Do you want to get a job at Google? If the answer is yes, these are the most important skills that will help you get an engineering job at Google, and also I will help you with how to gain these important skills.
Many of us that have some sort of engineering background have a dream wish to work in a company like Google, which has a huge impact on our lives and will have a huge impact on our future.
Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware.
Being one of the biggest companies in the world, the skills requirements for a job position are quite a lot and require you to have at least a bachelor’s degree in engineering.
But, we all know a couple of exceptions where people work for such a big company and don’t have a degree, but have the skills. At the end of the day, it is very important how you present yourself at the interviews, and if you represent a good level of knowledge, you will get the job. And in this situation, you will get a job at Google.
In this article, we are going to list the most important skills that will lend you a Machine Learning or Data Scientist job position at Google, including sources (like websites or books) where you can learn those skills. The most important skills that we are going to list are based on the requirements for the job positions at Google Careers.
I must mention a few other articles that will also help you get any engineering job position:
- Python skills that will help you get a job as a data scientist or machine learning engineer
- How to write the perfect CV
- The best programming skills for every data scientist or machine learning engineer
- The most essential mathematical skills to get a job as a data scientist or machine learning engineer
These are I think the most important and most related articles with this post that will also help you even more to become an engineer at Google.
Are you ready to see which skills will help you to get an engineering job at Google? Because I’m ready to help you gain the skills you need to get your dream job.
Well, these are the most important skills that will help you get a job at Google
1. Machine Learning
- Machine Learning Tutorial at GeeksForGeeks: Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
The course/tutorial is FREE and it has over 50 articles related to it, that will get you ready in no time at all. The articles are very useful and helpful and contain information and knowledge about Machine Learning, supervised and unsupervised learning, optimization techniques, data processing, and streaming, and practical implementation in Python, R, and some other software based on these languages like PySpark, etc.
- Pattern Recognition and Machine Learning: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning.
No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow – author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- The book is organized in the following structure:
- Explore the machine learning landscape, particularly neural nets
- Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
- Machine Learning (MIT): The course is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks.
The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
- Mathematics of Machine Learning (MIT): Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.
- Prediction: Machine Learning and Statistics (MIT): Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age.
Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.
- Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT): Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning. Here are all the video lectures.
2. Artificial Intelligence
- Artificial Intelligence: A Modern Approach: The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI).
The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
- Applied Artificial Intelligence: A Handbook For Business Leaders: Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities. If you want to drive innovation by combining data, technology, design, and people to solve real problems at an enterprise scale, this is your playbook.
This book does not overload you with details on debugging TensorFlow code nor bore you with generalizations about the future of humanity. Instead, we teach you how to lead successful AI initiatives by prioritizing the right opportunities, building a diverse team of experts, conducting strategic experiments, and consciously designing your solutions to benefit both your organization and society as a whole. This book is focused on helping you drive concrete business decisions through applications of artificial intelligence and machine learning.
- Artificial Intelligence articles at TowardsDataScience: This section of this famous AI, ML, DL, DS web site has over 441,000 followers and includes some of the highest quality articles, papers, and examples regarding Artificial Intelligence and its subfields.
- Artificial Intelligence (MIT): This course introduces students to the basic knowledge representation, problem-solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem-solving, and learning in intelligent-system engineering; and appreciate the role of problem-solving, vision, and language in understanding human intelligence from a computational perspective. Here are all the video lectures.
3. Natural Language Processing
- Natural Language Processing in Action: Understanding, analyzing, and generating text with Python: Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in a text and react accordingly.
The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing a text, and answering free-form questions.
- Natural Language Processing with Python: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
With it, you’ll learn how to write Python programs that work with large collections of unstructured text. You’ll access richly annotated datasets using a comprehensive range of linguistic data structures, and you’ll understand the main algorithms for analyzing the content and structure of written communication.
Packed with examples and exercises, Natural Language Processing with Python will help you: extract information from unstructured text, either to guess the topic or identify “named entities”, analyze linguistic structure in text, including parsing and semantic analysis, access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence.
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (Nltk) open-source library. If you’re interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages – or if you’re simply curious to have a programmer’s perspective on how human language works – you’ll find Natural Language Processing with Python both fascinating and immensely useful.
- Advanced Natural Language Processing (MIT): This course is a graduate introduction to natural language processing – the study of human language from a computational perspective. It covers syntactic, semantic, and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms.
It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.
- NLTK at TutorialsPoint: Language is a method of communication with the help of which we can speak, read, and write. Natural Language Processing (NLP) is the subfield of computer science especially Artificial Intelligence (AI) that is concerned about enabling computers to understand and process human language.
We have various open-source NLP tools but NLTK (Natural Language Toolkit) scores very high when it comes to the ease of use and explanation of the concept. The learning curve of Python is very fast and NLTK is written in Python so NLTK is also having a very good learning kit. NLTK has incorporated most of the tasks like tokenization, stemming, Lemmatization, Punctuation, Character Count, and Word count. It is very elegant and easy to work with.
4. Software Engineering
- Software Engineering, 10th Edition: Software Engineering introduces readers to the overwhelmingly important subject of software programming and development. In the past few years, computer systems have come to dominate not just our technological growth, but the foundations of our world’s major industries. This text seeks to lay out the fundamental concepts of this huge and continually growing subject area in a clear and comprehensive manner.
- Clean Code: A Handbook of Agile Software Craftsmanship: Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code-of transforming a codebase that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and “smells” gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code.
- Software Engineering at Google: Lessons Learned from Programming Over Time: Today, software engineers need to know not only how to program effectively but also how to develop proper engineering practices to make their codebase sustainable and healthy. This book emphasizes this difference between programming and software engineering.
How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life? Based on their experience at Google, software engineers Titus Winters and Hyrum Wright, along with technical writer Tom Manshreck, present a candid and insightful look at how some of the world’s leading practitioners construct and maintain software. This book covers Google’s unique engineering culture, processes, and tools and how these aspects contribute to the effectiveness of an engineering organization.
- Software Engineering for Web Applications (MIT): This is a course for students who already have some programming and software engineering experience. The goal is to give students some experience in dealing with those challenges that are unique to Internet applications, such as: concurrency, unpredictable load, security risks, an opportunity for wide-area distributed computing, creating a reliable and stateful user experience on top of unreliable connections and stateless protocols, extreme requirements and absurd development schedules, requirements that change mid-way through a project, sometimes because of experience gained from testing with users, user demands for a multi-modal interface.
- Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming: Python Crash Course is the world’s best-selling guide to the Python programming language. This fast-paced, thorough introduction to programming with Python will have you writing programs, solving problems, and making things that work in no time.
- Python: 3 Manuscripts in 1 book: – Python Programming For Beginners – Python Programming For Intermediates – Python Programming for Advanced: This trilogy is a complete guide for every beginner in the world of programming with Python. It should make you an advanced Python programmer in just 21 days.
- Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners: If you’ve ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you? In this fully revised second edition of the best-selling classic Automate the Boring Stuff with Python, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.
You’ll learn the basics of Python and explore Python’s rich library of modules for performing specific tasks, like scraping data off websites, reading PDF and Word documents, and automating clicking and typing tasks.
- Python Programming Language tutorial at GeeksForGeeks: Python is a high-level, general-purpose, and a very popular programming language. Python programming language (latest Python 3) is being used in web development, Machine Learning applications, along with all cutting-edge technology in Software Industry.
Python Programming Language is very well suited for Beginners, also for experienced programmers with other programming languages like C++ and Java. This specially designed Python tutorial will help you learn Python Programming Language in the most efficient way, with the topics from basics to advanced (like Web-scraping, Django, Deep-Learning, etc.) with examples.
- Introduction to Computer Science and Programming in Python (MIT): This course is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language. Here are all the lecture videos.
- C Programming Language, 2nd Edition: The authors present the complete guide to ANSI standard C language programming. Written by the developers of C, this new version helps readers keep up with the finalized ANSI standard for C while showing how to take advantage of C’s rich set of operators, the economy of expression, improved control flow, and data structures.
The 2/E has been completely rewritten with additional examples and problem sets to clarify the implementation of difficult language constructs. For years, C programmers have let K&R guide them to building well-structured and efficient programs. Now, this same help is available to those working with ANSI compilers. Includes detailed coverage of the C language plus the official C language reference manual for at-a-glance help with syntax notation, declarations, ANSI changes, scope rules, and the list goes on and on.
- C Programming Absolute Beginner’s Guide: Write powerful C programs…without becoming a technical expert! This book is the fastest way to get comfortable with C, one incredibly clear and easy step at a time. You’ll learn all the basics: how to organize programs, store and display data, work with variables, operators, I/O, pointers, arrays, functions, and much more. C programming has never been this simple!
- The C++ Programming Language, 4th Edition: This book features an enhanced, lay-flat binding, which allows the book to stay open more easily when placed on a flat surface. This special binding method—noticeable by a small space inside the spine—also increases durability. C++11 has arrived: thoroughly master it, with the definitive new guide from C++ creator Bjarne Stroustrup, C++ Programming Language, Fourth Edition! The brand-new edition of the world’s most trusted and widely read guide to C++, has been comprehensively updated for the long-awaited C++11 standard.
Extensively rewritten to present the C++11 language, standard library, and key design techniques as an integrated whole, Stroustrup thoroughly addresses changes that make C++11 feel like a whole new language, offering definitive guidance for leveraging its improvements in performance, reliability, and clarity. C++ programmers around the world recognize Bjarne Stoustrup as the go-to expert for the absolutely authoritative and exceptionally useful information they need to write outstanding C++ programs. Now, as C++11 compilers arrive and development organizations migrate to the new standard, they know exactly where to turn once more: Stoustrup C++ Programming Language, Fourth Edition.
- C++ in One Hour a Day, Sams Teach Yourself: In just one hour a day, you’ll have all the skills you need to begin programming in C++. With this complete tutorial, you’ll quickly master the basics, and then move on to more advanced features and concepts. Completely updated for the C++14 standard, with a preview of C++17, this book presents the language from a practical point of view, helping you learn how to use C++ to create faster, simpler, and more efficient C++ applications.
- Programming: Principles and Practice Using C++ (2nd Edition): The book is an introduction to programming in general, including object-oriented programming and generic programming. It is also a solid introduction to the C++ programming language, one of the most widely used languages for real-world software. The book presents modern C++ programming techniques from the start, introducing the C++ standard library and C++11 and C++14 features to simplify programming tasks.
- Introduction to C and C++: This course provides a fast-paced introduction to the C and C++ programming languages. You will learn the required background knowledge, including memory management, pointers, preprocessor macros, object-oriented programming, and how to find bugs when you inevitably use any of those incorrectly.
- Head First Java, 2nd Edition: Learning a complex new language is no easy task especially when it’s an object-oriented computer programming language like Java. You might think the problem is your brain. It seems to have a mind of its own, a mind that doesn’t always want to take in the dry, technical stuff you’re forced to study.
- Java: Learn Java in One Day and Learn It Well. Java for Beginners with Hands-on Project. (Learn Coding Fast with Hands-On Project Book 4): Have you always wanted to learn computer programming but are afraid it’ll be too difficult for you? Or perhaps you know other programming languages but are interested in learning the Java language fast? This book is for you. You no longer have to waste your time and money trying to learn Java from boring books that are 600 pages long, expensive online courses, or complicated Java tutorials that just leave you more confused and frustrated.
What this book offers: Java for Beginners, Complex concepts are broken down into simple steps to ensure that you can easily master the Java language even if you have never coded before, Carefully Chosen Java Examples, Examples are carefully chosen to illustrate all concepts.
In addition, the output for all examples are provided immediately so you do not have to wait till you have access to your computer to test the examples, Careful selection of topics, Topics are carefully selected to give you broad exposure to Java, while not overwhelming you with information overload.
These topics include object-oriented programming concepts, error handling techniques, file handling techniques, and more. In addition, new features in Java (such as lambda expressions and default methods, etc.) are also covered so that you are always up to date with the latest advancement in the Java language.
- Introduction to Programming in Java (MIT): This course is an introduction to software engineering, using the Java™ programming language. It covers concepts useful to 6.005. Students will learn the fundamentals of Java. The focus is on developing high quality, working software that solves real problems.
The course is designed for students with some programming experience, but if you have none and are motivated you will do fine. Students who have taken 6.005 should not take this course. Each class is composed of one hour of lecture and one hour of assisted lab work.
- Java Programming at TutorialsPoint: Java is a high-level programming language originally developed by Sun Microsystems and released in 1995. Java runs on a variety of platforms, such as Windows, Mac OS, and the various versions of UNIX. This tutorial gives a complete understanding of Java. This reference will take you through simple and practical approaches while learning Java Programming language.
8. TensorFlow, PyTorch, Keras
- Deep Learning with Python: Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
- Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition: Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.
TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.
This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
- Deep Learning for Coders with fastai and PyTorch: AI Applications Without a Ph.D.: 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.
- TensorFlow tutorial at TutorialsPoint: TensorFlow is an open-source machine learning framework for all developers. It is used for implementing machine learning and deep learning applications. To develop and research fascinating ideas on artificial intelligence, the Google team created TensorFlow. TensorFlow is designed in Python programming language, hence it is considered an easy to understand framework.
- PyTorch tutorial at TutorialsPoint: PyTorch is an open-source machine learning library for Python and is completely based on Torch. It is primarily used for applications such as natural language processing. PyTorch is developed by Facebook’s artificial-intelligence research group along with Uber’s “Pyro” software for the concept of in-built probabilistic programming.
9. SQL and related DBMS (MySql, Oracle, PostgreSQL)
- SQL Cookbook (Cookbooks (O’Reilly)): You know the rudiments of the SQL query language, yet you feel you aren’t taking full advantage of SQL’s expressive power. You’d like to learn how to do more work with SQL inside the database before pushing data across the network to your applications.
You’d like to take your SQL skills to the next level. Let’s face it, SQL is a deceptively simple language to learn, and many database developers never go far beyond the simple statement: Select columns From table Where conditions. But there is so much more you can do with the language.
In the SQL Cookbook, experienced SQL developer Anthony Molinaro shares his favorite SQL techniques and features. You’ll learn about: Window functions, arguably the most significant enhancement to SQL in the past decade. If you’re not using these, you’re missing outPowerful, database-specific features such as SQL Server’s Pivot and Unpivot operators, Oracle’s Model clause, and PostgreSQL’s very useful Generate_Series functionPivoting rows into columns, reverse-pivoting columns into rows, using pivoting to facilitate inter-row calculations, and double-pivoting a result setBucketization, and why you should never use that term in Brooklyn.
How to create histograms, summarize data into buckets, perform aggregations over a moving range of values, generate running-totals and subtotals, and other advanced, data warehousing techniques. The technique of walking a string, which allows you to use SQL to parse through the characters, words, or delimited elements of a string Written in O’Reilly’s popular Problem/Solution/Discussion style, the SQL Cookbook is sure to please.
Anthony’s credo is: “When it comes down to it, we all go to work, we all have bills to pay, and we all want to go home at a reasonable time and enjoy what’s still available of our days”. The SQL Cookbook moves quickly from problem to solution, saving you time each step of the way.
- SQL All-in-One For Dummies (For Dummies (Computer/Tech)): SQL All-In-One For Dummies, 3rd Edition, is a one-stop-shop for everything you need to know about SQL and SQL-based relational databases. Everyone from database administrators to application programmers and the people who manage them will find clear, concise explanations of the SQL language and its many powerful applications.
With the ballooning amount of data out there, more and more businesses, large and small, are moving from spreadsheets to SQL databases like Access, Microsoft SQL Server, Oracle databases, MySQL, and PostgreSQL. This compendium of information covers designing, developing, and maintaining these databases.
- SQL: Learn SQL (using MySQL) in One Day and Learn It Well. SQL for Beginners with Hands-on Project. (Learn Coding Fast with Hands-On Project Book 5): The information era is upon us and the ability to organize and make sense of data has become an invaluable skill. Have you been hearing about data, databases, and SQL and wondering what it’s all about? Or perhaps you have just gotten a new job and need to learn SQL fast.
This book is for you. You no longer have to feel lost and overwhelmed by all the fragmented tutorials online, nor do you have to waste your time and money learning SQL from lengthy books and expensive online courses.
- Database Systems (MIT): This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.
- PostgreSQL official documentation: PostgreSQL is a powerful, open-source object-relational database system with over 30 years of active development that has earned it a strong reputation for reliability, feature robustness, and performance.
10. Big Data/Data processing, NoSQL, and tools:
- Mathematics of Big Data and Machine Learning (MIT): This course introduces the Dynamic Distributed Dimensional Data Model (D4M), a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in vast quantities of data.
This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms, group theory, and database design. This approach has been implemented in software. The class will begin with a number of practical problems, introduce the appropriate theory, and then apply the theory to these problems. Students will apply these ideas in the final project of their choosing. The course will contain a number of smaller assignments that will prepare the students with appropriate software infrastructure for completing their final projects. These are all lecture videos.
- Spark: The Definitive Guide: Big Data Processing Made Simple: Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.
You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library.
- Big Data: Principles and Best Practices of Scalable Realtime Data Systems: Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team.
You’ll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you’ll learn specific technologies like Hadoop, Storm, and NoSQL databases.
Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems: Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?
In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. The software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.
- Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale: Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. You’ll learn about recent changes to Hadoop, and explore new case studies on Hadoop’s role in healthcare systems and genomics data processing.
- Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale: There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform.
Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects.
11. Go language
- Go in Action: Go in Action introduces the Go language, guiding you from inquisitive developer to Go guru. The book begins by introducing the unique features and concepts of Go. Then, you’ll get hands-on experience writing real-world applications including websites and network servers, as well as techniques to manipulate and convert data at speeds that will make your friends jealous.
- Go language official documentation: Go is an open-source programming language that makes it easy to build simple, reliable, and efficient software.
Here is our choice of sources that will help you learn every skill and maybe become an engineer at Google. The sources come in different forms (courses, books, etc.).
That being said, not everything posted here is crucial for you, the most important thing is to learn concepts, everything else is just adaptation, so use this article as a guide, and don’t take everything in its literal form.
Do you have some of these most important skills and would you get a job at Google? I’m curious to know what do you think about getting an engineering job at Google.
If you are new to Machine Learning, Deep Learning, Computer Vision, Data Science or just Artificial Intelligence, in general, we will suggest you some of our other articles that you might find helpful, like:
- FREE Computer Science Curriculum From The Best Universities and Companies In The World
- How To Become a Certified Data Scientist at Harvard University for FREE
- How to Gain a Computer Science Education from MIT University for FREE
- Top 10 Best FREE Artificial Intelligence Courses from Harvard, MIT, and Stanford
- Top 10 Best Artificial Intelligence YouTube Channels in 2020
Like with every post we do, we encourage you to continue learning, trying and creating.