Your company has one definition for revenue across the organization, one definition of the customer, and one definition of sign-up. For people whose jobs are so defined by ensuring we're aligned, we can't seem to standardize on one definition for the Data Scientist. In this talk, Emilie Schario (Data Strategist-in-Residence at Amplify Partners and longtime dbt community member) proposes we lobby against the title Data Scientist, instead choosing some variation of the Core Four Data Roles: Data Analyst, Analytics Engineer, Data Engineer, and Machine Learning Engineer. Register to catch the rest of Coalesce, the Analytics Engineering Conference, at https://coalesce.getdbt.com. The Analytics Engineering Podcast is brought to you by dbt Labs.

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Abstract Making Data Simple Podcast is hosted by Al Martin, VP, IBM Expert Services Delivery, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. This week on Making Data Simple, we have Wennie Allen Business Director, Data Science and AI Elite Team and Carlo Appugliese Program Director – Data &AI, Data Science Elite Team. This week we talk about agile AI and remote data science. Carlo discusses his book, while Wennie talks about the secret sauce. Show Notes 2:56 – How do we get people to adopt AI? 4:49 – Carlo’s book 6:15 – Why do we call it agile AI? 11:12 – Six weeks to get it done! 15:07 – Where are we at with AI? 16:54 - Problems with AI today 22:05 – Secret sauce 26:31 - Process and methodology  30:22 – Talk data 34:19 – Integration, trust, and quick deployment 36:10 – Working remote 39:40 – How do you engage? Remote Data Science Website: http://ibm.biz/RemoteDataScience Agile AI Blog: http://ibm.biz/DSE-AgileAI-Blog Agile AI Book:   http://ibm.biz/DSE-AgileAI Community: http://ibm.biz/DSE-Community Chat with the Lab: http://ibm.biz/DSE-ChatWithTheLab Consultation: http://ibm.biz/DSE-Consultation Blogs:  Virtual Data Science can rise to the challenge in unprecedented times by Wennie Allen Data Science and AI from anywhere... by Carlo Appugliese Wennie on LinkedIn linkedin.com/in/wennie-allen Carlo on LinkedIn linkedin.com/in/carloappugliese Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  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.

Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You’ll start by delving into the fundamentals of data science – classes of data science problems, data science techniques and their applications – and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice. What You’ll Learn Transform business objectives into concrete problems that can be solved using data science Evaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a project Build and operate an effective interdisciplinary data science team within an organization Evaluating the progress of the team towards the business RoI Understand the important regulatory aspects that are applicable to a data science practice Who This Book Is For Technology leaders, data scientists, and project managers

Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You’ll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You’ll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You’ll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark’s latest ML library. After completing this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: Build a spectrum of supervised and unsupervised machine learning algorithms Use PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark’s machine learning library Understand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.

A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the Technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the Book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's Inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the Reader For data science practitioners at all levels. About the Authors Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Quotes Spot-on as a career resource! Captures what’s important to be successful as a data scientist. - Eric Colson, Former Data Executive at Stitch Fix, Netflix The first-of-its-kind book to discuss data science career development in a systematic way! Highly valuable and timely in a world that generates more and more data!” - Michael Li, VP of Data at Coinbase A valuable reference filled with new and useful coaching and techniques. A must-have. - Jesse Bridgewater, VP Data Science at Brightline, formerly Livongo, Twitter, eBay A great book providing frameworks and tools that help contemplate and address key problems faced by data science leaders. - Ron Kohavi, Best-selling Author, Former Executive at Airbnb, Microsoft, Amazon

In this episode of DataFramed, we speak with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma about how data science is being leveraged to increase financial inclusion.

Throughout the episode, Vishnu discusses his background, Credit Karma’s mission, how data science is being used at Credit Karma to lower the barrier to entry for financial products, how he managed a data team through rapid growth, transitioning to Google Cloud, exciting trends in data science, and more. 

Relevant links from the interview:

You can now learn data science with your team for free—try out DataCamp Professional with our 14-day free trial. Data roles at Credit KarmaCredit Karma’s mission

Dive into the world of advanced analytics and visualizations in Power BI with "Extending Power BI with Python and R". This comprehensive guide will teach you how to integrate Python and R scripting into your Power BI projects, allowing you to build data models, transform data, and create rich visualizations. Learn practical techniques to make your Power BI dashboards more interactive and insightful. What this Book will help me do Master the integration of Python and R scripts into Power BI to enhance its functionality. Learn to implement advanced data transformations and enrichments using external APIs. Create advanced visualizations and custom visuals with R for improved analytics. Perform advanced data analysis including handling missing data using Python and R. Leverage machine learning techniques within Power BI projects to extract actionable insights. Author(s) None Zavarella is a data science expert and renowned author specializing in data analytics and visualization tools. With years of experience working with Power BI, Python, and R in diverse data-driven projects, Zavarella offers a unique perspective on enhancing Power BI capabilities. Passionate about teaching, they craft clear and impactful tutorials for learners. Who is it for? This book is perfect for business intelligence professionals, data scientists, and business analysts who already use Power BI and want to augment its features with Python and R. If you have a foundational understanding of Power BI and some basic familiarity with Python and R, this book will help you explore their combined potential for advanced analytics.

We talked about:

Barbara’s background Do you need a manager or an expert? Technical and non-technical requirements for managers Importance of technical skills for managers Responsibilities and skills of a manager Importance of technical background for managers Getting involved in business development and sales Developing the team Checking team’s work Data science expert Hiring experts Who should we hire first? Can an expert build a team? Data science managers in startups Project management Ensuring that projects provide value Questions before starting a project Women in data science Finding Barbara online General advice

Link:

Barbara's LinkedIn: https://www.linkedin.com/in/barbara-sobkowiak-1a4a9568

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David is Sr. Director of Data at Lyst, and as leader of their analytics + data science teams he has followed the evolution of data roles closely over the past decade. David spends a lot of time thinking about career progression + data team structure, and in this conversation with Tristan + Julia they dive into the classic individual contributor vs manager conundrum, migrating between warehouses, and reactive vs proactive data workflows. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: Techniques for computing and plotting probabilities Statistical analysis using Scipy How to organize datasets with clustering algorithms How to visualize complex multi-variable datasets How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. About the Technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the Book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's Inside Web scraping Organize datasets with clustering algorithms Visualize complex multi-variable datasets Train a decision tree machine learning algorithm About the Reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the Author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Quotes Valuable and accessible… a solid foundation for anyone aspiring to be a data scientist. - Amaresh Rajasekharan, IBM Corporation Really good introduction of statistical data science concepts. A must-have for every beginner! - Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland A full-fledged tutorial in data science including common Python libraries and language tricks! - Jean-François Morin, Laval University This book is a complete package for understanding how the data science process works end to end. - Ayon Roy, Internshala

There are so many ways to use AI technology in retail to improve customer experience, optimise supply chains and reduce waste. Yet it seems to me that most innovations in the retail industry over the last 30 years have focused on automating labour-intensive tasks. In my personal opinion, the retail customer experience has not improved markedly in my lifetime, and in some cases, it has gotten worse. Anyone who’s ever interacted with a self-checkout machine will know what I mean. So, what is next for the retail industry and what can technology and data science do to improve efficiency and customer experience across the many disparate parts of retailing? To answer these questions, I recently spoke to Shantha Mohan who is a true expert in the field. Shantha is currently an Executive in Residence at the Integrated Innovation Institute at Carnegie Mellon University, where she co-delivers courses, contributes to curriculum design, and mentors students in their projects and practicums. Shantha is also a co-founder and long-time executive of Retail Solutions Inc (RSi) where she ran the company’s worldwide product Development team that built the products & services which made the company a leader in retail analytics solutions used by consumer packaged goods companies and retailers across the globe. She holds a PhD in Operations Management and a Bachelor of Engineering in Electronics and Communication Engineering. In this episode of Leaders of Analytics, we discuss: The applications of AI in retail with the most potential, for online and in-store shopping respectivelyThe differences between retail in developed and developing countries and how AI must be customised for different markets across the globe.The typical consequences of items being out of stock and how can AI and other relevant technologies help combat out-of-stock problems.Whether AI in retail will increase or diminish the ability for small retailers to compete, and much more.

We talked about:

Nick’s background Being a career coach Overview of the hiring process Behavioral interviews for data scientists Preparing for behavioral interviews Handling "tricky" questions Project deep dive Business context Pacing, rambling, and honesty “What’s your favorite model?” What if I haven’t worked on a project that brought $1 mln? Different questions for different levels Product-sense interviews Identifying key metrics in unfamiliar domains Tech blogs Cold emailing

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Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Tim Freestone. Tim is the founder of Alooba. Alloba is a skills assessment platform for analytics, data science and data engineering. They help businesses identify the best candidates that apply for a role within its company. Show Notes 4:46 – How do you go from economics teacher to head of business intelligence? 7:53 – Do CV’s matter anymore? 13:22 – What business problem is Alooba solving? 16:05 – Do you have any data that supports your theory? 19:01 – Why analytics, data science, data engineering? 20:26 - What do you do that others don’t? 23:50 – How does Alooba define success? 25:42 – Who’s your target client base? 32:40 –Is there a customer you can talk about? 36:24 – What does Alooba mean? Alooba  Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  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.

Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Printed in full color throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. About the Technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science. This book shows you how to get started. About the Book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications. What's Inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Printed in full color throughout About the Reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Quotes Chollet is a master of pedagogy and explains complex concepts with minimal fuss, cutting through the math with practical Python code. He is also an experienced ML researcher and his insights on various model architectures or training tips are a joy to read. - Martin Görner, Google Immerse yourself into this exciting introduction to the topic with lots of real-world examples. A must-read for every deep learning practitioner. - Sayak Paul, Carted The modern classic just got better. - Edmon Begoli, Oak Ridge National Laboratory Truly the bible of deep learning. - Yiannis Paraskevopoulos, University of West Attica

In this episode of DataFramed, we speak with Brian Campbell, Engineering Manager at Lucid Software about managing data science projects effectively and harnessing the power of collaboration. Throughout the episode, Brian discusses his background, how data leaders can become better collaborators, data science project management best practices, the type of collaborators data teams should seek out, the latest innovations in the data engineering tooling space, and more.

Relevant links from the interview:

We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyLucid’s Tech Blog

Dive into the world of scalable data processing with 'Essential PySpark for Scalable Data Analytics'. This book is a comprehensive guide that helps beginners understand and utilize PySpark to process, analyze, and draw insights from large datasets effectively. With hands-on tutorials and clear explanations, you will gain the confidence to tackle big data analytics challenges. What this Book will help me do Understand and apply the distributed computing paradigm for big data. Learn to perform scalable data ingestion, cleansing, and preparation using PySpark. Create and utilize data lakes and the Lakehouse paradigm for efficient data storage and access. Develop and deploy machine learning models with scalability in mind. Master real-time analytics pipelines and create impactful data visualizations. Author(s) None Nudurupati is an experienced data engineer and educator, specializing in distributed systems and big data technologies. With years of practical experience in the field, None brings a clear and approachable teaching style to technical topics. Passionate about empowering readers, the author has designed this book to be both practical and inspirational for aspiring data practitioners. Who is it for? This book is ideal for data professionals including data scientists, engineers, and analysts looking to scale their data analytics processes. It assumes familiarity with basic data science concepts and Python, as well as some experience with SQL-like data analysis. This is particularly suitable for individuals aiming to expand their knowledge in distributed computing and PySpark to handle big data challenges. Achieving scalable and efficient data solutions is at the core of this guide.

If you dream of using analytics to optimise your customer interactions and squeeze additional value out of your existing operations, then is episode is for you! Today, most large services businesses have established data science functions that churn out countless reports, dashboards, customer insights packs, machine learning models, forecasts and predictions. With all this information to hand, you would hope that front-line operations are making data-driven decisions across the board. But alas, many of these same businesses struggle to turn their analytics into more than glossy PowerPoint packs that describe what could be done. Often, this is because the technical implementation of data science solutions run into resource constraints or remain unsupported by IT departments. So, how can we successfully make use of our analytical output in our front-line operations without spending eons creating overly complex systems that never quite deliver? To answer this question, I recently spoke to Jason Tan who is an expert in operationalising data science solutions that deliver positive customer outcomes and real financial results. Jason Is the managing director of consulting group Data Driven Analytics and an expert in optimising customer experience, pricing and long-term customer value.         In this episode of Leaders of Analytics, we discuss: How to use analytics to optimise your customer interactionsHow to identify the most valuable data science use cases in your organisationHow Jason has created successful data science solutions around legacy IT platformsWhether you should buy off-the-shelf pricing software or build your own solution

Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models Who This Book Is For Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives

From a global pandemic to extreme weather, the events of 2020 and 2021 have caused organizations to make quick and constant adjustments to their strategy and operations. This transformation is likely to continue and have a major impact on analytics. Not only do responders to Experian's annual Global Data Management survey confirm more demand for data insights, but most of them also believe the lack of agility hurt their organization's responses to fast-changing business needs. With this O'Reilly report, you'll learn how organizations have begun to take new approaches to analytics for business reinvention and digital transformation. Chief analytics and data officers and data analytics, data science, data visualization leaders will explore converged analytics and find out how it differs from legacy and current analytics approaches. You'll see where your organization stands in its journey to convergence--and what you need to do next. This report helps you: Examine how three organizations in different industries and with different objectives have benefited from modern analytics Learn how analytics has evolved to support greater business agility at scale Examine the alignment of people, processes, tools, and data in converged analytics Learn the five stages of analytical competition and six dimensions for benchmarking maturity Explore practices that you can adopt to improve your analytics capabilities and your agility

Data Engineering with Apache Spark, Delta Lake, and Lakehouse is a comprehensive guide packed with practical knowledge for building robust and scalable data pipelines. Throughout this book, you will explore the core concepts and applications of Apache Spark and Delta Lake, and learn how to design and implement efficient data engineering workflows using real-world examples. What this Book will help me do Master the core concepts and components of Apache Spark and Delta Lake. Create scalable and secure data pipelines for efficient data processing. Learn best practices and patterns for building enterprise-grade data lakes. Discover how to operationalize data models into production-ready pipelines. Gain insights into deploying and monitoring data pipelines effectively. Author(s) None Kukreja is a seasoned data engineer with over a decade of experience working with big data platforms. He specializes in implementing efficient and scalable data solutions to meet the demands of modern analytics and data science. Writing with clarity and a practical approach, he aims to provide actionable insights that professionals can apply to their projects. Who is it for? This book is tailored for aspiring data engineers and data analysts who wish to delve deeper into building scalable data platforms. It is suitable for those with basic knowledge of Python, Spark, and SQL, and seeking to learn Delta Lake and advanced data engineering concepts. Readers should be eager to develop practical skills for tackling real-world data engineering challenges.