Build powerful data analytics applications with this business intelligence tool and overcome all your business challenges Key Features Master time-saving techniques and make your QlikView development more efficient Perform geographical analysis and sentiment analysis in your QlikView applications Explore advanced QlikView techniques, tips, and tricks to deliver complex business requirements Book Description QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations. Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources. With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro. This Learning Path includes content from the following Packt products: QlikView for Developers by Miguel Angel Garcia, Barry Harmsen Mastering QlikView by Stephen Redmond Mastering QlikView Data Visualization by Karl Pover What you will learn Deliver common business requirements using advanced techniques Load data from disparate sources to build associative data models Understand when to apply more advanced data visualization Utilize the built-in aggregation functions for complex calculations Build a data architecture that supports scalable QlikView deployments Troubleshoot common data visualization errors in QlikView Protect your QlikView applications and data Who this book is for This Learning Path is designed for developers who want to go beyond their technical knowledge of QlikView and understand how to create analysis and data visualizations that solve real business needs. To grasp the concepts explained in this Learning Path, you should have a basic understanding of the common QlikView functions and some hands-on experience with the tool. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Send us a text Happy holidays from the Making Data Simple team! Enjoy a rebroadcast of a conversation with Seth Dobrin, Vice President and Chief Data Officer for IBM Analytics, as he and Al explore the strategies and people your company needs to disrupt and succeed in the year ahead. Do you or your team members need new credentials to work in data? Seth also discusses what you need in your toolkit to be a data scientist at IBM.

Show Notes 00.30 Connect with Al Martin on Twitter and LinkedIn. 01.00 Connect with Seth Dobrin on Twitter and LinkedIn. 01.40 Read "What IBM looks for in a Data Scientist" by Seth Dobrin and Jean-Francois Puget. 06.00 Learn more about GDPR.  13.00 Learn more about master data management. 13.05 Learn more about unified governance and integration.  13.25 Learn more about machine learning.  14.00 Connect and learn more about Ginni Rometty.  14.40 Learn more about cognitive computing. 19.35 Connect with Rob Thomas on Twitter and LinkedIn. 21.00 Connect with Jean-Francois Puget on Twitter and LinkedIn. Follow @IBMAnalytics Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

"Machine Learning with Apache Spark Quick Start Guide" introduces you to the fundamental concepts and tools needed to harness the power of Apache Spark for data processing and machine learning. This book combines practical examples and real-world scenarios to show you how to manage big data efficiently while uncovering actionable insights through advanced analytics. What this Book will help me do Understand the role of Apache Spark in the big data ecosystem. Set up and configure an Apache Spark development environment. Learn and implement supervised and unsupervised learning models using Spark MLlib. Apply advanced analytical algorithms to real-world big data problems. Develop and deploy real-time machine learning pipelines with Apache Spark. Author(s) None Quddus is an experienced practitioner in the fields of big data, distributed technologies, and machine learning. With a career dedicated to using advanced analytics to solve real-world problems, Quddus brings practical expertise to each topic addressed. Their approachable writing style ensures readers can apply concepts effectively, even in complex scenarios. Who is it for? This book is ideal for business analysts, data analysts, and data scientists who are eager to gain hands-on experience with big data technologies. Whether you are new to Apache Spark or looking to expand your knowledge of its machine learning capabilities, this guide provides the tools and insights necessary to achieve those goals. Technical professionals wanting to develop their skills in processing and analyzing big data will find this resource invaluable.

Dive into the intricacies of data science with 'Principles of Data Science'. This book takes you on a journey to explore, analyze, and transform data into actionable insights using mathematical models, Python programming, and machine learning concepts. With a clear and engaging style, you will progress from understanding theoretical foundations to implementing advanced techniques in real-world scenarios. What this Book will help me do Master the five critical steps in a practical data science workflow. Clean and prepare raw datasets for accurate machine learning models. Understand and apply statistical models and mathematical principles for data analysis. Build and evaluate predictive models using Python and effective metrics. Create impactful visualizations that clearly convey data insights. Author(s) Sinan Ozdemir is an expert in data science, with a background in developing and teaching advanced courses in machine learning and predictive analytics. With co-authors None Kakade and None Tibaldeschi, they bring years of hands-on experience in data science to this comprehensive guide. Their approach simplifies complex concepts, making them accessible without sacrificing depth, to empower readers to make data-driven decisions confidently. Who is it for? This book is ideal for aspiring data scientists seeking a practical introduction to the field. It's perfect for those with basic math skills looking to apply them to data science or experienced programmers who want to explore the mathematical foundation of data science. A basic understanding of Python programming will be invaluable, but the book builds up core concepts step-by-step, making it accessible to both beginners and experienced professionals.

Explore and understand data with the powerful data visualization techniques of Tableau, and then communicate insights in powerful ways Key Features Apply best practices in data visualization and chart types exploration Explore the latest version of Tableau Desktop with hands-on examples Understand the fundamentals of Tableau storytelling Book Description Graphical presentation of data enables us to easily understand complex data sets. Tableau 10 Complete Reference provides easy-to-follow recipes with several use cases and real-world business scenarios to get you up and running with Tableau 10. This Learning Path begins with the history of data visualization and its importance in today's businesses. You'll also be introduced to Tableau - how to connect, clean, and analyze data in this visual analytics software. Then, you'll learn how to apply what you've learned by creating some simple calculations in Tableau and using Table Calculations to help drive greater analysis from your data. Next, you'll explore different advanced chart types in Tableau. These chart types require you to have some understanding of the Tableau interface and understand basic calculations. You'll study in detail all dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. Last but not least, you'll learn about the power of storytelling through the creation of interactive dashboards in Tableau. Through this Learning Path, you will gain confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau. This Learning Path includes content from the following Packt products: Learning Tableau 10 - Second Edition by Joshua N. Milligan Getting Started with Tableau 2018.x by Tristan Guillevin What you will learn Build effective visualizations, dashboards, and story points Build basic to more advanced charts with step-by-step recipes Become familiar row-level, aggregate, and table calculations Dig deep into data with clustering and distribution models Prepare and transform data for analysis Leverage Tableau's mapping capabilities to visualize data Use data storytelling techniques to aid decision making strategy Who this book is for Tableau 10 Complete Reference is designed for anyone who wants to understand their data better and represent it in an effective manner. It is also used for BI professionals and data analysts who want to do better at their jobs. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.

Design, develop, and master efficient Power BI solutions for impactful business insights Key Features Get to grips with the fundamentals of Microsoft Power BI Combine data from multiple sources, create visuals, and publish reports across platforms Understand Power BI concepts with real-world use cases Book Description Microsoft Power BI Complete Reference Guide gets you started with business intelligence by showing you how to install the Power BI toolset, design effective data models, and build basic dashboards and visualizations that make your data come to life. In this Learning Path, you will learn to create powerful interactive reports by visualizing your data and learn visualization styles, tips and tricks to bring your data to life. You will be able to administer your organization's Power BI environment to create and share dashboards. You will also be able to streamline deployment by implementing security and regular data refreshes. Next, you will delve deeper into the nuances of Power BI and handling projects. You will get acquainted with planning a Power BI project, development, and distribution of content, and deployment. You will learn to connect and extract data from various sources to create robust datasets, reports, and dashboards. Additionally, you will learn how to format reports and apply custom visuals, animation and analytics to further refine your data. By the end of this Learning Path, you will learn to implement the various Power BI tools such as on-premises gateway together along with staging and securely distributing content via apps. This Learning Path includes content from the following Packt products: Microsoft Power BI Quick Start Guide by Devin Knight et al. Mastering Microsoft Power BI by Brett Powell What you will learn Connect to data sources using both import and DirectQuery options Leverage built-in and custom visuals to design effective reports Administer a Power BI cloud tenant for your organization Deploy your Power BI Desktop files into the Power BI Report Server Build efficient data retrieval and transformation processes Who this book is for Microsoft Power BI Complete Reference Guide is for those who want to learn and use the Power BI features to extract maximum information and make intelligent decisions that boost their business. If you have a basic understanding of BI concepts and want to learn how to apply them using Microsoft Power BI, then Learning Path is for you. It consists of real-world examples on Power BI and goes deep into the technical issues, covers additional protocols, and much more.

Have you ever had stakeholders complain that they're not getting the glorious insights they expect from your analytics program? Have you ever had to deliver the news that the specific data they're looking for isn't actually available with the current platforms you have implemented? Have you ever wondered if things might just be a whole lot easier if you threw your current platform out the window and started over with a new one? If you answered "yes" to any of these questions, then this might be just the episode for you. Adam "Omniman" Greco -- a co-worker at Analytics Demystified of the Kiss sister who is not a co-host of this podcast -- joined the gang to chat about the perils of unmaintained analytics tools, the unpleasant taste of stale business requirements, and the human-based factors that can contribute to keeping a tool that should be jettisoned or jettisoning a tool that, objectively, should really be kept! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This week, Kyle interviews Scott Nestler on the topic of Data Ethics. Today, no ubiquitous, formal ethical protocol exists for data science, although some have been proposed. One example is the INFORMS Ethics Guidelines. Guidelines like this are rather informal compared to other professions, like the Hippocratic Oath. Yet not every profession requires such a formal commitment. In this episode, Scott shares his perspective on a variety of ethical questions specific to data and analytics.

Use an innovative approach that relies on big data and advanced analytical techniques to analyze and improve Oracle Database performance. The approach used in this book represents a step-change paradigm shift away from traditional methods. Instead of relying on a few hand-picked, favorite metrics, or wading through multiple specialized tables of information such as those found in an automatic workload repository (AWR) report, you will draw on all available data, applying big data methods and analytical techniques to help the performance tuner draw impactful, focused performance improvement conclusions. This book briefly reviews past and present practices, along with available tools, to help you recognize areas where improvements can be made. The book then guides you through a step-by-step method that can be used to take advantage of all available metrics to identify problem areas and work toward improving them. The method presented simplifies the tuning process and solves the problem of metric overload. You will learn how to: collect and normalize data, generate deltas that are useful in performing statistical analysis, create and use a taxonomy to enhance your understanding of problem performance areas in your database and its applications, and create a root cause analysis report that enables understanding of a specific performance problem and its likely solutions. What You'll Learn Collect and prepare metrics for analysis from a wide array of sources Apply statistical techniques to select relevant metrics Create a taxonomy to provide additional insight into problem areas Provide a metrics-based root cause analysis regarding the performance issue Generate an actionable tuning plan prioritized according to problem areas Monitor performance using database-specific normal ranges ​ Who This Book Is For Professional tuners: responsible for maintaining the efficient operation of large-scale databases who wish to focus on analysis, who want to expand their repertoire to include a big data methodology and use metrics without being overwhelmed, who desire to provide accurate root cause analysis and avoid the cyclical fix-test cycles that are inevitable when speculation is used

In this episode, Wayne Eckerson asks Steve Dine about the approach needed to migrate to the Cloud and architecture required to run analytics in the Cloud. Steve Dine talks extensively about the pitfalls to avoid during Cloud migration and finishes off by saying that even though security is a big issue, most organizations will have part of their architecture in the Cloud during the next two-three years. Steve Dine is a BI and enterprise data consultant and industry thought leader who has extensive experience in designing, delivering and managing highly scalable and maintainable modern data architecture solutions.

Dive into "Hands-On Data Science with R" and embark on a journey to master the R language for practical data science applications. This comprehensive guide walks through data manipulation, visualization, and advanced analytics, preparing you to tackle real-world data challenges with confidence. What this Book will help me do Understand how to utilize popular R packages effectively for data science tasks. Learn techniques for cleaning, preprocessing, and exploring datasets. Gain insights into implementing machine learning models in R for predictive analytics. Master the use of advanced visualization tools to extract and communicate insights. Develop expertise in integrating R with big data platforms like Hadoop and Spark. Author(s) This book was written by experts in data science and R including Doug Ortiz and his co-authors. They bring years of industry experience and a desire to teach, presenting complex topics in an approachable manner. Who is it for? Designed for data analysts, statisticians, or programmers with basic R knowledge looking to dive into machine learning and predictive analytics. If you're aiming to enhance your skill set or gain confidence in tackling real-world data problems, this book is an excellent choice.

In "Hands-On Data Science with SQL Server 2017," you will discover how to implement end-to-end data analysis workflows, leveraging SQL Server's robust capabilities. This book guides you through collecting, cleaning, and transforming data, querying for insights, creating compelling visualizations, and even constructing predictive models for sophisticated analytics. What this Book will help me do Grasp the essential data science processes and how SQL Server supports them. Conduct data analysis and create interactive visualizations using Power BI. Build, train, and assess predictive models using SQL Server tools. Integrate SQL Server with R, Python, and Azure for enhanced functionality. Apply best practices for managing and transforming big data with SQL Server. Author(s) Marek Chmel and Vladimír Mužný bring their extensive experience in data science and database management to this book. Marek is a seasoned database specialist with a strong background in SQL, while Vladimír is known for his instructional expertise in analytics and data manipulation. Together, they focus on providing actionable insights and practical examples tailored for data professionals. Who is it for? This book is an ideal resource for aspiring and seasoned data scientists, data analysts, and database professionals aiming to deepen their expertise in SQL Server for data science workflows. Beginners with fundamental SQL knowledge will find it a guided entry into data science applications. It is especially suited for those who aim to implement data-driven solutions in their roles while leveraging SQL's capabilities.

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

Perspective is a good thing. We've all agonized about a misreported metric or an unsatisfying entry page analysis and had to remind ourselves that we're not exactly saving lives with our work. On this episode, though, the gang actually meanders into life-and-death territory by chatting about one of the uses of data outside of the world of digital marketing and websites and eCommerce: natural disaster preparation and response. Sherilyn Burris from Cascia Consulting joins Michael, Moe, and Tim to chat about her experiences in a variety of roles in just that area, how she uses data, how the data landscape has evolved over the past 15 years, and what she has learned about communicating data to politicians, to the media, and to the general public (which has some intriguing parallels to the communication of data in digital analytics!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

In this Episode, Wayne Eckerson asks Charles Reeves about his organization’s Internet of Things and Big Data strategy. Reeves is senior manager of BI and analytics at Graphics Packaging International, a leader in the packaging industry with hundreds of worldwide customers. He has 25 years of professional experience in IT management including nine years in reporting, analytics, and data governance.

Leverage health data into insight! Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world’s data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include: Sampling and modeling health data – both structured and unstructured Exploring health data quality Developing health administration and health data assessment procedures Identifying future health trends Analyzing high-performance health data mining models Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives. This book is part of the SAS Press program.

In this episode, Wayne Eckerson and Shakeeb Ahkter dive into DataOps. They discuss what DataOps is, the goals and principles of DataOps, and reasons to adopt a DataOps strategy. Shakeeb also reveals the benefits gained from DataOps and what tools he uses. He is the Director of Enterprise Data Warehouse at Northwestern Medicine and is responsible for direction and oversight of data management, data engineering, and analytics.

Dive into the world of distributed data processing with the 'Apache Hadoop 3 Quick Start Guide.' This comprehensive resource equips you with the knowledge needed to handle large datasets effectively using Apache Hadoop. Learn how to set up and configure Hadoop, work with its core components, and explore its powerful ecosystem tools. What this Book will help me do Understand the fundamental concepts of Apache Hadoop, including HDFS, MapReduce, and YARN, and use them to store and process large datasets. Set up and configure Hadoop 3 in both developer and production environments to suit various deployment needs. Gain hands-on experience with Hadoop ecosystem tools like Hive, Kafka, and Spark to enhance your big data processing capabilities. Learn to manage, monitor, and troubleshoot Hadoop clusters efficiently to ensure smooth operations. Analyze real-time streaming data with tools like Apache Storm and perform advanced data analytics using Apache Spark. Author(s) The author of this guide, Vijay Karambelkar, brings years of experience working with big data technologies and Apache Hadoop in real-world applications. With a passion for teaching and simplifying complex topics, Vijay has compiled his expertise to help learners confidently approach Hadoop 3. His detailed, example-driven approach makes this book a practical resource for aspiring data professionals. Who is it for? This book is ideal for software developers, data engineers, and IT professionals who aspire to dive into the field of big data. If you're new to Apache Hadoop or looking to upgrade your skills to include version 3, this guide is for you. A basic understanding of Java programming is recommended to make the most of the topics covered. Embark on this journey to enhance your career in data-intensive industries.

This expert guide, "Mastering Apache Cassandra 3.x," is designed for individuals looking to achieve scalable and fault-tolerant database deployment using Apache Cassandra. From mastering the foundational components of Cassandra architecture to advanced topics like clustering and analytics integration with Apache Spark, this book equips readers with practical, actionable skills. What this Book will help me do Understand and deploy Apache Cassandra clusters for fault-tolerant and scalable databases. Use advanced features of CQL3 to streamline database queries and operations. Optimize and configure Cassandra nodes to improve performance for demanding applications. Monitor and manage Cassandra clusters effectively using best practices. Combine Cassandra with Apache Spark to build robust data analytics pipelines. Author(s) None Ploetz and None Malepati are experienced technologists and software professionals with extensive expertise in distributed database systems and big data algorithms. They've combined their industry knowledge and teaching backgrounds to create accessible and practical guides for learners worldwide. Their collaborative work is focused on demystifying complex systems for maximum learning impact. Who is it for? This book is ideal for database administrators, software developers, and big data specialists seeking to expand their skill set into scalable data storage using Cassandra. Readers should have a basic understanding of database concepts and some programming experience. If you're looking to design robust databases optimized for modern big data use-cases, this book will serve as a valuable resource.