9Natree

Google BigQuery (Valliappa Lakshmanan)

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- Interesting Topics:
- Serverless Warehousing Depends on Separating Storage from Compute
- Data Ingestion and Transformation Require Deliberate ETL, EL, and ELT Choices
- SQL at Scale Includes Nested Data, Analytical Functions, and Cross-Dataset Insight
- Performance and Cost Are Shaped by Physical Data Design and Query Behavior
- BigQuery ML Brings Predictive Modeling Closer to Warehouse Data

- Read more: https://english.9natree.com/read/1492044466/


Google BigQuery: The Definitive Guide is a practical technical reference on using Google Cloud BigQuery for large-scale data warehousing, analytics, and machine learning. Written by Valliappa Lakshmanan and Jordan Tigani, it explains both the user-facing workflow of querying and managing data and the architectural choices that make BigQuery a serverless distributed SQL system. The book addresses the needs of data engineers, analysts, data scientists, and technical leaders who work with datasets too large or too dynamic for conventional database practices. Its scope moves from data processing concepts and SQL analysis to ingestion, performance, security, operational design, and BigQuery ML. Rather than treating the platform as only a query engine, the guide presents it as part of a broader cloud data environment, with connections to pipelines, business intelligence tools, geospatial analysis, and machine learning services. The central purpose is to help readers make technically sound decisions about storing, transforming, querying, and learning from data at substantial scale.

Learn SQL Quickly (Code Quickly)

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- Interesting Topics:
- Relational database foundations before query syntax
- Data definition establishes the structure queries depend on
- SELECT, filtering, and sorting turn stored records into answers
- Joins explain how separate tables become usable business data
- A hands-on project makes SQL practice part of the learning method

- Read more: https://english.9natree.com/read/B09X6371JL/


Learn SQL Quickly: A Beginner’s Guide to Learning SQL, Even If You’re New to Databases is an introductory programming guide by Code Quickly. Positioned as a crash course, it addresses readers who need a working foundation in relational databases and SQL without assuming previous technical training. The book combines explanations of database concepts with examples of common commands and a hands-on project intended to move learning beyond passive reading. Its stated coverage includes setting up a database management environment, creating and modifying database structures, retrieving records, filtering and sorting results, and connecting related tables. The practical emphasis is important because SQL competence depends on translating a question about data into a precise query, then checking whether the result is meaningful. The material appears to use MySQL as its principal working environment, so readers should treat its syntax and setup guidance as especially relevant to that platform. Rather than serving as an exhaustive database reference, the book is best understood as a compact starting point for learning how structured data is organized, queried, and maintained.

Data Analytics Essentials You Always Wanted To Know (Vibrant Publishers)

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- Interesting Topics:
- From raw data to decision-ready insight
- Tools and analyst workflows without treating technology as the whole discipline
- Big data as an opportunity constrained by scale, quality, and implementation challenges
- Ethics, privacy, and regulation as core conditions of responsible analytics
- Case studies and self-assessment connect foundational concepts to business use

- Read more: https://english.9natree.com/read/163651118X/


Data Analytics Essentials You Always Wanted To Know is an introductory business and technology guide published by Vibrant Publishers in its Self-Learning Management Series. It is designed to explain how organizations turn raw data into information that can support decisions, rather than to serve as an advanced technical manual for experienced statisticians or data scientists. The book surveys the principal forms of analytics, common processes and tools, the everyday work associated with an analyst role, and the expanding challenges of big data. It also gives substantial attention to the nontechnical conditions of responsible analysis, including privacy, ethics, and regulatory frameworks. Its teaching approach combines concise explanations with chapter summaries, self-assessment quizzes, and applied case studies. These cases include recognizable examples from retail, streaming recommendations, and healthcare, helping connect analytical concepts to organizational outcomes. The result is a broad foundation for readers who need data literacy, are considering an analytics career, or want to understand how analytical work informs business operations.

Data Mining Techniques (Gordon S. Linoff)

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- Interesting Topics:
- Business Questions Determine the Appropriate Data Mining Method
- Data Preparation Is Treated as the Foundation of Reliable Customer Models
- Predictive Modeling Supports Targeted Acquisition, Retention, and Credit Decisions
- Association Rules and Collaborative Filtering Reveal Opportunities Beyond Individual Scores
- Model Deployment Requires Measurement, Feedback, and Organizational Infrastructure

- Read more: https://english.9natree.com/read/B004UB2KE4/


Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management is a practical business analytics guide by Gordon S. Linoff and Michael J. A. Berry. Its third edition, published by Wiley in 2011, explains how organizations can turn customer, transaction, and interaction data into decisions that improve acquisition, retention, cross selling, service, and risk management. Rather than presenting data mining as an isolated technical discipline, the book frames it around recurring commercial questions: Which prospects are most likely to respond, which customers are likely to leave, what products tend to be purchased together, and how should limited marketing resources be allocated. The authors introduce a broad set of methods, from traditional statistical approaches to decision trees, neural networks, association rules, collaborative filtering, link analysis, and survival analysis. It is an introductory but applied work, emphasizing data preparation, evaluation, deployment, and organizational infrastructure alongside modeling. Its central purpose is to help business practitioners connect analytical techniques to measurable customer relationship management actions.

Streaming, Sharing, Stealing: Big Data and the Future of Entertainment (Michael D. Smith)

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- Interesting Topics:
- Data Replaces Intuition in Content and Audience Decisions
- Streaming Platforms Redefine Distribution, Discovery, and Market Power
- Price Discrimination Becomes More Feasible and More Complex Online
- Piracy Is An Economic Problem, Not a Complete Explanation for Industry Decline
- Incumbents Must Organize Analytics Around Strategy Rather Than Legacy Silos

- Read more: https://english.9natree.com/read/0262034794/


Streaming, Sharing, Stealing: Big Data and the Future of Entertainment is a 2016 nonfiction analysis by Michael D. Smith and Rahul Telang, scholars of information systems, marketing, and entertainment economics. The book examines how internet distribution, digital platforms, and consumer data altered the economics of television, film, music, and publishing. Its purpose is not simply to celebrate streaming services or condemn piracy. Instead, it assesses how established creative businesses made decisions under technological change, often relying on inherited assumptions rather than evidence about actual customer behavior. Smith and Telang use research, industry examples, and economic reasoning to explain why firms such as Netflix, Amazon, Apple, and Spotify gained strategic influence. They address content discovery, the long tail, differentiated products, pricing, platform competition, and unauthorized copying. A central claim is that abundant behavioral data can improve decisions about what to produce, how to distribute it, and what customers may pay. The book consequently treats entertainment as both a cultural sector and a data-intensive market, where adaptation depends on testing assumptions rather than defending older distribution systems.

Canvas LMS For Dummies (Marcus Painter)

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- Interesting Topics:
- Canvas Is Treated as a Teaching Environment Rather Than a Storage Site
- The Panda Primer and Course Setup Build a Usable Starting Structure
- The Rich Content Editor Connects Existing Materials to Digital Course Design
- Inbox, Calendar, and Communication Routines Reduce Operational Friction
- Modules, Deliverables, and Grading Turn Course Activity into a Coherent Workflow

- Read more: https://english.9natree.com/read/1119828422/


Canvas LMS For Dummies is a practical technology and education guide to using Canvas, the learning management system widely adopted in schools and higher education. Published in 2022, it is written by Marcus Painter and Eddie Small, both former educators and Instructure leaders with direct experience supporting Canvas users. The book belongs to the instructional software manual genre, but its purpose extends beyond explaining menus and settings. It frames Canvas as an environment for organizing teaching, communication, assessment, collaboration, and blended learning. Its three-part structure moves from introductory orientation and a change in instructional thinking to course construction and the recurring work of communication, modules, assignments, and grading. The authors use approachable labels such as Panda Primer, Rolling in the RCE, and Deliverables Tree to make platform functions easier to remember. Rather than presenting digital learning as a complete substitute for classroom teaching, the guide concentrates on integrating online course spaces with existing in-person materials and routines. It is primarily designed for beginners, though experienced educators may find its emphasis on coherent course design useful.

Excel for Busy Professionals (Mark J. Roger)

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- Interesting Topics:
- A Seven-Minute Learning Format for Time-Constrained Professionals
- Pivot Tables as a Faster Route from Raw Data to Summaries
- VLOOKUP and XLOOKUP for Reliable Cross-Table Data Retrieval
- Dashboards and KPIs for Clearer Operational Reporting
- Filtering, Sorting, and Introductory VBA to Reduce Repetitive Work

- Read more: https://english.9natree.com/read/1806476509/


Excel for Busy Professionals by Mark J. Roger is a practical workplace-skills guide aimed at people who need to improve their spreadsheet work without committing to a lengthy technical course. Presented as a seven-minute-a-day crash course, it focuses on commonly used Excel capabilities that support reporting, data analysis, routine administration, and managerial communication. The available description positions the book around a clear problem: professionals often lose time to manual cleanup, fragile formulas, repetitive updates, and difficulty answering data questions quickly. Roger addresses this problem through tools such as Pivot Tables, VLOOKUP, XLOOKUP, dashboards, filtering and sorting, key performance indicators, and introductory VBA automation. Rather than treating Excel as a purely technical subject, the book frames it as an operational tool whose quality affects reliability and perceived competence at work. Its stated purpose is to help nontechnical and time-constrained readers develop usable systems, reduce avoidable errors, and apply spreadsheet skills directly to recurring workplace tasks.

Learning Spark: Lightning-Fast Data Analytics (Jules S. Damji)

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https://www.amazon.com/dp/1492050040?tag=9natree-20

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- Interesting Topics:
- Structured APIs replace much of the complexity of low-level distributed processing
- Execution planning, the Spark UI, and configuration connect code to real performance
- Data source integration makes Spark a practical layer for heterogeneous analytics
- Structured Streaming applies a consistent model to batch and continuous data
- Delta Lake, MLlib, and MLflow extend Spark from analysis into managed pipelines

- Read more: https://english.9natree.com/read/1492050040/


Learning Spark: Lightning-Fast Data Analytics is a technical, practice-oriented guide to Apache Spark, written for data engineers and data scientists working with data too large, varied, or fast-moving for single-machine workflows. The second edition, updated for Spark 3.0, presents Spark not merely as a distributed computation engine but as a unified platform for SQL analytics, batch processing, streaming, and machine learning. Its organizing idea is that structured interfaces, especially DataFrames, Datasets, and Spark SQL, make distributed processing more accessible and more optimizable than lower-level approaches alone. The book combines conceptual explanations with code-driven walkthroughs across Python, SQL, Scala, and Java. It addresses the full path from establishing a Spark application and reading external data to inspecting execution plans, tuning jobs, building streaming workloads, and managing machine learning pipelines. Rather than focusing solely on isolated APIs, it emphasizes how Spark components interact in production-oriented analytical systems.

Practical Linear Algebra for Data Science (Mike X Cohen)

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- Interesting Topics:
- Vectors and matrices as computational representations of data and transformations
- Matrix arithmetic and dimensional structure behind numerical algorithms
- Independence, rank, and inverses as tests of information and solvability
- LU and QR decompositions as practical alternatives to direct matrix inversion
- Eigendecomposition and singular value decomposition for modeling and dimensionality reduction

- Read more: https://english.9natree.com/read/1098120612/


Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python is a technical, application-oriented introduction to the mathematical structures underlying computational analysis. Written by Mike X Cohen, it presents linear algebra not chiefly as a proof-centered academic subject, but as a working language for algorithms implemented on computers. The book connects fundamental objects such as vectors and matrices to Python-based computation and to domains including data science, machine learning, deep learning, simulation, and biomedical data processing. Its stated coverage spans matrix arithmetic, linear independence, rank, inverses, LU and QR decompositions, eigendecomposition, singular value decomposition, least-squares fitting, and principal components analysis. This scope gives readers both the vocabulary for interpreting common numerical methods and the operational perspective needed to implement them. Rather than treating formulas as isolated rules, the book positions them as descriptions of transformations, representations of data, and tools for solving structured problems. It is therefore best understood as a bridge between foundational linear algebra and the matrix-based workflows used in modern scientific and technical computing.

Think Bayes: Bayesian Statistics in Python (Allen B. Downey)

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- Interesting Topics:
- Bayesian Updating Is Presented as an Explicit Model of Changing Belief
- Discrete Probability Distributions Replace Much of the Usual Calculus
- Estimation and Prediction Are Treated as Distributional Questions
- Evidence, Hypothesis Testing, and Decisions Depend on Competing Models
- Simulation and Multidimensional Models Connect Fundamentals to Real Data

- Read more: https://english.9natree.com/read/149208946X/


Think Bayes: Bayesian Statistics in Python is a practical introduction to Bayesian inference by computer scientist Allen B. Downey. Written for readers who can program in Python and have some familiarity with probability, the book treats statistics primarily as a computational activity rather than a calculus-heavy branch of mathematics. Its central purpose is to show how prior beliefs can be represented as probability distributions, revised with observed evidence, and used to answer questions about uncertainty. The second edition uses Python, Jupyter notebooks, and discrete approximations to make the mechanics of Bayesian updating inspectable and executable. It begins with small probability puzzles involving coins, dice, cookies, and similar examples before moving toward estimation, prediction, decision analysis, hypothesis testing, simulation, and models with multiple parameters. Rather than presenting Bayesian methods as a collection of formulas to memorize, Downey emphasizes a reusable workflow: state assumptions, encode uncertainty, update the model with data, and interpret the resulting distribution in the context of a real decision or prediction.