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A power-packed guide with solutions to crack a Big data Hadoop Interview Key Features Get familiar with Big data concepts Understand the working of Hadoop and its ecosystem. Understand the working of HBase, Pig, Hive, Flume, Sqoop and Spark Understand the capabilities of Big data including Hadoop and HDFS Up and running with how to perform speedy data processing using Apache Spark Description This book prepares you for Big data interviews w.r.t. Hadoop system and its ecosystems such as HBase, Pig, Hive, Flume, Sqoop, and Spark. Over the last few years, there is a rise in demand for Big Data Scientists/Analysts throughout the globe. Data Analysis and Interpretation have become very important lately The book covers many interview questions and the best possible ways to answer them. Along with the answers, you will come across real-world examples that will help you understand the concepts of Big Data. The book is divided into various sections to make it easy for you to remember and associate it with the questions asked. What you will learn Apache Pig interview questions and answers HBase and Hive interview questions and answers Apache Sqoop interview questions and answers Apache Flume interview questions and answers Apache Spark interview questions and answers Who this book is for This book is for anyone interested in big data. It is also useful for all jobseekers and freshers who wants to drive their career in the field of Big Data and Data Processing. Table of Contents 1. Big data, Hadoop and HDFS interview questions 2. Apache PIG interview questions 3. Hive interview questions 4. Hbase interview questions 5. Apache Sqoop interview questions 6. Apache Flume interview questions 7. Apache Spark interview questions About the Authors Vishwanathan Narayanan is an extreme programmer in various technologies, including Java, Python, and R, and he has around 18 years of experience in the field of information technology and data science. Exposure to real-world data science and advanced analytics using big data technologies gives him a great advantage, which he tries to impart through his books. A passionate teacher, he likes writing books as a hobby.
Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services Key Features Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS) Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platform Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrep Build real-time data pipeline to support real-time analytics using Pub/Sub messaging service Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient manner Learn how to use Cloud Data Studio for visualizing the data on top of Big Query Implement and understand real-world business scenarios for Machine Learning, Data Pipeline Engineering Description Modern businesses are awash with data, making data-driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert. The current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will be cover all the services that are being offered by GCP, putting emphasis on Data services. What will you learn By the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Datawarehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning API’s to support real-life business problems. Remember to practice additional examples to master these techniques. Who this book is for This book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. One-stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space. -Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field of data analytics, can refer/use this book to master their knowledge/understanding. -The highlight of this book is that it will start with the basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences. Table of Contents 1. GCP Overview and Architecture 2. Data Storage in GCP 3. Data Processing in GCP with Pub/Sub and Dataflow 4. Data Processing in GCP with DataPrep and Dataflow 5. Big Query and Data Studio 6. Machine Learning with GCP 7. Sample Use cases and Examples
Get answers to frequently asked questions on Data Science and Machine Learning using R Key Features Understand the capabilities of the R programming language Most of the machine learning algorithms and their R implementation covered in depth Answers on conceptual data science concepts are also covered Description This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language. The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job. By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. What will you learn Get answers to the basics, intermediate and advanced questions on R programming Understand the transformation and filtering capabilities of R Know how to perform visualization using R Who this book is for This book is a must for anyone interested in Data Science and Machine Learning. Anyone who wants to clear the interview can use it as a last-minute revision guide. Table of Contents 1. Data Science basic questions and terms 2. R programming questions 3. GGPLOT Questions 4. Statistics with excel sheet About the Author Vishwanathan Narayanan has 18 years of experience in the field of information technology and data analysis. He made many enterprise-level applications with stable output and scalability. Advanced level data analysis for complex problems using both R and Python has been the key area of work for many years. Extreme programmer on Java, Python, R, and many more technologies
Primer into the multidisciplinary world of Data Science Key Features Explore and use the key concepts of Statistics required to solve data science problems Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app Learn how to build Data Science solutions with GCP and AWS Description The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset. What you will learn Understand the multi-disciplinary nature of Data Science Get familiar with the key concepts in Mathematics and Statistics Explore a few key ML algorithms and their use cases Learn how to implement the basics of Data Pipelines Get an overview of Cloud Computing & DevOps Learn how to create visualizations using Tableau Who this book is for This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science. Table of Contents 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business Intelligence 16. Data Visualization Tools 17. Industry Use Case 1 – FormAssist 18. Industry Use Case 2 – PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments About the Author The book has been written by collective experience of many of Probyto past client projects, academic collaborations and team members for last 5 years. The collective work is represented by different experts in data driven decision making and portion they deal with in creating value for the clients at Probyto. The team has experienced professionals and freshers who have gained from the approach as mentioned in the book as well. Two key contributions for the book goes to Parvej Reja Saleh (Manager) and Namachivayam Dharmalingam (Senior Analyst). Blog links: https://probyto/resources/blogs LinkedIn Profile: https://www.linkedin.com/company/probyto
Learn how to process and analysis data using Python Key Features The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Author Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of ‘Social Network Analysis and Mining’. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals.
Leverage Google Analytics to make data-driven decisions to shape your marketing strategy Key Features Learn how to navigate the Google Analytics interface and reports. Understand the working of the Google Analytics platform. Understanding ‘Traffic Sources’ in Google Analytics. Learn how to use Segments in Google Analytics. Understand how Cross-Device reporting works in Google Analytics. Description This book will help you learn everything that you need to know about Google Analytics. We will start by setting up the account and updating the settings. Then, we will go through the main reports in Google Analytics will dive deep into the analysis. We will then analyze the users, their behavior, and their sources. This analysis will improve your business and website results. We will also go through the fundamentals of relating Google Analytics data to your marketing strategy. We will explore live examples of analysis with real Ecommerce data and learn approaches to analyze our data. At the end of the book, we will go through the Conversions section in Google Analytics. By the end of the book, you will be able to make informative decisions based on data related to your website visitors. What will you learn Learn how to set-up a Google Analytics account. Understand how to read all the reports in Google Analytics. Perform complex analysis based on the data in the reports. Learn how to relate the Google Analytics data to your marketing strategy. Read and analyze Conversion reports based on real Ecommerce data. Who this book is for This book is designed for business owners and webmasters who want to use Google Analytics to make better decisions and improve their sales. Table of Contents 1. Google Analytics Step-by-step setup. 2. Google Analytics reports explained. 3. 7P’s of Marketing and Google Analytics. 4. Your audience – your business. 5. The heartbeat of the Google Analytics: Acquisition & Behavior Reports. 6. Conversions. The final goal. About the Author Grigor Yovov is a certified Google Ads and Google Analytics expert and a bachelor in Marketing. He has over 20,000 students from 153 countries in the world’s biggest learning platform Udemy, where he creates courses related to Google Ads, Google Analytics and Business Development. In 2011 he founded his own digital marketing agency called Business Trend serving clients all around the world. Your Blog links: http://howtoads.com/ Your LinkedIn Profile: linkedin.com/in/grigor-yovov-digital-marketer
Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key Features Understand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work. Learn how to implement Decision Trees and Random Forests. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling. Description This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. What will you learn Learn how to prepare Data for Machine Learning. Learn how to implement learning algorithms from scratch. Use scikit-learn to implement algorithms. Use various Feature Selection and Feature Extraction methods. Learn how to develop a Face recognition system. Who this book is for The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular. Table of Contents 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography About the Author Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development. He has authored a few books including Programming in C#, Oxford University Press, Algorithms, Oxford University Press, Python Basics, Mercury, Python for Beginners, New Age International. Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship. Outside work, he is deeply interested in Hindi Poetry, progressive era, Hindustani Classical Music, percussion instruments. His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning.
Hands-on MuleSoft Flows using MuleSoft Anypoint Studio Components and understanding Payload processing along with debugging. Key Features Get familiar with the MuleSoft Anypoint Studio key techniques such as Payload, Logger, Variables, Flow and Flow Reference. Deep dive into Massage Structure and Payload value handling. Get familiar with the Global Configuration Properties and Securing properties. Explore Mule Run Time and Deploying Mule Projects in CloudHub. Description This book is aimed to teach the readers how to design RAML APIs using Anypoint Platform. It also focuses on popular topics such as System Integration, API Led Connectivity, and Centre for Enablement and RAML. It will show how to use, call and test free mock REST APIs. The readers can also work with some commercially available license-based APIs. Furthermore, the book will explain most of the examples provided by RAML.org so that you can simulate it from your local system. This book will then help you develop your RESTful API specification for adding, retrieving, updating and deleting data for a business entity. Later, you will learn how to use the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. By the end, you will be able to develop an end to end RAML API using the MuleSoft Anypoint Studio. What you will learn Get exposed to Payload handling, logging and variables Work with different Flow Control components such as Choice, First Successful, Round-Robin and Scatter Gather Explore and work with Error Handling components such as Error Handler, On Error, Continue, On Error Propagate and Raise Error Understand Global Configuration Properties and Securing properties Gain knowledge about various scopes involved in MuleSoft Flow designing Who this book is for This book is meant for anyone interested to become an API designer. Experienced technical persons of the IT industry also can utilize the book to get extra insights, and they can align their knowledge in line with it. Table of Contents 1. Start Project 2. Anypoint Studio Components 3. Flow Control Components 4. Idempotent, Parse Template and Scheduler 5. Payload Component 6. MUnit 7. MuleSoft Runtime 8. Global Secured Configurations 9. Error Handling 10. RAML and Anypoint Studio About the Authors Nanda Nachimuthu is an Engineering graduate from Tamil Nadu Agricultural University, Coimbatore, and has completed Advanced Diploma from the Indian Institute of Technology, Kharagpur, in the field of Java and Internet Computing. He has also completed an Advanced Diploma from Indian Institute of International Trading, Delhi, which specializes in Strategies for International Business. He has 25 years of experience in various domains like banking, healthcare, government, and airlines. He’s into technologies like Java, Big Data, Cloud, ESB, Security and IoT. He has taken up various roles like technical architect, solutions architect, cloud architect, and enterprise integration architect and always wanted to take up an individual contributor’s role with hands-on coding experience. He is passionate about social entrepreneurship and takes pro-bono consultations in multiple fields like information technology, manufacturing, trading, agriculture, and internet of things. The founder of some social platforms, he also has a few trademarks under his kitty. Presently, he is focusing on integration technology platforms like MuleSoft, where he finds a wide scope in the future for digital marketing and machine to machine communication. Github Link: github.com/nanda3008 LinkedIn Profile: https://www.linkedin.com/in/nanda3008/
Hands-On ML problem solving and creating solutions using Python. Key Features Introduction to Python Programming Python for Machine Learning Introduction to Machine Learning Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms Linear Regression, Logistic Regression and Support Vector Machines Description You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them. We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters. What You Will Learn Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning. Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries. Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you. Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation. Get to know the basics of Deep Learning and some interesting algorithms in this space. Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model Who this book is for This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. Table of Contents 1. Introduction to Python Programming 2. Python for Machine Learning 3. Introduction to Machine Learning 4. Supervised Learning and Unsupervised Learning 5. Linear Regression: A Hands-on guide 6. Logistic Regression – An Introduction 7. A sneak peek into the working of Support Vector machines(SVM) 8. Decision Trees 9. Random Forests 10. Time Series models in Machine Learning 11. Introduction to Neural Networks 12. Recurrent Neural Networks 13. Convolutional Neural Networks 14. Performance Metrics 15. Introduction to Design Thinking 16. Design Thinking Case Study About the Author Gnana Lakshmi T C —iis Technology Geek, Innovator, Keynote speaker, Community builder and holds a Bachelor degree in Computer Science from National Institute of Technology, Tiruchirappalli. She is currently associated with WileyNXT as Product Manager, Emerging Technologies. She is also a Fellow Alumni at WomenWhoCode and started WomenWhoCode Blockchain community (www.womenwhocode.com/blockchain). She harnesses her knowledge by sharing it with others by conducting live events like webinars and workshops and through online channels like tutorials, social media posts etc. She has conducted several meetups on Machine learning, Blockchain and various other emerging technology topics including a recent meetup at the International Open UP Summit on GPT-3. LinkedIn Profile: https://www.linkedin.com/in/gyan-lakshmi Madeleine Shang —is a Recommender Systems Team Lead @OpenMined. She started the Data Science and Machine Learning community at WomenWhoCode which is now successfully running with 2147 members. She is an expert in AI and Blockchain Research. She has been involved in many startups as a Founder. She is an Adventurer and Futurist at heart. LinkedIn Profile: https://www.linkedin.com/in/madeleine-shang/
A Cookbook that will help you implement Machine Learning algorithms and techniques by building real-world projects Key Features Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning. Description Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. What will you learn Understand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. Who this book is for This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners. Table of Contents 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI About the Author Rehan Guha —A Researcher by the day and an Artist by night. Our Author is a Scholar -lecturer, an Innovator, and also a Humanitarian -Philanthropist. He started his life as a Coder, Developer, and now he is into research in the field of Machine Learning and Algorithms but also has a keen interest in General Science, Technology, Invention & Innovation. The author holds a graduation degree from the Institute of Engineering & Management, Kolkata, and a Postgraduate certification on Deep Learning from the Indian Institute of Technology, Kharagpur (IIT-K)-AICTE approved FDP course. If we talk about Rehan's area of interest, it lies in Optimization Problems, Explainable AI, Deep Learning Architecture, Algorithms, Complexity, Algorithmic Thinking, et cetera… He has multiple publications through Journals and Open Publications, along with his publications he has filed multiple patents for his Innovations and Inventions. At an early age, one of his patents was also demonstrated to the Indian Army. In Rehan’s career, he has been involved with a variety of Business Verticals, starting from Banking, Consulting, Law, Insurance, Freight & Logistics, and Telcom.