COM021030 books

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.

Analyzing business data points with greater precision, efficiency, and speed Key Features -Exposure to work on large datasets, data mining techniques, and SAS built-in functions. -Exciting examples and a step-by-step guide to the entire field of business analytics. -Additional support of sophisticated SQL queries and the creation of strong visualization reports. Description This book teaches readers how to properly use SAS (R) Studio to enhance business analytics summaries and graphical reports to make more informed business decisions. Since the examples in the book are laid out in a logical sequence, no prior knowledge is required to get started with implementing what you learn in them. The book begins with configuring your SAS® OnDemand instance, complete with sample datasets and scripts. The book explains programming syntaxes before delving into sophisticated programming principles for managing data values and concludes with creating graphical reports for business data values. It explores how to implement datasets, read external files, execute conditional statements, loops, formats, text, date, numeric functions, and arrays. The book also helps writing SQL Statements such as joins, sets, index, views, etc. with Proc SQL, Univariate (PROC MEAN, PROC FREQ), Multivariate (PROC FREQ), and Design Graphs with the help of PROC SGPLOT. After reading this book, readers will be able to evaluate business data values and create excellent visualizations that will assist enterprises in making more informed business decisions. Readers will become confident to use SAS Studio's rich interface and develop analytical programs. What you will learn -Configuring Online SAS® OnDemand for Academics. -Writing BASE SAS Programming, and writing conditional, looping-based programs. -Implementing SAS built-in text, date, numeric functions, and reading external data files. -Using the SQL Statement with PROC SQL Processing. -Plotting attractive data visualization using PROC SGPLOT. -End-to-end case study on Employee Skill Development’s Data Analytics and Visualization. Who this book is for This book is for those who wish to learn how to use SAS Studio's rich interface and develop an analytical program that helps make better decisions. Having a basic understanding of how data analytics works helps. Table of Contents 1. Introducing SAS Environment 2. Starting with SAS Programming 3. Data Mining and Storage Techniques in SAS 4. Controlling the Program Flow in SAS 5. Using SAS Built-In Functions 6. Working with Advanced Data Input Techniques 7. Getting Started with SQL Processing in SAS 8. Managing Database Tables in SAS 9. Working with Dataset in SAS 10. Data Visualization and Macro Programming with SAS 11. Case Study

A Complete Data Analytics Guide for Learners and Professionals. Key Features -Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. -Dive into Machine Learning, its tools, and applications. -Coverage of applications of Big Data, Data Analysis, and Business Intelligence. Description These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book “Data Analytics: Principles, Tools, and Practices” can be considered a handbook or a guide for professionals who want to start their journey in the field of data science. The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples. After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science. What you will learn -Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. -Learn to manage data warehousing with real time transaction processing. -Explore various machine learning techniques that apply to data analytics. -Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry. -Acquaint yourself with Big Data tools and statistical techniques for machine learning. Who this book is for IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book. Table of Contents 1. Database Management System 2. Online Transaction Processing and Data Warehouse 3. Business Intelligence and its deeper dynamics 4. Introduction to Data Visualization 5. Advanced Data Visualization 6. Introduction to Big Data and Hadoop 7. Application of Big Data Real Use Cases 8. Application of Big Data 9. Introduction to Machine Learning 10. Advanced Concepts to Machine Learning 11. Application of Machine Learning

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

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

Plan, build, deploy, and monitor data solutions on Azure Key Features -Work with PostgreSQL, MySQL, and CosmosDB databases on Microsoft Azure. -Work with whole data architecture, leverage Azure Storage, Azure Synapse, and Azure Data Lake. -Data integration strategies with Azure Data Factory and Data Bricks. Description 'Hands-On Azure Data Platform' helps readers get a fundamental understanding of the Database, Data Warehouse, and Data Lake and their management on the Azure Data Platform. The book describes how to work efficiently with Relational and Non-Relational Databases, Azure Synapse Analytics, and Azure Data Lake. The readers will use Azure Databricks and Azure Data Factory to experience data processing and transformation. The book delves deeply into topics like continuous integration, continuous delivery, and the use of Azure DevOps. The book focuses on the integration of Azure DevOps with CI/CD pipelines for data ops solutions. The book teaches readers how to migrate data from an on-premises system or another cloud service provider to Azure. After reading the book, readers will develop end-to-end data solutions using the Azure data platform. Additionally, data engineers and ETL developers can streamline their ETL operations using various efficient Azure services. What you will learn -In-depth knowledge of the principles of the data warehouse and the data lake. -Acquaint yourself with Azure Storage Files, Blobs, and Queues. -Create relational databases on the Azure platform using SQL, PostgreSQL, and MySQL. -With Cosmos DB, you can create extremely scalable databases and data warehouses. -Utilize Azure Databricks and Data Factory to develop data integration solutions. Who this book is for This book is designed for big data engineers, data architects, and cloud engineers who want to understand how to use the Azure Data Platform to build enterprise-grade solutions. Learning about databases and the Azure Data Platform would be helpful but not necessary. Table of Contents 1. Getting Started with the Azure Data Platform 2. Working with Relational Databases on Azure 3. Working with Azure Synapse Analytics 4. Working with Azure Data Lake 5. Working with Azure Cosmos DB 6. Working with Azure Databricks 7. Working with Azure Data Factory 8. DevOps with the Azure Data Platform 9. Planning and Migrating On-Premises Azure Workloads to the Azure Data platform 10. Design and Implement Data Solutions on Azure

Explore and work with various Microsoft Azure services for real-time Data Analytics Key Features Understanding what Azure can do with your data Understanding the analytics services offered by Azure Understand how data can be transformed to generate more data Understand what is done after a Machine Learning model is built Go through some Data Analytics real-world use cases Description Data is the key input for Analytics. Building and implementing data platforms such as Data Lakes, modern Data Marts, and Analytics at scale require the right cloud platform that Azure provides through its services. The book starts by sharing how analytics has evolved and continues to evolve. Following the introduction, you will deep dive into ingestion technologies. You will learn about Data processing services in Azure. You will next learn about what is meant by a Data Lake and understand how Azure Data Lake Storage is used for analytical workloads. You will then learn about critical services that will provide actual Machine Learning capabilities in Azure. The book also talks about Azure Data Catalog for cataloging, Azure AD for Access Management, Web Apps and PowerApps for cloud web applications, Cognitive services for Speech, Vision, Search and Language, Azure VM for computing and Data Science VMs, Functions as serverless computing, Kubernetes and Containers as deployment options. Towards the end, the book discusses two use cases on Analytics. What will you learn Explore and work with various Azure services Orchestrate and ingest data using Azure Data Factory Learn how to use Azure Stream Analytics Get to know more about Synapse Analytics and its features Learn how to use Azure Analysis Services and its functionalities Who this book is for This book is for anyone who has basic to intermediate knowledge of cloud and analytics concepts and wants to use Microsoft Azure for Data Analytics. This book will also benefit Data Scientists who want to use Azure for Machine Learning. Table of Contents 1. Data and its power 2. Evolution of Analytics and its Types 3. Internet of Things 4. AI and ML 5. Why cloud 6. What are a data lake and a modern datamart 7. Introduction to Azure services 8. Types of data 9. Azure Data Factory 10. Stream Analytics 11. Azure Data Lake Store and Azure Storage 12. Cosmos DB 13. Synapse Analytics 14. Azure Databricks 15. Azure Analysis Services 16. Power BI 17. Azure Machine Learning 18. Sample Architectures and synergies - Real-Time and Batch 19. Azure Data Catalog 20. Azure Active Directory 21. Azure Webapps 22. Power apps 23. Time Series Insights 24. Azure Cognitive Services 25. Azure Logicapps 26. Azure VM 27. Azure Functions 28. Azure Containers 29. Azure Kubernetes Service 30. Use Case 1 31. Use Case 2 About the Authors Prashila Naik has over 16 years of experience in the tech sector. She has worked for multiple global organizations, primarily in the data and analytics space. She has seen data and analytics grow from strength to strength and thinks it will always be one of the most interesting areas in technology ever. She She is also a writer who primarily writes creative fiction and non-fiction, as well as an occasional translator. Her short stories have been published in various leading literary journals in India and elsewhere. Your LinkedIn Profile: https://www.linkedin.com/in/prashila-naik-7645604

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.

Learn, develop, test and document powerful yet simple RAML API specifications using MuleSoft API Designer and API Toolkit. Key Features Explore concept of API and its significance in enterprise applications Design your own API using Mulesoft Anypoint Platform Exciting coverage on how API works in Enterprise Applications Live demonstration on how to build and integrate API with end-to-end implementation and working code Description Hands-on MuleSoft Anypoint platform book directs you step-by-step in designing API, its Implementation, and how to integrate smartly with other applications. This book is enriched with lots of interactive screenshots and working source codes. Throughout this book, you will learn key industry insights on System Integration, API Led Connectivity, Centre for Enablement, and RAML. This book will talk about how to use publicly available free mock REST APIs and how to call and test them from RESTful clients like Postman. You can also see some of the commercially available license-based APIs. Equipped with exercises, you will practice developing your own RESTful API specification along with how to add, retrieve, update, and delete data for your business use. You will be using the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. At the end of the book, you will be summarizing your learnings with an end-to-end implementation demonstration on the API design and its implementation. What you will learn Know-how of public APIs, commercial APIs, and cloud-based SaaS APIs Role of Mulesoft in SaaS applications You learn to design and test the API development and implementation You get handy with all the features and mechanism of Mulesoft Anypoint Platform Who this book is for This book is for fresher, IT employees with less or no programming background such as Business Analysts, Quality Engineers, HR, Technical persons who are looking for a change in technology area if they are working in outdated technologies. Table of Contents 1. MuleSoft Fundamentals 2. MuleSoft Internals 3. MuleSoft Salient features 4. From ESB to API Led Connectivity 5. Cloud based SaaS Applications and MuleSoft Connectors 6. REST, SOAP, Postman and Anypoint Studio 7. Start RAML 8. RAML in detail 9. RAML Project About the Authors Nanda Nachimuthu is an Engineering graduate from Tamilnadu Agricultural University, Coimbatore, and Tamilnadu and has done Advanced Diploma from 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 on Strategies for International Business. His 25 years of experience comes from various domains like Banking, Healthcare, Government and Airlines. He is, in to the technologies like Java, Big Data, Cloud, ESB, Security and IoT. He played various roles like Technical Architect, Solutions Architect, Cloud Architect and Enterprise Integration Architect and wanted to be in an Individual Contributor role always with hands-on coding experience. He is passionate about Social Entrepreneurship and Pro Bono consultations in multiple fields like Information Technology, Manufacturing, Trading, Agriculture and Internet of Things. He is the founder of some social platforms, and he owns few trademarks under his kitty. Presently he is focusing on Integration Technology Platforms like MuleSoft, where he finds lots of scope in the future for Digital Marketing and Machine to Machine Communications. LinkedIn Profile: https://www.linkedin.com/in/nanda3008/ Github: https://github.com/nanda3008