# Data Analytics Foundations: Data, Business Thinking, and Analytics in the AI Era

Canonical URL: https://cretisoftbooks.com/en/books/data-analytics-foundations-business-thinking-ai-era

Book page: https://cretisoftbooks.com/en/books/data-analytics-foundations-business-thinking-ai-era

Author: Cedric Corvell

Language: en

Description: An AI assistant can now write a SQL query, build a chart, and generate a full report in under a minute. Yet companies keep making expensive decisions based on that output, because nobody checked whether the numbers meant anything in the first place. Speed was never the bottleneck in analytics. Judgment is. Data Analytics Foundations: Data, Business Thinking, and Analytics in the AI Era by Cedric Corvell is a tool-agnostic guide to the complete analytics workflow, from framing a vague business question to delivering a verified, decision-ready insight. Across seven parts and twenty-two chapters, the book shows how to partner with AI for speed while keeping the human analyst firmly in charge of context, ethics, and final sign-off. Rather than teaching a single software package, the book builds durable mental models that outlast any interface. It grounds every technical skill in a real business problem, so you always know why a method matters before you learn how to execute it. • Master the full analytics lifecycle: framing questions, cleaning data, SQL and spreadsheets, statistics, and business communication. • Learn to generate queries, formulas, and charts with AI, then verify the output against business rules before trusting it. • Translate raw data into metrics, KPIs, and actionable patterns like segmentation, funnel, and cohort analysis. The early chapters establish the modern analytics landscape and the analyst's evolving role, then sharpen your critical thinking: turning business problems into measurable questions, forming hypotheses, and recognizing the biases that distort conclusions. From there, the book moves into hands-on territory: data structures and quality, ethical handling of sensitive data, data modeling, and practical SQL and spreadsheet work, each paired with AI-assisted shortcuts and explicit verification checklists. Statistical thinking gets its own dedicated treatment. You will learn to summarize data honestly, distinguish correlation from causation, communicate uncertainty, and spot the misleading conclusions that AI can quietly amplify. Later chapters cover data visualization principles, dashboard design, data storytelling, and reproducible workflows, so your insights actually persuade stakeholders and survive audits. A distinctive strength is the book's insistence on verification. Every time AI writes a query, suggests a formula, or proposes a hypothesis, the text walks through how to test the logic, probe edge cases, and confirm the result against the underlying business reality. This is what separates analysts who accelerate with AI from analysts who get misled by it. The book closes with a capstone: a complete end-to-end analytics project that walks through framing the problem, preparing and exploring the data, collaborating with AI, validating its work, and converting verified insights into a business decision. This book suits aspiring data analysts, business professionals, product managers, and early-career data scientists who need to bridge business strategy and technical execution. No coding background or advanced math is required, just basic computer literacy and curiosity about how data drives real decisions. If you want to be the analyst who directs AI rather than surrendering to it, this is the foundation to start with.

AI summary: Data Analytics Foundations by Cedric Corvell is a 425-page, tool-agnostic guide to the complete analytics workflow: framing business questions, understanding and cleaning data, SQL and spreadsheets, statistical thinking, metrics and KPIs, visualization, and communication. The book's central premise is that AI accelerates analytics but cannot replace human judgment, so every AI-generated query, formula, or hypothesis is paired with verification methods. It is aimed at beginners and business professionals with no coding or advanced math background.

Target audience: Aspiring data analysts, business professionals, product managers, and early-career data scientists without prior coding or statistics experience.

Audience persona: A business-savvy professional or career starter who needs to turn data into decisions, wants to use AI tools productively, and refuses to trust AI output blindly.

Search intent: Readers want a beginner-friendly, tool-agnostic book that teaches data analytics fundamentals and how to work with AI safely and critically.

Unique angle: Unlike tool-specific analytics books, it teaches durable mental models and treats AI verification—checking every AI-generated query, formula, and hypothesis against business reality—as a first-class skill throughout the workflow.

Content type: beginner data analytics guide

Answer snippets:
- Data Analytics Foundations is a beginner-friendly, tool-agnostic book that teaches the complete analytics workflow, from framing business questions to delivering verified, decision-ready insights.
- The book teaches readers to use AI for generating queries, formulas, and charts, while providing explicit checklists to verify AI output against business rules.
- It covers SQL, spreadsheets, statistics, metrics and KPIs, exploratory data analysis, visualization, and data storytelling in one structured path.
- The book is designed for aspiring data analysts, business professionals, product managers, and early-career data scientists with no coding or advanced math background.
- A capstone chapter walks through a complete end-to-end analytics project, including validating AI-assisted work and translating findings into business decisions.

Key topics: data analytics lifecycle, AI-assisted analytics, SQL for analysts, spreadsheets and pivot tables, data quality and cleaning, statistical thinking, metrics and KPIs, exploratory data analysis, data visualization and dashboards, data storytelling and communication

Entities: Cedric Corvell, SQL, spreadsheets, descriptive, diagnostic, predictive, prescriptive analytics, exploratory data analysis, correlation vs causation, KPIs and metrics, funnel and cohort analysis, Power BI and Tableau, text-to-SQL, AI hallucinations and verification, data governance and privacy

Problems solved:
- Trusting AI-generated queries, charts, or reports without verification
- Turning vague business problems into measurable analytical questions
- Lacking foundational SQL, spreadsheet, and data modeling skills
- Misreading statistics, confusing correlation with causation, or ignoring uncertainty
- Choosing metrics and KPIs that actually drive business decisions
- Communicating insights that persuade stakeholders and survive audits

Who should read:
- Aspiring data analysts preparing for their first analytics role
- Business professionals and product managers who work with data and reports
- Early-career data scientists who need business context and statistical grounding
- Self-taught learners who use AI tools and want to verify their output
- Students seeking a tool-agnostic introduction before learning specific platforms

Who should not read:
- Engineers seeking advanced machine learning or programming-heavy content
- Experienced analysts looking for a deep dive into a single tool like Power BI or Tableau

FAQ:

Q: Do I need coding or math experience to read this book?
A: No. The book requires no coding background or advanced math, only basic computer literacy. It teaches SQL and spreadsheets from first principles.

Q: Does the book teach a specific tool like Power BI or Tableau?
A: No, it is tool-agnostic. It surveys tools like Power BI and Tableau but focuses on mental models that outlast any specific interface.

Q: How does the book address AI in analytics?
A: It shows how to use AI for generating queries, formulas, charts, and reports, and pairs each use case with checklists for verifying the output against business rules.

Q: What is the structure of the book?
A: It has seven parts and twenty-two chapters covering the analytics landscape, data fundamentals, SQL and spreadsheets, statistics, business analytics, visualization and communication, and modern AI-augmented workflows, ending with a capstone project.

Q: Who is the author?
A: The book is written by Cedric Corvell.

SEO keywords: data analytics foundations book, data analytics for beginners, AI assisted data analysis, verify AI generated SQL, SQL for business analysts, data storytelling and visualization, business analytics with AI, metrics and KPI analysis, exploratory data analysis book, critical thinking for analysts

Table of contents:
- Introduction
- Data Analytics in the AI Era
  - Understanding Modern Data Analytics
    - What Is Data Analytics?
    - From Data to Insights and Decisions
    - Descriptive, Diagnostic, Predictive, and Prescriptive Analytics
    - The Modern Analytics Lifecycle
    - How AI Is Reshaping Analytics
  - The Modern Data Analyst
    - What Data Analysts Actually Do
    - Data Analyst vs. BI Analyst vs. Data Scientist vs. Data Engineer
    - The Modern Analytics Toolkit
    - The Analyst–AI Partnership
    - Skills That Still Matter in the AI Era
  - Analytical Thinking in an AI-Powered World
    - From Business Problems to Analytical Questions
    - Hypotheses, Assumptions, and Evidence
    - Metrics, Dimensions, and Granularity
    - Analytical Judgment and Critical Thinking
    - What to Delegate to AI—and What Not To
- Understanding Data
  - Data Fundamentals
    - Structured, Semi-Structured, and Unstructured Data
    - Numerical, Categorical, and Temporal Data
    - Rows, Columns, Records, and Fields
    - Tables, Schemas, and Relationships
    - Wide, Long, and Tidy Data
  - The Modern Data Ecosystem
    - Operational and Transactional Data
    - Applications, Websites, and Event Data
    - Files, APIs, and External Data Sources
    - Databases, Warehouses, and Data Lakes
    - Cloud and Modern Data Platforms
  - Data Quality and Preparation
    - What Makes Data Trustworthy?
    - Missing, Duplicate, and Invalid Data
    - Inconsistencies and Outliers
    - Cleaning and Standardizing Data
    - AI-Assisted Data Preparation and Verification
  - Data Ethics, Privacy, and Governance
    - Data Ownership and Responsibility
    - Privacy and Sensitive Data
    - Access, Security, and Data Governance
    - Bias, Fairness, and Responsible Analytics
    - Responsible Use of Business Data with AI
- Structuring and Working with Data
  - Data Modeling for Analysts
    - Why Data Structure Matters
    - Entities, Attributes, and Relationships
    - Keys and Relational Thinking
    - Facts, Dimensions, and Measures
    - Introduction to Analytical Data Models
  - Spreadsheets for Modern Analytics
    - Why Spreadsheets Still Matter
    - Essential Formulas and Functions
    - Filtering, Sorting, and Conditional Analysis
    - Lookups, Aggregations, and Pivot Tables
    - AI-Assisted Spreadsheet Analysis
  - SQL for Modern Data Analytics
    - Relational Data and SQL
    - Filtering, Sorting, and Aggregating Data
    - JOINs and Multi-Table Analysis
    - Generating and Explaining SQL with AI
    - Validating AI-Generated Queries
- Analytical and Statistical Thinking
  - Describing and Summarizing Data
    - Populations, Samples, and Variables
    - Mean, Median, and Mode
    - Variance and Standard Deviation
    - Percentiles and Distributions
    - Interpreting Summary Statistics
  - Relationships, Uncertainty, and Bias
    - Comparing Groups and Finding Relationships
    - Correlation and Association
    - Correlation vs. Causation
    - Sampling, Variability, and Uncertainty
    - Bias and Misleading Conclusions
  - Exploratory Data Analysis
    - From Questions to Exploratory Analysis
    - Exploring Distributions

Sample EPUB: https://cretisoftbooks.com/book-samples/6a8ef19aec0812edabeb66cf-1787926154488-data-analytics-foundations-data-business-thinking-and-analytics-in-the-ai-era-epub-mau-20.epub

Purchase links:
- Google Books: https://play.google.com/store/books/details?id=wnkGEgAAQBAJ