You use APIs and third-party services to automate the extraction of data from a web analytics tool. But what about automating the sending of data too? In this hands-on talk, Damion shows you how to use services like IFTTT and Zapier to augment the clickstream with context.

Let's be blunt: the way most practitioners and consultants approach digital analytics is defunct and in need of a radical overhaul. We were expected to dive in the depth of an ocean of data and find the "precious"? - the one and unique golden nugget of wisdom that would transform the organization. We've been told to "get executive sponsorship" and our goal should be to get a seat at the "grown up" table of management. In a nice echo chamber marketers despised IT and gave themselves pat on the back reinforcing the idea they were destined to become the CTOs, CMOs and CEOs of the future. Seriously? How is it going for you?

A lot of APIs - both free and paid - can be used to enrich data tracked in web analytics products and this could also include your own created internal APIs to access relevant data, reacting to realtime interactions of your users as they browse. This session dives into some practical examples of doing just that, using R and tag management systems in connection with generally used web technologies.

With "Tabular Modeling with SQL Server 2016 Analysis Services Cookbook," you'll discover how to harness the full potential of the latest Tabular models in SQL Server Analysis Services (SSAS). This practical guide equips data professionals with the tools, techniques, and knowledge to optimize data analytics and deliver fast, reliable, and impactful business insights. What this Book will help me do Understand the fundamentals of Tabular modeling and its advantages over traditional methods. Use SQL Server 2016 SSAS features to build and deploy Tabular models tailored to business needs. Master DAX for creating powerful calculated fields and optimized measures. Administer and secure your models effectively, ensuring robust BI solutions. Optimize performance and explore advanced features in Tabular solutions for maximum efficiency. Author(s) None Wilson is an experienced SQL BI professional with a strong background in database modeling and analytics. With years of hands-on experience in developing BI solutions, Wilson takes a practical and straightforward teaching approach. Their guidance in this book makes the complex topics of Tabular modeling and SSAS accessible to both seasoned professionals and newcomers to the field. Who is it for? This book is tailored for SQL BI professionals, database architects, and data analysts aiming to leverage Tabular models in SQL Server Analysis Services. It caters to those familiar with database management and basic BI concepts who are eager to improve their analysis solutions. It's a valuable resource if you aim to gain expertise in using tabular modeling for business intelligence.

This IBM® Redbooks® publication describes the concepts, architecture, and implementation of the IBM DS8880 family. The book provides reference information to assist readers who need to plan for, install, and configure the DS8880 systems. The IBM DS8000® family is a high-performance, high-capacity, highly secure, and resilient series of disk storage systems. The DS8880 family is the latest and most advanced of the DS8000 offerings to date. The high availability, multiplatform support, including IBM z Systems®, and simplified management tools help provide a cost-effective path to an on-demand world. The IBM DS8880 family now offers business-critical, all-flash, and hybrid data systems that span a wide range of price points: DS8884 -- Business Class DS8886 -- Enterprise Class DS8888 -- Analytics Class The DS8884 and DS8886 are available as either hybrid models, or can be configured as all-flash. Each model represents the most recent in this series of high-performance, high-capacity, flexible, and resilient storage systems. These systems are intended to address the needs of the most demanding clients. Two powerful IBM POWER8® processor-based servers manage the cache to streamline disk I/O, maximizing performance and throughput. These capabilities are further enhanced with the availability of the second generation of high-performance flash enclosures (HPFEs Gen-2). Like its predecessors, the DS8880 supports advanced disaster recovery (DR) solutions, business continuity solutions, and thin provisioning. All disk drives in the DS8880 storage system include the Full Disk Encryption (FDE) feature. The DS8880 can automatically optimize the use of each storage tier, particularly flash drives and flash cards, through the IBM Easy Tier® feature. The DS8880 also includes the Copy Services Manager code and allows for easier integration in a Lightweight Directory Access Protocol (LDAP) infrastructure.

Summary

There is a vast constellation of tools and platforms for processing and analyzing your data. In this episode Matthew Rocklin talks about how Dask fills the gap between a task oriented workflow tool and an in memory processing framework, and how it brings the power of Python to bear on the problem of big data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Matthew Rocklin about Dask and the Blaze ecosystem.

Interview with Matthew Rocklin

Introduction How did you get involved in the area of data engineering? Dask began its life as part of the Blaze project. Can you start by describing what Dask is and how it originated? There are a vast number of tools in the field of data analytics. What are some of the specific use cases that Dask was built for that weren’t able to be solved by the existing options? One of the compelling features of Dask is the fact that it is a Python library that allows for distributed computation at a scale that has largely been the exclusive domain of tools in the Hadoop ecosystem. Why do you think that the JVM has been the reigning platform in the data analytics space for so long? Do you consider Dask, along with the larger Blaze ecosystem, to be a competitor to the Hadoop ecosystem, either now or in the future? Are you seeing many Hadoop or Spark solutions being migrated to Dask? If so, what are the common reasons? There is a strong focus for using Dask as a tool for interactive exploration of data. How does it compare to something like Apache Drill? For anyone looking to integrate Dask into an existing code base that is already using NumPy or Pandas, what does that process look like? How do the task graph capabilities compare to something like Airflow or Luigi? Looking through the documentation for the graph specification in Dask, it appears that there is the potential to introduce cycles or other bugs into a large or complex task chain. Is there any built-in tooling to check for that before submitting the graph for execution? What are some of the most interesting or unexpected projects that you have seen Dask used for? What do you perceive as being the most relevant aspects of Dask for data engineering/data infrastructure practitioners, as compared to the end users of the systems that they support? What are some of the most significant problems that you have been faced with, and which still need to be overcome in the Dask project? I know that the work on Dask is largely performed under the umbrella of PyData and sponsored by Continuum Analytics. What are your thoughts on the financial landscape for open source data analytics and distributed computation frameworks as compared to the broader world of open source projects?

Keep in touch

@mrocklin on Twitter mrocklin on GitHub

Links

http://matthewrocklin.com/blog/work/2016/09/22/cluster-deployments?utm_source=rss&utm_medium=rss https://opendatascience.com/blog/dask-for-institutions/?utm_source=rss&utm_medium=rss Continuum Analytics 2sigma X-Array Tornado

Website Podcast Interview

Airflow Luigi Mesos Kubernetes Spark Dryad Yarn Read The Docs XData

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

As we are stepping into 4th industrial age, we must prepare our HR4.0 workforce to use new age tools to fix Employee engagement. We will build-up a case for need of an AI in HR and discuss some considerations we must take to build a powerful and scalable system. We will spend some time discussing one of the ways TAO.ai is solving employee engagement and preparing powerful AI to work with new age tools, talent, technologies and techniques to empower workers with best decision making support system.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy

Pop psychology is fun, if not that useful. Pop analytics can be dangerous! What IS pop analytics? It's a term coined (as far as we can tell) by analytics legend Kevin Hillstrom, and we managed to get him on the show to chat about it! The fact that it turned into a therapy session for Tim was just an added bonus. NOTE: We hit a glitch with Kevin's audio 45 minutes into the episode and have done our best to work around it. It was especially painful, in that he had some very nice things to say about the show, but, alas, the choppy audio means we won't be able to repurpose the clip for marketing purposes! We apologize for the glitch. It was something we didn't recognize for what it was when it happened, but now we know! See the show notes, links, and transcription at: http://www.analyticshour.io/2017/01/17/054-pop-analytics-with-kevin-hillstrom/.

In this podcast, Kevin Sonsky reveals the secrets to his success as a business intelligence leader at Citrix Systems. During the past 11 years, he has implemented an enterprise-wide self-service reporting environment that has delivered deeper insights into customer purchasing behavior. At the same time, he has established a grassroots governance program that has successfully standardized on dozens of key enterprise metrics and reports. Kevin is interviewed by Wayne W. Eckerson, long-time thought leader in the business analytics field.

Summary

Do you wish that you could track the changes in your data the same way that you track the changes in your code? Pachyderm is a platform for building a data lake with a versioned file system. It also lets you use whatever languages you want to run your analysis with its container based task graph. This week Daniel Whitenack shares the story of how the project got started, how it works under the covers, and how you can get started using it today!

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Daniel Whitenack about Pachyderm, a modern container based system for building and analyzing a versioned data lake.

Interview with Daniel Whitenack

Introduction How did you get started in the data engineering space? What is pachyderm and what problem were you trying to solve when the project was started? Where does the name come from? What are some of the competing projects in the space and what features does Pachyderm offer that would convince someone to choose it over the other options? Because of the fact that the analysis code and the data that it acts on are all versioned together it allows for tracking the provenance of the end result. Why is this such an important capability in the context of data engineering and analytics? What does Pachyderm use for the distribution and scaling mechanism of the file system? Given that you can version your data and track all of the modifications made to it in a manner that allows for traversal of those changesets, how much additional storage is necessary over and above the original capacity needed for the raw data? For a typical use of Pachyderm would someone keep all of the revisions in perpetuity or are the changesets primarily just useful in the context of an analysis workflow? Given that the state of the data is calculated by applying the diffs in sequence what impact does that have on processing speed and what are some of the ways of mitigating that? Another compelling feature of Pachyderm is the fact that it natively supports the use of any language for interacting with your data. Why is this such an important capability and why is it more difficult with alternative solutions?

How did you implement this feature so that it would be maintainable and easy to implement for end users?

Given that the intent of using containers is for encapsulating the analysis code from experimentation through to production, it seems that there is the potential for the implementations to run into problems as they scale. What are some things that users should be aware of to help mitigate this? The data pipeline and dependency graph tooling is a useful addition to the combination of file system and processing interface. Does that preclude any requirement for external tools such as Luigi or Airflow? I see that the docs mention using the map reduce pattern for analyzing the data in Pachyderm. Does it support other approaches such as streaming or tools like Apache Drill? What are some of the most interesting deployments and uses of Pachyderm that you have seen? What are some of the areas that you are looking for help from the community and are there any particular issues that the listeners can check out to get started with the project?

Keep in touch

Daniel

Twitter – @dwhitena

Pachyderm

Website

Free Weekend Project

GopherNotes

Links

AirBnB RethinkDB Flocker Infinite Project Git LFS Luigi Airflow Kafka Kubernetes Rkt SciKit Learn Docker Minikube General Fusion

The intro and outro music is from The Hug by The Freak Fandango Or

In this session, Juan F. Gorricho, Chief Data & Analytics Officer, PFCU / Walt Disney, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey as an analytics executive, best practices, hacks for upcoming executives, and some challenges/opportunities he's observing as a Chief Data & Analytics Officer. Juan discussed creating a data-driven culture and what leaders could do to get buy-ins for building strong data science capabilities.

Timeline: 0:29 Juan's journey. 4:57 Defining International society of chief data officers. 7:08 Joining International society of CDO. 7:45 Being in a credit union and being a CDO. 10:33 Hacks to creating a data-driven culture. 16:25 Being a partner of Walt Disney. 19:20 Data sharing with Disney. 21:50 Data officers vs. analytics officer. 25:59 Getting the leadership onboard on data. 30:44 The business decision making of a CDO at PFCU. 33:33 Collaboration with IT. 37:48 Challenges Juan faces in his current role. 45:03 Building data solutions or buying data solutions? 49:05 Advice for data leaders.

Podcast link: https://futureofdata.org/analyticsweek-leadership-podcast-with-juan-f-gorricho-disney/

Here's Juan F. Gorricho Bio: Juan F. Gorricho is currently the Chief Data & Analytics Officer for Partners Federal Credit Union. In this role, Juan leads the data and analytics strategy development and execution for Partners, one of the top credit unions in the country, exclusively serving the more than 100,000 cast members of The Walt Disney Company. Juan has more than 15 years of experience in the data and analytics space, including multiple speaking engagements. In his prior roles with Disney, Juan led multiple multimillion-dollar projects to implement business intelligence and analytical solutions for key lines of business such as Labor Operations and Merchandise. Juan has an Industrial Engineering degree from Universidad de los Andes in Bogotá, Colombia, and an MBA from the Darden Graduate School of Business Administration at the University of Virginia. Juan is married and lives with his wife and two children in Orlando, Florida, United States of America.

Follow @jgorricho

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Good UX is based on evidence. Qualitative evidence, such as user testing and field research, can only get you so far. To get the full picture of how users are engaging with your website or app, you'll need to use quantitative evidence in the form of analytics. This book will show you, step by step, how you can use website and app analytics data to inform design choices and definitively improve user experience. Offering practical guidelines, with plenty of detailed examples, this book covers: why you need to gather analytics data for your UX projects getting set up with analytics tools analyzing data how to find problems in your analytics using analytics to aid user research, measure and report on outcomes By the end of this book, you'll have a strong understanding of the important role analytics plays in the UX process. It will inspire you to take an "analytics first" approach to your UX projects.

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

For one- and two-semester courses in introductory business statistics. Understand Business. Understand Data. The 3rd Edition of Statistics for Business: Decision Making and Analysis emphasizes an application-based approach, in which readers learn how to work with data to make decisions. In this contemporary presentation of business statistics, readers learn how to approach business decisions through a 4M Analytics decision making strategy—motivation, method, mechanics and message—to better understand how a business context motivates the statistical process and how the results inform a course of action. Each chapter includes hints on using Excel, Minitab Express, and JMP for calculations, pointing the reader in the right direction to get started with analysis of data. Also available with MyLab Statistics MyLab™ Statistics from Pearson is the world’s leading online resource for teaching and learning statistics; it integrates interactive homework, assessment, and media in a flexible, easy-to-use format. MyLab Statistics is a course management system that helps individual students succeed. It provides engaging experiences that personalize, stimulate, and measure learning for each student. Tools are embedded to make it easy to integrate statistical software into the course. Note: You are purchasing a standalone product; MyLab™does not come packaged with this content. Students, if interested in purchasing this title with MyLab, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab, search for: 0134763734 / 9780134763736 Statistics for Business: Decision Making and Analysis, Student Value Edition Plus MyLab Statistics with Pearson eText - Access Card Package, 3/e Package consists of: 0134497260 / 9780134497266 Statistics for Business: Decision Making and Analysis, Student Value Edition 0134748646 / 9780134748641 MyLab Statistics for Business Stats with Pearson eText - Standalone Access Card - for Statistics for Business: Decision Making and Analysis

The success or failure of businesses often depends on how well organizations use their data assets for competitive advantage. Deeper insights from data require better information technology. As organizations modernize their IT infrastructure to boost innovation rather than limit it, they need a data storage system that can keep pace with highly virtualized environments, cloud computing, mobile and social systems of engagement, and in-depth, real-time analytics. Making the correct decision on storage investment is critical. Organizations must have enough storage performance and agility to innovate as they need to implement cloud-based IT services, deploy virtual desktop infrastructure, enhance fraud detection, and use new analytics capabilities. At the same time, future storage investments must lower IT infrastructure costs while helping organizations to derive the greatest possible value from their data assets. The IBM® FlashSystem V9000 is the premier, fully integrated, Tier 1, all-flash offering from IBM. It has changed the economics of today’s data center by eliminating storage bottlenecks. Its software-defined storage features simplify data management, improve data security, and preserve your investments in storage. The IBM FlashSystem® V9000 SAS expansion enclosures provide new tiering options with read-intensive SSDs or nearline SAS HDDs. IBM FlashSystem V9000 includes IBM FlashCore® technology and advanced software-defined storage available in one solution in a compact 6U form factor. IBM FlashSystem V9000 improves business application availability. It delivers greater resource utilization so you can get the most from your storage resources, and achieve a simpler, more scalable, and cost-efficient IT Infrastructure. This IBM Redbooks® publication provides information about IBM FlashSystem V9000 Software V7.7 and introduces the recently announced V7.8. It describes the product architecture, software, hardware, and implementation, and provides hints and tips. It illustrates use cases and independent software vendor (ISV) scenarios that demonstrate real-world solutions, and also provides examples of the benefits gained by integrating the IBM FlashSystem storage into business environments. This book offers IBM FlashSystem V9000 scalability concepts and guidelines for planning, installing, and configuring, which can help environments scale up and out to add more flash capacity and expand virtualized systems. Port utilization methodologies are provided to help you maximize the full potential of IBM FlashSystem V9000 performance and low latency in your scalable environment. This book is intended for pre-sales and post-sales technical support professionals, storage administrators, and anyone who wants to understand how to implement this exciting technology.

Mastering Text Mining with R is your go-to guide for learning how to process and analyze textual data using R. Throughout the book, you'll gain the skills necessary to perform data extraction and natural language processing, equipping you with practical applications tailored to real-world scenarios. What this Book will help me do Learn to access and manipulate textual data from various sources using R. Understand text processing techniques and employ them with tools like OpenNLP. Explore methods for text categorization, reduction, and summarization with hands-on exercises. Perform text classification tasks such as sentiment analysis and entity recognition. Build custom applications using text mining techniques and frameworks. Author(s) Ashish Kumar is a seasoned data scientist and software developer with years of experience in text analytics and the R programming language. He has a knack for explaining complex topics in an accessible and practical manner, ideal for learners embracing their text mining journey. Who is it for? This book is for anyone keen on mastering text mining with R. If you're an R programmer, data analyst, or data scientist looking to delve into text analytics, you'll find it ideal. Some familiarity with basic programming and statistics will enhance your experience, but all concepts are introduced clearly and effectively.

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.

"Tableau Cookbook - Recipes for Data Visualization" walks you through the features and tools of Tableau, one of the industry-leading platforms for building data visualizations. Using over 50 hands-on recipes, you'll learn to create professional dashboards and storyboards to effectively present data trends and patterns. What this Book will help me do Understand the Tableau interface and connect it to various data sources. Build basic and advanced charts, from bar graphs to histograms and maps. Design interactive dashboards that link multiple visual components. Utilize parameters and calculations for advanced data visualizations. Integrate multiple data sources and leverage Tableau's data blending features. Author(s) Shweta Savale brings years of experience in data visualization and analytics to her writing of this cookbook. As a Tableau expert, Shweta has taught and consulted with professionals across industries, empowering them to gain insights from data. Her step-by-step instructional style makes learning both engaging and approachable. Who is it for? This book caters to both beginners looking to learn Tableau from scratch and advanced users needing a quick reference guide. It's perfect for data professionals, analysts, and anyone seeking to visualize and interpret data effectively. If you're looking to simplify Tableau's functions or sharpen your visualization skills, this book is for you.

Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards. This book will help those familiar with Tableau software chart their journey to being a visualization expert. Pro Tableau demonstrates the power of visual analytics and teaches you how to: Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc. Write your own custom SQL, etc. Perform statistical analysis in Tableau using R Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.) What you'll learn Connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc. Leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc. Integrate Tableau with R Tell a compelling story with data by creating highly interactive dashboards Who this book is for All levels of IT professionals, from executives responsible for determining IT strategies to systems administrators, to data analysts, to decision makers responsible for driving strategic initiatives, etc. The book will help those familiar with Tableau software chart their journey to a visualization expert.

In "Apache Spark for Data Science Cookbook," you'll delve into solving real-world analytical challenges using the robust Apache Spark framework. This book features hands-on recipes that cover data analysis, distributed machine learning, and real-time data processing. You'll gain practical skills to process, visualize, and extract insights from large datasets efficiently. What this Book will help me do Master using Apache Spark for processing and analyzing large-scale datasets effectively. Harness Spark's MLLib for implementing machine learning algorithms like classification and clustering. Utilize libraries such as NumPy, SciPy, and Pandas in conjunction with Spark for numerical computations. Apply techniques like Natural Language Processing and text mining using Spark-integrated tools. Perform end-to-end data science workflows, including data exploration, modeling, and visualization. Author(s) Nagamallikarjuna Inelu and None Chitturi bring their extensive experience working with data science and distributed computing frameworks like Apache Spark. Nagamallikarjuna specializes in applying machine learning algorithms to big data problems, while None has contributed to various big data system implementations. Together, they focus on providing practitioners with practical and efficient solutions. Who is it for? This book is primarily intended for novice and intermediate data scientists and analysts who are curious about using Apache Spark to tackle data science problems. Readers are expected to have some familiarity with basic data science tasks. If you want to learn practical applications of Spark in data analysis and enhance your big data analytics skills, this resource is for you.