Data Analytics

A Beginner's Guide to Data Analytics Concepts, Tools, and Techniques

Build a solid foundation in data analytics — from raw data to actionable insight — using the methods and tools practitioners rely on every day.

A Anil Maheshwari

Data Analytics by Anil Maheshwari introduces the core concepts, tools, and techniques that turn raw data into decisions. Covering everything from data collection and preparation to statistical analysis, visualization, and predictive modeling, this 250-page textbook gives beginners a clear, structured path into the field. It is widely used in university courses and by self-learners who want a grounded, practical entry point rather than a shallow survey.

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About this book

Most people entering data analytics face the same problem: there is an overwhelming amount to learn and no clear order in which to learn it. Data Analytics by Anil Maheshwari solves that by laying out the field in a logical, progressive sequence — from foundational ideas to applied techniques — so you always know where you are and what comes next.

The book treats data analytics as a discipline with real structure. You start with what data is, where it comes from, and why its quality matters before a single calculation is performed. From there you move through the analytical pipeline: summarizing data statistically, visualizing patterns, applying predictive models, and communicating results to decision-makers. Each stage builds directly on the last.

Rather than surveying every tool on the market, the book focuses on durable concepts that transfer across tools and industries. When you understand why a technique works — not just how to click through a menu — you can adapt as the software landscape changes.

Core statistical concepts explained without assuming a mathematics background
Data collection, cleaning, and preparation techniques used in real projects
Visualization principles that make patterns visible and presentations credible
An introduction to predictive modeling and what distinguishes good models from overfit ones
The business context: how analytics connects to organizational decisions

This is the kind of textbook that works both in a structured course and as independent study. The concepts are precise enough to be useful, and the writing is accessible enough that you do not need a statistics degree to follow it. If you are starting out in data analytics and want a reliable map of the territory, this is a practical first book to own.

🎯 What you'll learn

Describe the data analytics pipeline from raw data collection through decision-ready insight
Assess data quality and apply common cleaning techniques before analysis begins
Apply core descriptive and inferential statistics without getting lost in mathematical notation
Choose the right visualization type for a given dataset and audience
Explain how predictive models work and identify the signs of overfitting or underfitting
Connect analytical findings to business problems and frame results for non-technical stakeholders
Identify which tools and techniques are appropriate for a given analytical task

👤 Who is this book for?

Students enrolled in business analytics, information systems, or data science programs who need a structured course text
Career changers who want a clear, honest introduction to what data analytics actually involves before committing to a bootcamp or graduate program
Business professionals who work alongside analysts and want enough fluency to ask the right questions and interpret results
Self-learners who prefer a book-length, coherent treatment over scattered online tutorials
Early-career analysts who picked up tools on the job but lack a solid conceptual foundation

01

Introduction to Data Analytics

Defines data analytics as a discipline, explains why it matters to organizations, and maps out the landscape of roles, tools, and techniques the rest of the book covers.
02

Data and Its Sources

Examines where data comes from — transactional systems, sensors, surveys, and the web — and explains the difference between structured and unstructured data.
03

Data Quality and Preparation

Covers the most time-consuming part of any real project: identifying missing values, handling duplicates, correcting formats, and transforming raw data into an analysis-ready state.
04

Descriptive Statistics

Introduces measures of central tendency, spread, and distribution shape, and shows how to summarize a dataset in a way that reveals its key characteristics.
05

Data Visualization

Explains the principles behind effective charts and graphs, walks through common visualization types, and shows how poor design choices obscure rather than reveal patterns.
06

Predictive Analytics and Modeling

Introduces regression, classification, and the concept of model training and validation, giving readers a conceptual grounding in how predictive models are built and evaluated.
07

Big Data and Analytics Tools

Surveys the technologies used when data volumes exceed what a spreadsheet can handle, including an overview of databases, cloud platforms, and widely used analytics software.
08

Analytics in Business Decision-Making

Shows how analytical findings translate into organizational action, including how to frame results for different audiences and measure the impact of data-driven decisions.

Frequently asked questions

Do I need a statistics or programming background to read this book?

No. The book is written for readers with no prior statistics or coding experience. Mathematical concepts are introduced gradually and explained in plain language.

Is this a textbook or a self-study guide?

It functions as both. It is widely adopted in undergraduate and graduate courses, but the writing is clear enough that self-learners can work through it independently without an instructor.

Does the book cover specific tools like Python, R, or Excel?

The book focuses primarily on concepts and techniques rather than a specific software tool. It provides enough context to understand what different tools do and when to use them, but hands-on coding tutorials are not its main focus.

Is a 2017 publication date a problem given how fast the field moves?

The foundational concepts covered — statistics, data preparation, visualization principles, and model evaluation — are stable and remain accurate. Specific platform or product details may have evolved, but the conceptual core holds up.

Who is this book not a good fit for?

Readers who already have practical analytics experience and want advanced modeling techniques or production engineering guidance will find the content too introductory. It is aimed squarely at beginners.