9Natree

Everybody Lies (Seth Stephens-Davidowitz)

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- Interesting Topics:
- Search behavior as evidence against social desirability bias
- The four methodological powers attributed to big data
- Hidden prejudice and the gap between public language and private inquiry
- Digital traces reveal private concerns about sex, parenting, and health
- From observation to prediction and responsible real world use

- Read more: https://english.9natree.com/read/B06XCYD5KG/


Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are is a nonfiction work of popular social science by Seth Stephens-Davidowitz, an economist and former Google data scientist. The book examines what digital traces, especially internet searches, can reveal about private beliefs, fears, desires, and behavior. Its central claim is not that search data is flawless, but that it can expose forms of self-presentation that distort surveys, polls, and social media. People often give answers that sound acceptable to others, whereas a search box can become a relatively private place to ask embarrassing or troubling questions. Stephens-Davidowitz combines search trends with administrative records, experiments, and other large datasets to investigate subjects including politics, prejudice, sexuality, parenting, health, and economic behavior. The purpose is both explanatory and methodological: to show how new sources of data can improve social inquiry when researchers ask careful questions. Written for general readers, the book translates data analysis into accessible examples while also raising questions about inference, privacy, and the responsible interpretation of digital evidence.

Python for Data Analysis (Wes McKinney)

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- Interesting Topics:
- Interactive Computing Turns Analysis into an Inspectable Process
- NumPy Provides the Array Logic Behind Efficient Numerical Work
- pandas Makes Labeled Tables Suitable for Real World Data Wrangling
- Grouping and Reshaping Convert Records into Comparable Summaries
- Time Aware Data Requires Explicit Treatment of Dates, Frequency, and Irregularity

- Read more: https://english.9natree.com/read/109810403X/


Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter is a technical, practice-oriented guide to the core Python tools used to prepare, inspect, transform, and summarize structured data. Written by Wes McKinney, the creator of pandas, the third edition centers on the working skills required for data-intensive computing rather than on statistical theory or a survey of analytical methods. It introduces relevant Python language features and interactive work in IPython and Jupyter before developing the numerical foundations supplied by NumPy and the labeled tabular structures supplied by pandas. From there, the book addresses the everyday mechanics of an analysis workflow: importing files and external data, resolving incomplete or inconsistent values, combining datasets, reshaping tables, producing basic plots, and handling dates and time series. Examples and accompanying notebooks make the material executable rather than purely conceptual. Its purpose is to help readers understand not only which library operations exist, but also how those operations fit together into reliable data-wrangling processes.

Trustworthy Online Controlled Experiments (Ron Kohavi)

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- Interesting Topics:
- Randomization Creates a Credible Basis for Causal Product Decisions
- Reliable Metrics Connect Local Changes to Durable Product Value
- Statistical Significance Does Not by Itself Make an Experiment Trustworthy
- Sanity Checks Detect Assignment and Data Failures Before Decisions Are Made
- A Mature Experimentation Culture Learns From Replication and Accumulated Evidence

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Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing is a technical and managerial guide to designing, running, and interpreting online randomized controlled experiments. Written by Ron Kohavi, Diane Tang, and Ya Xu, it draws on experience with experimentation programs at major technology companies and addresses the realities of testing changes on websites, applications, and digital services. Its purpose is not merely to explain how to compare a treatment with a control. Instead, it shows why apparently simple A/B tests can produce misleading results when assignment, instrumentation, metrics, statistical inference, or organizational processes are weak. The book combines foundations of causal experimentation with operational advice for teams that must make product decisions quickly and repeatedly. It treats experimentation as an institutional capability: reliable results require sound methodology, automated quality checks, shared metrics, and a culture willing to accept evidence that challenges intuition. The central standard is trustworthiness, meaning that a reported effect should be credible enough to support a real decision rather than simply pass a statistical threshold.

Learning SQL: Generate, Manipulate, and Retrieve Data (Alan Beaulieu)

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- Interesting Topics:
- A MySQL Based Path from Database Setup to First Queries
- Data Manipulation Connects Retrieval with Safe Record Changes
- Relational Reasoning Through Joins, Sets, and Subqueries
- Schema Statements Make Data Quality Part of Database Design
- Advanced SQL Extends the Foundation to Analysis and Scale

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Learning SQL: Generate, Manipulate, and Retrieve Data is a practical introductory technical book by Alan Beaulieu, published by O Reilly Media in its third edition. It explains how to use Structured Query Language to work with relational databases, beginning with the basic concepts needed by readers who have no prior database experience. The book uses MySQL as a learning environment while concentrating on broadly applicable SQL ideas. Its scope extends beyond simple data retrieval: readers are introduced to creating database structures, inserting and changing records, enforcing data rules, joining related tables, and shaping results for reports or applications. Beaulieu organizes the material as focused lessons supported by annotated examples, illustrations, and exercises. Later material addresses more demanding concerns, including subqueries, analytic functions, SQL and big data, and work with very large databases. The result is a foundation-oriented guide whose purpose is to help developers, analysts, administrators, and report writers understand both the language of SQL and the relational reasoning that makes queries reliable.

How to Win the Premier League (Ian Graham)

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- Interesting Topics:
- Results Must Be Separated From the Quality of Performance
- Recruitment Depends on Measuring Actions That Create Future Value
- Analytics Creates an Edge Only When It Influences Club Decisions
- Football Data Reveals Market Inefficiencies Rather Than Guaranteed Winners
- Home Advantage and Greatness Can Be Reconsidered With Better Evidence

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How to Win the Premier League is a nonfiction account of how statistical analysis became integral to elite football decision making. Its author, Ian Graham, was Liverpool FC Director of Research from 2012 to 2023, a period that included the club winning the Champions League and its first league title in three decades. Rather than presenting data as a secret formula or a replacement for coaches, Graham examines the organizational work required to make analytical evidence useful. The book combines an insider perspective on Liverpool with an accessible explanation of ideas that changed recruitment, player evaluation, tactical assessment, and judgments of managerial performance. Prominent decisions, including the appointment of Jürgen Klopp and the signing of Mohamed Salah, provide practical context for the broader argument. Graham repeatedly distinguishes results from the quality of the process that produced them, showing why league tables, goals, and reputations can mislead. The purpose is both explanatory and corrective: to show what football data can measure, what it cannot settle alone, and why a club gains an advantage only when its leaders are prepared to act on better evidence.

Practical SQL, 2nd Edition (Anthony DeBarros)

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- Interesting Topics:
- SQL Is Treated as an Investigative Language Rather Than a List of Commands
- PostgreSQL Fundamentals Are Built Through Database Design and Reproducible Hands-On Work
- Aggregation, Functions, and Joins Turn Raw Records into Comparable Evidence
- Data Cleaning Is Presented as a Prerequisite for Trustworthy Analysis
- Spatial Queries and JSON Expand SQL Beyond Conventional Flat Tables

- Read more: https://english.9natree.com/read/1718501064/


Practical SQL, 2nd Edition by Anthony DeBarros is a beginner-oriented technical guide to using SQL for investigation, analysis, and clear evidence-based reporting. Rather than presenting database commands as isolated syntax to memorize, it frames querying as a way to ask progressively sharper questions of structured data. The book uses PostgreSQL and its pgAdmin interface for its hands-on work, while explaining principles that transfer broadly to other relational database systems. Its examples draw on public, recognizable datasets, including demographic information from the US Census, New York City taxi trips, and US Geological Survey earthquake records. The second edition updates the learning environment and expands coverage of newer practical needs, notably more advanced data-wrangling queries and PostgreSQL support for JSON. It moves from database construction and core query logic toward cleaning, statistical functions, spatial analysis, and more sophisticated query structures. The purpose is not merely to make readers capable of retrieving rows, but to help them evaluate data quality, identify patterns, and turn results into defensible findings.

Fundamentals of Data Engineering: Plan and Build Robust Data Systems (Joe Reis)

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- Interesting Topics:
- The Data Engineering Lifecycle Connects Isolated Tasks into an End-to-End System
- Technology Choices Should Follow Requirements and Trade-Offs Rather Than Market Hype
- Architecture Must Combine Reusable Foundations with Flexibility for Local Needs
- Governance, Security, and Data Quality Belong in Every Lifecycle Stage
- Data Engineering Is an Organizational Service for Downstream Data Consumers

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Fundamentals of Data Engineering: Plan and Build Robust Data Systems is a practical, conceptual guide to the discipline of turning raw data into reliable information for analytics, business intelligence, and machine learning. Written by Joe Reis and Matt Housley, it is not a programming manual or a catalog of vendor tools. Instead, it offers a way to reason about data systems amid rapidly changing cloud platforms, databases, and processing frameworks. Its central organizing model is the Data Engineering Lifecycle, which follows data from its generation through ingestion, orchestration, transformation, storage, serving, and governance. The book explains that durable data engineering depends less on selecting a fashionable product than on understanding requirements, constraints, trade-offs, and downstream users. It also situates technical work within organizational realities, including costs, security, team structure, and business goals. The result is a broad foundation for readers who need to design, assess, or lead modern data systems without confusing a particular technology stack with the underlying engineering discipline.

New Perspectives Microsoft Office 365 & Access 2019 Comprehensive (Mark Shellman)

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- Interesting Topics:
- Relational database design is treated as the foundation of effective Access work
- Queries develop the ability to retrieve, calculate, and interpret selected data
- Forms and reports connect stored data to everyday input and communication needs
- Project-based instruction links Access features to realistic problem-solving workflows
- Comprehensive coverage supports progression from basic objects to maintainable databases

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New Perspectives Microsoft Office 365 and Access 2019 Comprehensive is a college-level instructional textbook by Mark Shellman and Sasha Vodnik, published for the Cengage MindTap courseware ecosystem. Its subject is Microsoft Access 2019, presented within the broader Office 365 environment. Rather than functioning as a simple command reference, the book is positioned as a comprehensive, project-oriented guide to building, managing, and analyzing relational databases. Its stated instructional approach combines detailed software coverage with critical thinking, problem solving, and applications resembling workplace database tasks. Readers are guided through the major components of an Access database, including tables, relationships, queries, forms, reports, and database-management features. The comprehensive designation signals a substantial scope: it is intended to move beyond introductory data entry toward the design choices that make a database usable, accurate, and maintainable. The MindTap association also places the text in a structured learning setting, where reading can be paired with digital assignments and practice. It is principally a skills textbook for academic courses, though its database concepts are relevant to office and business information work.

Access 2019 For Dummies (Laurie A. Ulrich)

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- Interesting Topics:
- Database design begins with tables, fields, and usable data types
- Forms make routine data entry more controlled and approachable
- Queries convert stored records into answers and calculations
- Importing, cleaning, and managing outside data are part of database work
- Reports turn database results into shareable, structured output

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Access 2019 For Dummies is a beginner-oriented software guide to Microsoft Access 2019, written by Laurie A. Ulrich with Ken Cook. Published in the practical For Dummies style, it explains how a desktop database can organize information that would become difficult to maintain in ordinary spreadsheets or disconnected documents. The book moves from the Access workspace and basic database concepts to the everyday tasks of designing tables, entering data, creating forms, asking questions through queries, and presenting results in reports. Its purpose is operational rather than theoretical: readers are guided through the choices and interface actions needed to construct usable small-scale databases. It also addresses importing and managing data from external sources, an important concern for people whose records already exist elsewhere. The text assumes basic familiarity with computers and Windows rather than prior database training. It is therefore aimed at office users, students, and small organizations that need a structured introduction to Access, while remaining focused on core features rather than advanced automation such as macros.

Concepts of Database Management (Lisa Friedrichsen)

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- Interesting Topics:
- The relational model provides the book's organizing framework
- Normalization is presented as a practical defense against inconsistent data
- SQL instruction connects classroom queries to portable database skills
- Management topics treat databases as shared and governed systems
- Advanced trends broaden the course beyond desktop database construction

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Concepts of Database Management, 10th Edition, is an introductory database-management textbook by Lisa Friedrichsen and a collaborative author team, published by Cengage. It combines conceptual instruction with applied work, positioning the relational database as the central model for organizing, querying, and protecting organizational data. Its scope moves from basic database terminology and DBMS functions to relational design, Query By Example, SQL, normalization, relationships, administration, and selected industry developments. The edition reflects Microsoft Access 2019, Office 365 conventions, and SQL Server 2019, while presenting SQL in a broadly database-neutral manner. Rather than treating database work as only a programming exercise, the book emphasizes the chain of decisions that connects information requirements, table design, data integrity, retrieval, security, and ongoing management. Screenshots, cases, exercises, and MindTap practice materials support an instructional format intended for formal courses and independent learners who need structured, practical exposure to database concepts.