how a Technical Cloud Broker can ease your migration to the cloud! This book will answer the key questions that every organization is asking about emerging technologies like Cloud Computing, Big Data and Linked Data. Written by a seasoned expert and author/co-author of 11 other technical books, this book deftly guides you with real-world experience, case studies, illustrative diagrams and in-depth analysis. * How do you migrate your software applications to the cloud? This book is your definitive guide to migrating applications to the cloud! It explains all the options, tradeoffs, challenges and obstacles to the migration. It provides a migration lifecycle and process you can follow to migrate each application. It provides in-depth case studies: an Infrastructure-as-a-Service case study and a Platform-as-a-Service case study. It covers the difference between application migration and data migration to the cloud and walks you through how to do both well. It covers migration to all the major cloud providers to include Amazon Web Services (AWS), Google AppEngine and Microsoft Azure. * How do you develop a sound implementation strategy for the migration to the cloud? This book leverages Mr. Daconta's 25 years of leadership experience, from the Military to Corporate Executive teams to the Office of the CIO in the Department of Homeland Security, to guide you through the development of a practical and sound implementation strategy. The book's Triple-A Strategy: Assessment, Architecture then Action is must reading for every project lead and IT manager! * This book covers twenty migration scenarios! Application and data migration to the cloud |
| degree in business analytics: Data Mining For Dummies Meta S. Brown, 2014-09-04 Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining. |
| degree in business analytics: Business Analytics S. Christian Albright, Wayne L. Winston, 2017 |
| degree in business analytics: Business Analytics Principles, Concepts, and Applications with SAS Marc J. Schniederjans, Dara G. Schniederjans, Christopher M. Starkey, 2014-09-12 Learn everything you need to know to start using business analytics and integrating it throughout your organization. Business Analytics Principles, Concepts, and Applications with SAS brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives. They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making. Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning. Unlike most competitive guides, this text demonstrates the use of SAS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself. Business Analytics Principles, Concepts, and Applications with SAS will be a valuable resource for all beginning-to-intermediate level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research. |
| degree in business analytics: 5 Keys to Business Analytics Program Success John Boyer, Bill Frank, Brian Green, Tracy Harris, Kay Van De Vanter, 2012-11-15 A roadmap to understanding and achieving excellence in business analytics initiatives With business analytics is becoming increasingly strategic to all types of organizations and with many companies struggling to create a meaningful impact with this emerging technology, this book based on the combined experience of 10 organizations that display excellence and expertise on the subject shares the best practices, discusses the management aspects and sociology that drives success, and uncovers the five key aspects behind the success of some of the top business analytics programs in the industry. Readers will learn about numerous topics, including how to create and manage a changing business analytics strategy; align business priorities to technological innovation; quantify and demonstrate tangible business value; implement program processes that balance agility, empowerment, and control; and architecting a business analytics technology solution with future innovation in mind.This is the ideal resource for any organization that wants to learn how a business analytics program can help manage value, employees, and technology to translate strategies into actionable insight and achievement. |
| degree in business analytics: Business Analytics Mary Ellen Gordon, 2023-05-24 This new textbook focuses on how data and analytics can be used to help inform organisational decision-making across the business by complementing human judgement. Taking a highly practical approach, it covers major use cases for analytics across different business areas, including marketing analytics, HR analytics, operational analytics and financial analytics. This concise and readable book grounds discussion in the fundamentals of data, analytics and data visualisation, and in an understanding of the legal and ethical responsibilities that come with working with data. Key features include: • Analytics in Practice vignettes show how data and analytics have been applied in real organisations • Video interviews with industry professionals bring examples to life • A running case study and accompanying dataset allow you to apply what you have learnt Suitable for undergraduate and postgraduate students studying business analytics. Mary Ellen Gordon is Senior Professional Teaching Fellow/Senior Lecturer in the School of Information Systems at the Victoria University of Wellington, New Zealand. |
| degree in business analytics: A Business Analyst's Introduction to Business Analytics Adam Fleischhacker, 2020-07-20 This up-to-date business analytics textbook (published in July 2020) will get you harnessing the power of the R programming language to: manipulate and model data, discover and communicate insight, to visually communicate that insight, and successfully advocate for change within an organization. Book Description A frequent teaching-award winning professor with an analytics-industry background shares his hands-on guide to learning business analytics. It is the first textbook addressing a complete and modern business analytics workflow that includes data manipulation, data visualization, modelling business problems with graphical models, translating graphical models into code, and presenting insights back to stakeholders. Book Highlights Content that is accessible to anyone, even most analytics beginners. If you have taken a stats course, you are good to go. Assumes no knowledge of the R programming language. Provides introduction to R, RStudio, and the Tidyverse. Provides a solid foundation and an implementable workflow for anyone wading into the Bayesian inference waters. Provides a complete workflow within the R-ecosystem; there is no need to learn several programming languages or work through clunky interfaces between software tools. First book introducing two powerful R-packages - `causact` for visual modelling of business problems and `greta` which is an R interface to `TensorFlow` used for Bayesian inference. Uses the intuitive coding practices of the `tidyverse` including using `dplyr` for data manipulation and `ggplot2` for data visualization. Datasets that are freely and easily accessible. Code for generating all results and almost every visualization used in the textbook. Do not learn statistical computation or fancy math in a vacuum, learn it through this guide within the context of solving business problems. |
| degree in business analytics: Advanced Business Analytics Fausto Pedro García Márquez, Benjamin Lev, 2015-01-24 The book describes advanced business analytics and shows how to apply them to many different professional areas of engineering and management. Each chapter of the book is contributed by a different author and covers a different area of business analytics. The book connects the analytic principles with business practice and provides an interface between the main disciplines of engineering/technology and the organizational, administrative and planning abilities of management. It also refers to other disciplines such as economy, finance, marketing, behavioral economics and risk analysis. This book is of special interest to engineers, economists and researchers who are developing new advances in engineering management but also to practitioners working on this subject. |
| degree in business analytics: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| degree in business analytics: HOW TO BECOME A BUSINESS ANALYST Ranjan Kumar Barick, 2024-09-15 Introduction Welcome to How to Become a Business Analyst – your essential guide to mastering the dynamic and vital role of a business analyst in today’s ever-evolving supply chain landscape. In an era where efficiency, innovation, and strategic agility define success, business analysts are the architects behind seamless supply chain operations. This book is crafted to equip you with the knowledge, tools, and insights to excel in this exciting field. Whether you're a student embarking on a career journey or a professional seeking to pivot into supply chain analysis, this book will serve as your comprehensive roadmap. Discover the Essentials: Understand the Fundamentals: Dive deep into the core principles of supply chain management and grasp the crucial components that drive global commerce. Explore Key Players: Learn about the integral stakeholders, from suppliers to customers, and how technology and third-party logistics shape modern supply chains. Tackle Challenges: Navigate through common issues like delays and cost overruns while understanding the impact of global risks and external factors. Master the Tools and Techniques: Data Analysis Tools: Uncover how Excel, Power BI, and Tableau transform data into actionable insights. Process Mapping: Use Visio and Lucid chart to visualize and optimize supply chain processes. Demand Forecasting: Leverage SAP and Oracle to predict and manage inventory with precision. Get Inspired by Real-World Success Stories: Case Studies: Gain practical insights from real-world examples of inventory management, logistics optimization, and procurement strategies. Prepare for a Rewarding Career: Career Guidance: From educational requirements and certifications to building a standout resume, learn how to kickstart your journey as a successful business analyst. With engaging content, practical examples, and actionable strategies, this book is your gateway to becoming a proficient and influential business analyst in supply chain management. Dive in, and let’s unlock your potential to drive efficiency, innovation, and success in the world of supply chains! Embark on this journey and transform your career. Your future as a top-tier business analyst starts here! |
| degree in business analytics: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| degree in business analytics: SAP Business Analytics Sudipa DuttaRoy, 2016-11-12 Focus on SAP business analytics business gains, key features, and implementation. The book includes example implementations of SAP business analytics, the challenges faced, and the solutions implemented. SAP Business Analytics explains both the strategy and technical implementation for gathering and analyzing all the information pertaining to an organization. Key features of the book are: A 360-degree view of an organization’s data and the methods to gather and analyze that data The strategies that need to be in place to gather relevant data from disparate systems Details about the SAP business analytics suite of products The technical implementations used to gather data from disparate systems such as ERP and CRM Real business cases as examples Analytics is the driving force in today’s business, be it healthcare, marketing, telecommunications, or retail and hence the most vital part of any organization’s strategy. What You'll Learn Gain an understanding of business analytics in general Absorb the technical details of the SAP business analytics suite of products Discover the challenges faced during an enterprise-level analytics project implementation Learn the key points to be kept in mind during the technical implementation of an SAP business analytics project Who This Book Is For Analytics strategists, BI managers, BI architects, business analysts, and BI developers. |
| degree in business analytics: BUSINESS ANALYTICS PURBA HALADY RAO, 2013-07-29 Business Analytics refers to various categories of analytical approaches for modelling different business situations and arriving at solutions and strategies for optimal decision-making in marketing, finance, operations, organizational behaviour and other managerial processes. Thus, Business Analytics today refers to different approaches for modelling and arriving at assessing and predicting risk, predicting market preferences, project feasibility, customer segmentation, inherent and underlying dimensions in consumer preferences, factors leading to probability of purchase, preferred segments in financial and credit card industry, probability of attrition in large organizations, etc.The myriad of modelling and other analytical approaches which constitute Business Analytical applications in Indian Industry today include predominantly:• Determining which attributes in a product are considered significant by the market and which are found to be significantly satisfactory—Gap Analysis.• Analytical Modelling by Factor and Cluster Analysis.• Analytical Modelling by Logistics Regression and Discriminant Analysis.• Segmentation of primary target market by Heuristic Modelling such as RFM (recency, frequency, monetary) analysis.• Segmentation of target market based on large databases using Decision Tree approaches such as CHAID (Chi-square Automatic Interaction Detection) and other Classification and Regression Trees.• Determining Linkages between unobserved constructs such as customer satisfaction and factors leading to it, using Structural Equation Modelling (SEM).• Determining relative preferences in consumer perceptions by Conjoint Analysis.In this book, the author has discussed these analytical approaches following a classroom teaching format, drawing from her extensive teaching experience spanning over 30 years. The book first discusses all important concepts and then case studies are discussed which emulate real-life managerial situations.This textbook is designed to serve the needs of management students for a course in Business Analytics. |
| degree in business analytics: Business Analytics for Effective Decision Making Sumi K. V., R. Vasanthagopal, 2024-07-03 Business Analytics for Effective Decision Making is a comprehensive reference that explores the role of business analytics in driving informed decision-making. The book begins with an introduction to business analytics, highlighting its significance in today's dynamic business landscape. The subsequent chapters review various tools and software available for data analytics, addressing both the opportunities and challenges for professionals in different sectors. Readers will find practical insights and real-world case studies across diverse industries, including banking, retail, marketing, and supply chain management. Each chapter provides actionable insights and concludes with implications for implementing data-driven strategies. Key Features: Practical Examples: Real-world case studies and examples make complex concepts easy to understand. Ethical Considerations: Guidance on responsible data usage and addressing ethical implications. Comprehensive Coverage: From data collection to analysis and interpretation, the book covers all aspects of business analytics. Diverse Perspectives: Contributions from industry experts offer diverse insights into data analytics applications in business research, marketing, supply chain and the retail industry. Actionable Insights: Each chapter concludes with practical implications for implementing data-driven strategies. |
| degree in business analytics: Recent Advancements in Computational Finance and Business Analytics Rangan Gupta, |
| degree in business analytics: Profit Driven Business Analytics Wouter Verbeke, Bart Baesens, Cristian Bravo, 2017-10-09 Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques. |
| degree in business analytics: Business Analysis A-Z J. S. Sandhu, 2022-01-06 Business Analysts (BAs) are not just about gathering & managing requirements or running workshops. They are lot more than that! Until now the focus has been on business analysis tools, techniques and project delivery methodologies, rather than focusing on other important ingredients like Accountability, Leadership and Attention to Detail. They also need to show agility, be innovative and stay abreast of emerging technologies to deliver solutions that will stand the test of time. Whether you are an experienced BA, Project Manager, Consultant, Business Leader, Entrepreneur or exploring your career as a new BA – this book provides an excellent cross-section of skills (from A to Z) required to be a Superstar BA. |
| degree in business analytics: Business Analysis life cycle & IT-Business Analyst Subramanyam Gunda, 2020-03-13 I'm happy to see this book being selected, awarded and securing it's place in 100 notable books of 2020. Business Analysis life cycle & IT-Business Analyst (Role in Traditional, Digital and Agile world) book, is a quick read for Engineering, IT and Management graduates, novice and experienced Business analysts, Scrum Masters and Agile coaches, Business Architects and Business consultants. The book is beneficial for training institutes, BA nurturing programs, BA Internships, meet ups for knowledge sharing, webinar topics, in-house BA trainings, BA skill build, Scrum teams, sales team, governance teams, Center of excellence, Project management professionals and Agile practitioner's. Some key concepts you would love and enjoy reading: Traditional Business Analysis and processes Digital Business Analyst Skills and techniques for BA in DevOps environment Agile manifesto principles applied to a BA Core activities of an Agile BA Requirements cycle BA Career track and the available certifications A brief about the Enterprise Business Analysis Various Tools and techniques For reader's information: All job designation employees should read the book as a casual read and every chapter can be turned to a single book. So, enjoy the read, understand the role and it's scope and keep upskilling. You will find the content to its relevancy and post completion of reading, you can immediately relate the concepts to your job. Thank you. |
| degree in business analytics: Getting Started with Business Analytics David Roi Hardoon, Galit Shmueli, 2013-03-26 Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications. The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics. The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data. The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data. Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization. |
| degree in business analytics: Competing on Analytics Thomas H. Davenport, Jeanne G. Harris, 2007-03-06 You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics. |
| degree in business analytics: Applied Sport Business Analytics Christopher Atwater, Robert E. Baker, Ted Kwartler, 2022-02-21 Applied Sport Business Analytics With HKPropel Access provides a practical explanation of the use of data analytic metrics in sport, exploring selected techniques and tools as well as addressing fundamental applications of analytics within modern sports organizations. Current and aspiring sport managers will develop their understanding of how analytics can be used strategically to make data-informed decisions by selecting and translating data into evidence and meaningful metrics. The text begins with an introduction to the world of analytics, exploring the social, economic, and business foundations that form the history of data analytics. Different strategies used to make data-driven decisions are discussed to demonstrate the importance of analytics in a modern sport context. The text explains terms and methods that are typical in sport analytics, bridging the gap between sport managers and sport analysts to help them understand the perceptions and needs of one another. The text’s focus on quantitative statistical analysis—with its exploration of modeling, predictive analytics, and forecasting—helps students learn how to analyze data and make use of it. Students will then learn to turn data into visual representations such as cluster diagrams to reveal clear results. With practical exercises that utilize five included datasets and are heavily support by related video tutorials delivered through HKPropel, even those without programming experience will learn how to program and transform complex statistical data into easy-to-understand visuals. Case studies exploring real-world scenarios—including player position analysis in women's professional basketball, esport player popularity and market analysis, and prospective player evaluation for the NFL draft—examine managerial implications to help develop understanding of what questions to ask, how to interpret data, and how to use data to make informed decisions. Finally, an in-depth look at how cutting-edge analytics mechanisms were used to analyze over one million tweets associated with the NBA over an entire season will illustrate how to successfully work with large amounts of data to achieve results. Concepts throughout the book are made easy to understand through exercises, datasets, and video lectures on key topics, all accessible through HKPropel. These tools combine to provide valuable experience and practical understanding. Interview With a Professional sidebars offer additional real-world glimpses into the use of analytics by practitioners in sport business. Applied Sport Business Analytics will provide a broader and deeper knowledge of the use of sport analytics for aspiring sport managers, data analysts, and practitioners alike. It will prepare them to translate metrics in a useful way that allows them to make data-informed and data-driven decisions to achieve desired outcomes in their organization. Note: A code for accessing HKPropel is not included with this ebook but may be purchased separately. |
| degree in business analytics: Machine Learning Techniques for Improved Business Analytics G., Dileep Kumar, 2018-07-06 Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques. |
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