Introduction to Data Science Using Python

Afrand Agah’s Introduction to Data Science Using Python begins with programming fundamentals and progresses to arrays, graphics, packages, graph theory, machine learning, and statistics. Its fifteen chapters provide a bridge into data-oriented work rather than a complete treatment of every data-science method. The linked source is the first-edition file updated in December 2025.

Chapter-by-chapter summaries

Chapter 1: Installing Python

Installation establishes a reproducible place to run examples. The important distinction is between writing source code and executing it in an interpreter or development environment. Recording the environment helps make later errors interpretable rather than treating every failure as a problem in the algorithm.

Chapter 2: Introduction to Programming

Values, variables, expressions, and basic operations introduce computation as an explicit sequence of instructions. The chapter builds the vocabulary needed to describe what a program stores and changes. A useful reading habit is to predict intermediate values rather than looking only at the final printed result.

Chapter 3: Decision Structures

Conditions allow a program to select among possible actions. Correct behavior depends on both the test and the branch associated with its outcome. The chapter develops the ability to translate a verbal distinction into an explicit logical decision and to test what happens at a boundary.

Chapter 4: Repetitions

Loops apply a procedure repeatedly, often while a variable or collection changes. Understanding initialization, continuation, and termination prevents accidental infinite repetition or omitted cases. The chapter also prepares the reader to see data processing as systematic work over many observations rather than a sequence written separately for each item.

Chapter 5: Functions

Functions organize reusable computation around inputs, a task, and a result. They make a program easier to test because a behavior can be examined independently. The central design question is what one function should be responsible for, not simply how to reduce the number of repeated lines.

Chapter 6: Recursion

A recursive solution reduces a problem to a related smaller problem. The base case and the progress toward it must both be clear. Tracing a small example exposes the difference between the order of calls and the order in which results become available.

Chapter 7: File Access

Files allow information to persist beyond one execution. Reading and writing require attention to representation, ordering, and the distinction between stored text and the values a program uses. This chapter connects programming fundamentals with the practical need to acquire and preserve data.

Chapter 8: Lists

Lists organize multiple values in a collection that can be accessed and processed systematically. Indexing, iteration, and changes to the collection require a consistent model of its contents. The chapter develops data-structure reasoning that later supports tabular and numerical work.

Chapter 9: Arrays

Arrays introduce a representation suited to numerical collections. Their organization and operations should be understood in relation to the quantities being represented. The transition from general lists to array-oriented work helps explain why data-science tools use structures that support consistent shapes and numerical operations.

Chapter 10: Plotting Graphs

A graph makes a numerical relationship visible, but the result depends on the choice of variables, scale, and labels. Plotting is therefore both a programming task and an interpretive decision. The chapter encourages using visualization to examine data and communicate a relationship, not merely decorate an output.

Chapter 11: Object-Oriented Programming

Objects connect data with behavior and provide another way to organize programs. Classes describe a kind of entity, while individual objects hold particular state. The chapter extends program design beyond isolated functions and prepares learners to read the interfaces of packages built around objects.

Chapter 12: Using Python Packages

Packages provide existing implementations behind documented interfaces. Effective use requires understanding expected inputs, returned values, and assumptions. The chapter introduces an essential data-science habit: reuse reliable tools while retaining responsibility for checking that the tool answers the actual question.

Chapter 13: Python and Graph Theory

Graph structures represent relationships through nodes and connections. The chapter brings programming into problems where connectivity matters more than a simple ordered list. Learners should identify what each part of the graph represents before interpreting a computation performed on it.

Chapter 14: Python and Machine Learning

Machine learning uses data to construct a model for a task. The chapter introduces this relationship through Python tools. A model's output should be interpreted in terms of the data and objective used to build it, rather than assuming that executing a library call establishes accuracy or general applicability.

Chapter 15: Python and Statistics

Statistical tools connect numerical computation with descriptions and inferences about data. The programming result needs an explanation of the quantity calculated and its relevance to the question. This final chapter reinforces the distinction between obtaining a number and making a justified interpretation of it.

Study instructions

1. Use a small dataset throughout the later chapters, retaining its source, variable definitions, and cleaning decisions.
2. Trace control flow and function outputs by hand before running short examples.
3. Explain each plot or model result in ordinary language, including what the input data cannot establish.
4. Consult the appendix when a language detail blocks understanding, then return to the larger problem.

Sources and coverage

This original study guide follows the linked source edition and its chapters or explicitly identified sections. It condenses the main ideas and their relationships; examples and practice directions are for study alongside the complete text. Complete source edition.