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| data engineer python coding questions: Elements of Programming Interviews Adnan Aziz, Tsung-Hsien Lee, Amit Prakash, 2012 The core of EPI is a collection of over 300 problems with detailed solutions, including 100 figures, 250 tested programs, and 150 variants. The problems are representative of questions asked at the leading software companies. The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns. |
| data engineer python coding questions: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| data engineer python coding questions: T-SQL Window Functions Itzik Ben-Gan, 2019-10-18 Use window functions to write simpler, better, more efficient T-SQL queries Most T-SQL developers recognize the value of window functions for data analysis calculations. But they can do far more, and recent optimizations make them even more powerful. In T-SQL Window Functions, renowned T-SQL expert Itzik Ben-Gan introduces breakthrough techniques for using them to handle many common T-SQL querying tasks with unprecedented elegance and power. Using extensive code examples, he guides you through window aggregate, ranking, distribution, offset, and ordered set functions. You’ll find a detailed section on optimization, plus an extensive collection of business solutions — including novel techniques available in no other book. Microsoft MVP Itzik Ben-Gan shows how to: • Use window functions to improve queries you previously built with predicates • Master essential SQL windowing concepts, and efficiently design window functions • Effectively utilize partitioning, ordering, and framing • Gain practical in-depth insight into window aggregate, ranking, offset, and statistical functions • Understand how the SQL standard supports ordered set functions, and find working solutions for functions not yet available in the language • Preview advanced Row Pattern Recognition (RPR) data analysis techniques • Optimize window functions in SQL Server and Azure SQL Database, making the most of indexing, parallelism, and more • Discover a full library of window function solutions for common business problems About This Book • For developers, DBAs, data analysts, data scientists, BI professionals, and power users familiar with T-SQL queries • Addresses any edition of the SQL Server 2019 database engine or later, as well as Azure SQL Database Get all code samples at: MicrosoftPressStore.com/TSQLWindowFunctions/downloads |
| data engineer python coding questions: Learn Java the Easy Way Bryson Payne, 2017-11-14 Java is the world’s most popular programming language, but it’s known for having a steep learning curve. Learn Java the Easy Way takes the chore out of learning Java with hands-on projects that will get you building real, functioning apps right away. You’ll start by familiarizing yourself with JShell, Java’s interactive command line shell that allows programmers to run single lines of code and get immediate feedback. Then, you’ll create a guessing game, a secret message encoder, and a multitouch bubble-drawing app for both desktop and mobile devices using Eclipse, an industry-standard IDE, and Android Studio, the development environment for making Android apps. As you build these apps, you’ll learn how to: -Perform calculations, manipulate text strings, and generate random colors -Use conditions, loops, and methods to make your programs responsive and concise -Create functions to reuse code and save time -Build graphical user interface (GUI) elements, including buttons, menus, pop-ups, and sliders -Take advantage of Eclipse and Android Studio features to debug your code and find, fix, and prevent common mistakes If you’ve been thinking about learning Java, Learn Java the Easy Way will bring you up to speed in no time. |
| data engineer python coding questions: Cracking the Coding Interview Gayle Laakmann McDowell, 2011 Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides: 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions. 5 Algorithm Approaches: Stop being blind-sided by tough algorithm questions, and learn these five approaches to tackle the trickiest problems. Behind the Scenes of the interview processes at Google, Amazon, Microsoft, Facebook, Yahoo, and Apple: Learn what really goes on during your interview day and how decisions get made. Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues. Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time. |
| data engineer python coding questions: Data Engineering with Python Paul Crickard, 2020-10-23 Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. |
| data engineer python coding questions: Powerful Python Aaron Maxwell, 2024-11-08 Once you've mastered the basics of Python, how do you skill up to the top 1%? How do you focus your learning time on topics that yield the most benefit for production engineering and data teams—without getting distracted by info of little real-world use? This book answers these questions and more. Based on author Aaron Maxwell's software engineering career in Silicon Valley, this unique book focuses on the Python first principles that act to accelerate everything else: the 5% of programming knowledge that makes the remaining 95% fall like dominos. It's also this knowledge that helps you become an exceptional Python programmer, fast. Learn how to think like a Pythonista: explore advanced Pythonic thinking Create lists, dicts, and other data structures using a high-level, readable, and maintainable syntax Explore higher-order function abstractions that form the basis of Python libraries Examine Python's metaprogramming tool for priceless patterns of code reuse Master Python's error model and learn how to leverage it in your own code Learn the more potent and advanced tools of Python's object system Take a deep dive into Python's automated testing and TDD Learn how Python logging helps you troubleshoot and debug more quickly |
| data engineer python coding questions: Programming Pearls Jon Bentley, 2016-04-21 When programmers list their favorite books, Jon Bentley’s collection of programming pearls is commonly included among the classics. Just as natural pearls grow from grains of sand that irritate oysters, programming pearls have grown from real problems that have irritated real programmers. With origins beyond solid engineering, in the realm of insight and creativity, Bentley’s pearls offer unique and clever solutions to those nagging problems. Illustrated by programs designed as much for fun as for instruction, the book is filled with lucid and witty descriptions of practical programming techniques and fundamental design principles. It is not at all surprising that Programming Pearls has been so highly valued by programmers at every level of experience. In this revision, the first in 14 years, Bentley has substantially updated his essays to reflect current programming methods and environments. In addition, there are three new essays on testing, debugging, and timing set representations string problems All the original programs have been rewritten, and an equal amount of new code has been generated. Implementations of all the programs, in C or C++, are now available on the Web. What remains the same in this new edition is Bentley’s focus on the hard core of programming problems and his delivery of workable solutions to those problems. Whether you are new to Bentley’s classic or are revisiting his work for some fresh insight, the book is sure to make your own list of favorites. |
| data engineer python coding questions: Cracking The Machine Learning Interview Nitin Suri, 2018-12-18 A breakthrough in machine learning would be worth ten Microsofts. -Bill Gates Despite being one of the hottest disciplines in the Tech industry right now, Artificial Intelligence and Machine Learning remain a little elusive to most.The erratic availability of resources online makes it extremely challenging for us to delve deeper into these fields. Especially when gearing up for job interviews, most of us are at a loss due to the unavailability of a complete and uncondensed source of learning. Cracking the Machine Learning Interview Equips you with 225 of the best Machine Learning problems along with their solutions. Requires only a basic knowledge of fundamental mathematical and statistical concepts. Assists in learning the intricacies underlying Machine Learning concepts and algorithms suited to specific problems. Uniquely provides a manifold understanding of both statistical foundations and applied programming models for solving problems. Discusses key points and concrete tips for approaching real life system design problems and imparts the ability to apply them to your day to day work. This book covers all the major topics within Machine Learning which are frequently asked in the Interviews. These include: Supervised and Unsupervised Learning Classification and Regression Decision Trees Ensembles K-Nearest Neighbors Logistic Regression Support Vector Machines Neural Networks Regularization Clustering Dimensionality Reduction Feature Extraction Feature Engineering Model Evaluation Natural Language Processing Real life system design problems Mathematics and Statistics behind the Machine Learning Algorithms Various distributions and statistical tests This book can be used by students and professionals alike. It has been drafted in a way to benefit both, novices as well as individuals with substantial experience in Machine Learning. Following Cracking The Machine Learning Interview diligently would equip you to face any Machine Learning Interview. |
| data engineer python coding questions: Data Teams Jesse Anderson, 2020 |
| data engineer python coding questions: Quant Job Interview Questions and Answers Mark Joshi, Nick Denson, Nicholas Denson, Andrew Downes, 2013 The quant job market has never been tougher. Extensive preparation is essential. Expanding on the successful first edition, this second edition has been updated to reflect the latest questions asked. It now provides over 300 interview questions taken from actual interviews in the City and Wall Street. Each question comes with a full detailed solution, discussion of what the interviewer is seeking and possible follow-up questions. Topics covered include option pricing, probability, mathematics, numerical algorithms and C++, as well as a discussion of the interview process and the non-technical interview. All three authors have worked as quants and they have done many interviews from both sides of the desk. Mark Joshi has written many papers and books including the very successful introductory textbook, The Concepts and Practice of Mathematical Finance. |
| data engineer python coding questions: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| data engineer python coding questions: Database Internals Alex Petrov, 2019-09-13 When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals. Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed. This book examines: Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each Storage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log Distributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns Database clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency |
| data engineer python coding questions: Think Data Structures Allen B. Downey, 2017-07-07 If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials. By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online. Use data structures such as lists and maps, and understand how they work Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree Analyze code to predict how fast it will run and how much memory it will require Write classes that implement the Map interface, using a hash table and binary search tree Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes. |
| data engineer python coding questions: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data engineer python coding questions: The Google Resume Gayle Laakmann McDowell, 2011-01-25 The Google Resume is the only book available on how to win a coveted spot at Google, Microsoft, Apple, or other top tech firms. Gayle Laakmann McDowell worked in Google Engineering for three years, where she served on the hiring committee and interviewed over 120 candidates. She interned for Microsoft and Apple, and interviewed with and received offers from ten tech firms. If you’re a student, you’ll learn what to study and how to prepare while in school, as well as what career paths to consider. If you’re a job seeker, you’ll get an edge on your competition by learning about hiring procedures and making yourself stand out from other candidates. Covers key concerns like what to major in, which extra-curriculars and other experiences look good, how to apply, how to design and tailor your resume, how to prepare for and excel in the interview, and much more Author was on Google’s hiring committee; interned at Microsoft and Apple; has received job offers from more than 10 tech firms; and runs CareerCup.com, a site devoted to tech jobs Get the only comprehensive guide to working at some of America’s most dynamic, innovative, and well-paying tech companies with The Google Resume. |
| data engineer python coding questions: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data engineer python coding questions: Ace the Programming Interview Edward Guiness, 2013-06-24 Be prepared to answer the most relevant interview questions and land the job Programmers are in demand, but to land the job, you must demonstrate knowledge of those things expected by today's employers. This guide sets you up for success. Not only does it provide 160 of the most commonly asked interview questions and model answers, but it also offers insight into the context and motivation of hiring managers in today's marketplace. Written by a veteran hiring manager, this book is a comprehensive guide for experienced and first-time programmers alike. Provides insight into what drives the recruitment process and how hiring managers think Covers both practical knowledge and recommendations for handling the interview process Features 160 actual interview questions, including some related to code samples that are available for download on a companion website Includes information on landing an interview, preparing a cheat-sheet for a phone interview, how to demonstrate your programming wisdom, and more Ace the Programming Interview, like the earlier Wiley bestseller Programming Interviews Exposed, helps you approach the job interview with the confidence that comes from being prepared. |
| data engineer python coding questions: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data engineer python coding questions: Python Programming and Numerical Methods Qingkai Kong, Timmy Siauw, Alexandre Bayen, 2020-11-27 Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. - Includes tips, warnings and try this features within each chapter to help the reader develop good programming practice - Summaries at the end of each chapter allow for quick access to important information - Includes code in Jupyter notebook format that can be directly run online |
| data engineer python coding questions: The Holloway Guide to Technical Recruiting and Hiring Osman (Ozzie) Osman, 2022-01-10 Learn how the best teams hire software engineers and fill technical roles. The Holloway Guide to Technical Recruiting and Hiring is the authoritative guide to growing software engineering teams effectively, written by and for hiring managers, recruiters, interviewers, and candidates. Hiring is rated as one of the biggest obstacles to growth by most CEOs. Hiring managers, recruiters, and interviewers all wrestle with how to source candidates, interview fairly and effectively, and ultimately motivate the right candidates to accept offers. Yet the process is costly, frustrating, and often stressful or unfair to candidates. Anyone who cares about building effective software teams will return to this book again and again. Inside, you'll find know-how from some of the most insightful and experienced leaders and practitioners—senior engineers, recruiters, entrepreneurs, and hiring managers—who’ve built teams from early-stage startups to thousand-person engineering organizations. The lead author of this guide, Ozzie Osman, previously led product engineering at Quora and teams at Google, and built (and sold) his own startup. Additional contributors include Aditya Agarwal, former CTO of Dropbox; Jennifer Kim, former head of diversity at Lever; veteran recruiters and startup founders Jose Guardado (founder of Build Talent and former Y Combinator) and Aline Lerner (CEO of Interviewing.io); and over a dozen others. Recruiting and hiring can be done well, in a way that has a positive impact on companies, employees, and every candidate. With the right foundations and practice, teams and candidates can approach a stressful and difficult process with knowledge and confidence. Ask your employer if you can expense this book—it's one of the highest-leverage investments they can make in your team. |
| data engineer python coding questions: Making Embedded Systems Elecia White, 2011-10-25 Interested in developing embedded systems? Since they donâ??t tolerate inefficiency, these systems require a disciplined approach to programming. This easy-to-read guide helps you cultivate a host of good development practices, based on classic software design patterns and new patterns unique to embedded programming. Learn how to build system architecture for processors, not operating systems, and discover specific techniques for dealing with hardware difficulties and manufacturing requirements. Written by an expert whoâ??s created embedded systems ranging from urban surveillance and DNA scanners to childrenâ??s toys, this book is ideal for intermediate and experienced programmers, no matter what platform you use. Optimize your system to reduce cost and increase performance Develop an architecture that makes your software robust in resource-constrained environments Explore sensors, motors, and other I/O devices Do more with less: reduce RAM consumption, code space, processor cycles, and power consumption Learn how to update embedded code directly in the processor Discover how to implement complex mathematics on small processors Understand what interviewers look for when you apply for an embedded systems job Making Embedded Systems is the book for a C programmer who wants to enter the fun (and lucrative) world of embedded systems. Itâ??s very well writtenâ??entertaining, evenâ??and filled with clear illustrations. â??Jack Ganssle, author and embedded system expert. |
| data engineer python coding questions: Agile Data Warehouse Design Lawrence Corr, Jim Stagnitto, 2011-11 Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. This book describes BEAM✲, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM✲ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: ✲ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM✲) ✲ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! ✲ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) ✲ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail ✲ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development ✲ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply ✲ Agile design documentation: enhancing star schemas with BEAM✲ dimensional shorthand notation ✲ Solving difficult DW/BI performance and usability problems with proven dimensional design patterns Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino. |
| data engineer python coding questions: Python Crash Course Eric Matthes, 2015-11-01 Python Crash Course is a fast-paced, thorough introduction to Python that will have you writing programs, solving problems, and making things that work in no time. In the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. You’ll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you’ll put your new knowledge into practice with three substantial projects: a Space Invaders–inspired arcade game, data visualizations with Python’s super-handy libraries, and a simple web app you can deploy online. As you work through Python Crash Course you’ll learn how to: –Use powerful Python libraries and tools, including matplotlib, NumPy, and Pygal –Make 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progresses –Work with data to generate interactive visualizations –Create and customize Web apps and deploy them safely online –Deal with mistakes and errors so you can solve your own programming problems If you’ve been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code! Uses Python 2 and 3 |
| data engineer python coding questions: Coding Interview Questions Narasimha Karumanchi, 2012-05 Coding Interview Questions is a book that presents interview questions in simple and straightforward manner with a clear-cut explanation. This book will provide an introduction to the basics. It comes handy as an interview and exam guide for computer scientists. Programming puzzles for interviews Campus Preparation Degree/Masters Course Preparation Big job hunters: Apple, Microsoft, Google, Amazon, Yahoo, Flip Kart, Adobe, IBM Labs, Citrix, Mentor Graphics, NetApp, Oracle, Webaroo, De-Shaw, Success Factors, Face book, McAfee and many more Reference Manual for working people Topics Covered: Programming BasicsIntroductionRecursion and BacktrackingLinked Lists Stacks Queues Trees Priority Queue and HeapsGraph AlgorithmsSortingSearching Selection Algorithms [Medians] Symbol TablesHashing String Algorithms Algorithms Design Techniques Greedy Algorithms Divide and Conquer Algorithms Dynamic Programming Complexity Classes Design Interview Questions Operating System Concepts Computer Networking Basics Database Concepts Brain Teasers NonTechnical Help Miscellaneous Concepts Note: If you already have Data Structures and Algorithms Made Easy no need to buy this. |
| data engineer python coding questions: Learn Python 3 the Hard Way Zed A. Shaw, 2017-06-26 You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3 |
| data engineer python coding questions: Cracking the Data Engineering Interview Kedeisha Bryan, Taamir Ransome, 2023-11-07 Get to grips with the fundamental concepts of data engineering, and solve mock interview questions while building a strong resume and a personal brand to attract the right employers Key Features Develop your own brand, projects, and portfolio with expert help to stand out in the interview round Get a quick refresher on core data engineering topics, such as Python, SQL, ETL, and data modeling Practice with 50 mock questions on SQL, Python, and more to ace the behavioral and technical rounds Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPreparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey. The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you’ll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you’ll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions. By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.What you will learn Create maintainable and scalable code for unit testing Understand the fundamental concepts of core data engineering tasks Prepare with over 100 behavioral and technical interview questions Discover data engineer archetypes and how they can help you prepare for the interview Apply the essential concepts of Python and SQL in data engineering Build your personal brand to noticeably stand out as a candidate Who this book is for If you’re an aspiring data engineer looking for guidance on how to land, prepare for, and excel in data engineering interviews, this book is for you. Familiarity with the fundamentals of data engineering, such as data modeling, cloud warehouses, programming (python and SQL), building data pipelines, scheduling your workflows (Airflow), and APIs, is a prerequisite. |
| data engineer python coding questions: A Common-Sense Guide to Data Structures and Algorithms, Second Edition Jay Wengrow, 2020-08-10 Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You’ll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable. |
| data engineer python coding questions: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data engineer python coding questions: Python Interviews Michael Driscoll, 2018-02-28 Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You’ll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python. Key Features Hear from these key Python thinkers about the current status of Python, and where it's heading in the future Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning Understand the direction of Python, and what needs to change for Python 4 Book Description Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips. • Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3. • Steve Holden - tireless Python promoter and former chairman and director of the PSF. • Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member. • Nick Coghlan - founding member of the PSF's Packaging Working Group and Python core developer. • Jessica McKellar - former director of the PSF and Python activist. • Marc-André Lemburg - Python core developer and founding member of the PSF. • Glyph Lefkowitz - founder of Twisted and fellow of the PSF • Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998. • Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. • Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell. • Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs. • Tarek Ziadé - founder of Afpy and author of Expert Python Programming. • Sebastian Raschka - data scientist and author of Python Machine Learning. • Wesley Chun - fellow of the PSF and author of the Core Python Programming books. • Steven Lott - Python blogger and author of Python for Secret Agents. • Oliver Schoenborn - author of Pypubsub and wxPython mailing list contributor. • Al Sweigart - bestselling author of Automate the Boring Stuff with Python and creator of the Python modules Pyperclip and PyAutoGUI. • Luciano Ramalho - fellow of the PSF and the author of Fluent Python. • Mike Bayer - fellow of the PSF, creator of open source libraries including SQLAlchemy. • Jake Vanderplas - data scientist and author of Python Data Science Handbook. What you will learn How successful programmers think The history of Python Insights into the minds of the Python core team Trends in Python programming Who this book is for Python programmers and students interested in the way that Python is used – past and present – with useful anecdotes. It will also be of interest to those looking to gain insights from top programmers. |
| data engineer python coding questions: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| data engineer python coding questions: Programming Interview Problems Leonardo Rossi, 2020-11-05 Are you preparing for a programming interview? Would you like to work at one of the Internet giants, such as Google, Facebook, Amazon, Apple, Microsoft or Netflix? Are you looking for a software engineer position? Are you studying computer science or programming? Would you like to improve your programming skills? If the answer to any of these questions is yes, this book is for you! The book contains very detailed answers and explanations for the most common dynamic programming problems asked in programming interviews. The solutions consist of cleanly written code, with plenty of comments, accompanied by verbal explanations, hundreds of drawings, diagrams and detailed examples, to help you get a good understanding of even the toughest problems. The goal is for you to learn the patterns and principles needed to solve even dynamic programming problems that you have never seen before. Here is what you will get: A 180-page book presenting dynamic programming problems that are often asked in interviews. Multiple solutions for each problem, starting from simple but naive answers that are gradually improved until reaching the optimal solution. Plenty of detailed examples and walkthroughs, so that you can see right away how the solution works. 350+ drawings and diagrams which cater towards visual learners. Clear and detailed verbal explanations of how to approach the problems and how the code works. Analysis of time and space complexity. Discussion of other variants of the same problem, with solutions. Unit tests, including the reasoning behind choosing each one (edge case identification, performance evaluation etc.). Suggestions regarding what clarification questions you should ask, for each problem. Multiple solutions to the problems, where appropriate. General Python implementation tips. Wishing you the best of luck with your interviews! |
| data engineer python coding questions: Problem Solving in Data Structures & Algorithms Using Python Hemant Jain, 2019-05-16 Problem Solving in Data Structures & Algorithms is a series of books about the usage of Data Structures and Algorithms in computer programming. The book is easy to follow and is written for interview preparation point of view. In these books, the examples are solved in various languages like Go, C, C++, Java, C#, Python, VB, JavaScript and PHP. GitHub Repositories for these books. https: //github.com/Hemant-Jain-Author Book's Composition This book introduces you to the world of data structures and algorithms. Data structures defines the way in which data is arranged in memory for fast and efficient access while algorithms are a set of instruction to solve problems by manipulating these data structures. Designing an efficient algorithm is a very important skill that all software companies, e.g. Microsoft, Google, Facebook etc. pursues. Most of the interviews for these companies are focused on knowledge of data-structures and algorithms. They look for how candidates use concepts of data structures and algorithms to solve complex problems efficiently. Apart from knowing, a programming language you also need to have good command of these key computer fundamentals to not only qualify the interview but also excel in you jobs as a software engineer. This book assumes that you are a C language developer. You are not an expert in C language, but you are well familiar with concepts of classes, functions, arrays, pointers and recursion. At the start of this book, we will be looking into Complexity Analysis followed by the various data structures and their algorithms. We will be looking into a Linked-List, Stack, Queue, Trees, Heap, Hash-Table and Graphs. We will also be looking into Sorting, Searching techniques. In last few chapters, we will be looking into various algorithmic techniques. Such as, Brute-Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, Reduction and Backtracking. . Table of Contents Chapter 0: How to use this book. Chapter 1: Algorithms Analysis Chapter 2: Approach to solve algorithm design problems Chapter 3: Abstract Data Type & C# Collections Chapter 4: Searching Chapter 5: Sorting Chapter 6: Linked List Chapter 7: Stack Chapter 8: Queue Chapter 9: Tree Chapter 10: Priority Queue Chapter 11: Hash-Table Chapter 12: Graphs Chapter 13: String Algorithms Chapter 14: Algorithm Design Techniques Chapter 15: Brute Force Algorithm Chapter 16: Greedy Algorithm Chapter 17: Divide & Conquer Chapter 18: Dynamic Programming Chapter 19: Backtracking Chapter 20: Complexity Theory |
| data engineer python coding questions: Python Tricks Dan Bader, 2017 I don't even feel like I've scratched the surface of what I can do with Python With Python Tricks: The Book you'll discover Python's best practices and the power of beautiful & Pythonic code with simple examples and a step-by-step narrative. You'll get one step closer to mastering Python, so you can write beautiful and idiomatic code that comes to you naturally. Learning the ins and outs of Python is difficult-and with this book you'll be able to focus on the practical skills that really matter. Discover the hidden gold in Python's standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you're wondering which lesser known parts in Python you should know about, you'll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers' minds in your next code review. If you've got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you've worked with other programming languages and you want to get up to speed with Python, you'll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you'll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: I kept thinking that I wished I had access to a book like this when I started learning Python many years ago. - Mariatta Wijaya, Python Core Developer This book makes you write better Python code! - Bob Belderbos, Software Developer at Oracle Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty. - Ben Felder, Pythonista It's like having a seasoned tutor explaining, well, tricks! - Daniel Meyer, Sr. Desktop Administrator at Tesla Inc. |
| data engineer python coding questions: Data Structures and Algorithms in Python Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, 2013-06-17 Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects. |
| data engineer python coding questions: Grokking the System Design Interview Design Gurus, 2021-12-18 This book (also available online at www.designgurus.org) by Design Gurus has helped 60k+ readers to crack their system design interview (SDI). System design questions have become a standard part of the software engineering interview process. These interviews determine your ability to work with complex systems and the position and salary you will be offered by the interviewing company. Unfortunately, SDI is difficult for most engineers, partly because they lack experience developing large-scale systems and partly because SDIs are unstructured in nature. Even engineers who've some experience building such systems aren't comfortable with these interviews, mainly due to the open-ended nature of design problems that don't have a standard answer. This book is a comprehensive guide to master SDIs. It was created by hiring managers who have worked for Google, Facebook, Microsoft, and Amazon. The book contains a carefully chosen set of questions that have been repeatedly asked at top companies. What's inside? This book is divided into two parts. The first part includes a step-by-step guide on how to answer a system design question in an interview, followed by famous system design case studies. The second part of the book includes a glossary of system design concepts. Table of Contents First Part: System Design Interviews: A step-by-step guide. Designing a URL Shortening service like TinyURL. Designing Pastebin. Designing Instagram. Designing Dropbox. Designing Facebook Messenger. Designing Twitter. Designing YouTube or Netflix. Designing Typeahead Suggestion. Designing an API Rate Limiter. Designing Twitter Search. Designing a Web Crawler. Designing Facebook's Newsfeed. Designing Yelp or Nearby Friends. Designing Uber backend. Designing Ticketmaster. Second Part: Key Characteristics of Distributed Systems. Load Balancing. Caching. Data Partitioning. Indexes. Proxies. Redundancy and Replication. SQL vs. NoSQL. CAP Theorem. PACELC Theorem. Consistent Hashing. Long-Polling vs. WebSockets vs. Server-Sent Events. Bloom Filters. Quorum. Leader and Follower. Heartbeat. Checksum. About the Authors Designed Gurus is a platform that offers online courses to help software engineers prepare for coding and system design interviews. Learn more about our courses at www.designgurus.org. |
| data engineer python coding questions: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data engineer python coding questions: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| data engineer python coding questions: The Algorithm Design Manual Steven S Skiena, 2009-04-05 This newly expanded and updated second edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW war stories relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java |
| data engineer python coding questions: CPython Internals Anthony Shaw, 2021-05-05 Get your guided tour through the Python 3.9 interpreter: Unlock the inner workings of the Python language, compile the Python interpreter from source code, and participate in the development of CPython. Are there certain parts of Python that just seem like magic? This book explains the concepts, ideas, and technicalities of the Python interpreter in an approachable and hands-on fashion. Once you see how Python works at the interpreter level, you can optimize your applications and fully leverage the power of Python. By the End of the Book You'll Be Able To: Read and navigate the CPython 3.9 interpreter source code. You'll deeply comprehend and appreciate the inner workings of concepts like lists, dictionaries, and generators. Make changes to the Python syntax and compile your own version of CPython, from scratch. You'll customize the Python core data types with new functionality and run CPython's automated test suite. Master Python's memory management capabilities and scale your Python code with parallelism and concurrency. Debug C and Python code like a true professional. Profile and benchmark the performance of your Python code and the runtime. Participate in the development of CPython and know how to contribute to future versions of the Python interpreter and standard library. How great would it feel to give back to the community as a Python Core Developer? With this book you'll cover the critical concepts behind the internals of CPython and how they work with visual explanations as you go along. Each page in the book has been carefully laid out with beautiful typography, syntax highlighting for code examples. What Python Developers Say About The Book: It's the book that I wish existed years ago when I started my Python journey. [...] After reading this book your skills will grow and you will be able solve even more complex problems that can improve our world. - Carol Willing, CPython Core Developer & Member of the CPython Steering Council CPython Internals is a great (and unique) resource for anybody looking to take their knowledge of Python to a deeper level. - Dan Bader, Author of Python Tricks There are a ton of books on Python which teach the language, but I haven't really come across anything that would go about explaining the internals to those curious minded. - Milan Patel, Vice President at (a major investment bank) |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
