Chapter 1: Introduction

Data Science and Programming Foundations

Sections: Data Science · On Programming

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Chapter Roadmap

Part Main Question Core Vocabulary
Data Science How do organizations turn data into decisions? data, domain expertise, workflow, analytics, AI
Programming How do people give precise instructions to computers? formal language, syntax, interpreter, compiler, variable
Representation How do computers store numbers and text? binary, byte, ASCII, UTF-8

This chapter gives students the vocabulary needed before they start writing Python programs in Chapter 2.

Section 1

Data Science

Why Data Science?

Data science matters because organizations increasingly make decisions through data-backed processes.

Reason What It Means for Management Students
Job market demand Data roles appear across technology, health care, finance, retail, and government.
Transferable skills Statistical reasoning, coding, and problem solving move across industries.
Better decisions Data analysis reduces guesswork and supports evidence-based management.
Foundation for AI Machine learning and generative AI build on data science workflows.

What Is Data Science?

Data science combines multiple areas of expertise:

  • Data: recorded facts, measurements, or observations.
  • Computing: tools for storage, transformation, and analysis.
  • Statistics: reasoning under uncertainty.
  • Domain expertise: knowledge of the business context.
Data Science Venn Diagram

Data Science Is Interdisciplinary

Data Science Fields

Data science overlaps with business analytics, data analytics, machine learning, artificial intelligence, and business intelligence.

Reading the Field Map

Layer Examples Main Focus
Business layer Business analytics, business intelligence Decisions, reports, dashboards, and organizational questions.
Data layer Data analytics, data science Data preparation, exploration, modeling, and interpretation.
AI layer Machine learning, deep learning, generative AI Prediction, automation, and intelligent systems.

For this course, the management question comes first. The tool is chosen after the question is clear.

Evolution of Data Fields

Era Key Technologies and Developments Analytics Type
1970s Relational databases, SQL, Decision Support Systems Descriptive and decision support
1980s OLTP systems, data modeling, Executive Information Systems Descriptive reporting
1990s Data warehousing, OLAP, business intelligence, data mining Descriptive and diagnostic
2000s Dashboards, KPIs, and business analytics Descriptive, diagnostic, early predictive
2010s Data science, machine learning, end-to-end workflows Predictive and prescriptive
2020s Responsible AI and generative AI Predictive and prescriptive

Analytics Phases

Question Type Example Management Question Common Output
Descriptive What happened last quarter? Dashboard, KPI report, summary table
Diagnostic Why did sales drop in one region? Drill-down analysis, comparison
Predictive Which customers may churn next month? Forecast, probability, model score
Prescriptive What action should the team take? Recommendation, decision rule

CRISP-DM Process

The Cross-Industry Standard Process for Data Mining organizes projects into six phases:

  1. Business understanding
  2. Data understanding
  3. Data preparation
  4. Modeling
  5. Evaluation
  6. Deployment
CRISP-DM Process Model

CRISP-DM Tasks

Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment
Determine objectives Collect initial data Select data Select techniques Evaluate results Plan deployment
Assess situation Describe data Clean data Generate test design Review process Plan monitoring
Define data mining goals Explore data Construct data Build model Decide next steps Produce report
Produce project plan Verify quality Integrate and format data Assess model Review project

General Data Science Lifecycle

General data science lifecycle model

Many organizations customize CRISP-DM, but most workflows still move from business questions to data, modeling, evaluation, and deployment.

Careers Across the Workflow

Data science workflow and related job titles

Common Data Roles

Role Common Work Course Connection
Data analyst Reports, summaries, dashboards, exploratory analysis Tables, charts, basic statistics
Data engineer Data collection, pipelines, storage, and quality Databases and data management
Machine learning engineer Model training, deployment, and monitoring Predictive modeling and evaluation
Data scientist End-to-end analysis, modeling, interpretation, and communication Full data science workflow

Data Science Tools

Data Science Tools

Tool Categories

Category Typical Tools Main Purpose
Data management SQL databases, MongoDB, Redis, Neo4j Store, collect, and organize data.
Data manipulation Python, R, pandas, NumPy Clean, reshape, and transform data.
Data analysis scikit-learn, PyTorch, TensorFlow Fit models and analyze patterns.
Visualization Matplotlib, Seaborn, dashboards Communicate results clearly.
Environment Jupyter Notebook and related tools Combine code, notes, output, and explanation.

Section 2

On Programming

Why Programming Belongs Here

Programming is the technical language used to express data work precisely.

  • A program gives the computer instructions.
  • A programming language has formal rules.
  • A small syntax mistake can change meaning or stop execution.
  • Python is the course language, but the core ideas apply broadly.

Students are not just learning Python syntax. They are learning how computational instructions are structured.

Natural vs. Formal Languages

Feature Natural Language Formal Language
Main use Human communication Precise computation or notation
Ambiguity Often ambiguous and context-dependent Designed to be unambiguous
Redundancy Often verbose to reduce misunderstanding Usually concise
Literalness Allows idioms and metaphors Means exactly what it says
Examples English, Spanish, French Python, SQL, mathematical notation

Levels of Abstraction

Level Description Examples Code Example
High-level language Close to human-readable language Python, Java, JavaScript, C, C++ print("Hello, World!")
Low-level language Close to machine operations Assembly language mov eax, 1
Machine code Binary instructions executed by the CPU 0s and 1s 10110000 00000000

Python hides many hardware details so beginners can focus on computational thinking.

Execution Models

Model Basic Idea Examples
Interpreted Run code through an interpreter. Python, JavaScript
Compiled Translate source code before execution. C, C++, Rust
Hybrid Compile to bytecode, then run on a virtual machine. Python, Java, C#

Execution Diagrams

Interpreter Execution
┌───────────┐   ┌───────────┐   ┌──────┐
│Source Code│ → │Interpreter│ → │Output│
└───────────┘   └───────────┘   └──────┘

Compiler Execution
┌───────────┐   ┌────────┐   ┌───────────┐   ┌──────┐
│Source Code│ → │Compiler│ → │Object Code│ → │Output│
└───────────┘   └────────┘   └───────────┘   └──────┘

Python (CPython) Execution
┌─────────┐   ┌────────┐   ┌──────────┐   ┌─────┐   ┌──────┐
│script.py│ → │Compiler│ → │Bytecode  │ → │ PVM │ → │Output│
└─────────┘   └────────┘   │  (.pyc)  │   └─────┘   └──────┘
                           └──────────┘

Python is often described as interpreted, but modern Python also compiles code to bytecode before the Python virtual machine runs it.

Programming Constructs

Construct Meaning
Sequence Run instructions one after another.
Selection Choose between alternative paths.
Iteration Repeat instructions.
Subroutine Group instructions into a function or method.
Variable Store a value using a name.
Data type Define the kind of value and the operations it supports.
Operator Perform an operation on one or more values.

Expressions and Statements

An expression produces a value.

A statement performs an action.

Type Example Result
Expression 2 + 3 Produces 5
Expression x * y Produces a value
Statement x = 5 Changes program state
Statement print(x) Displays output
Expression, Operand, and Operator

Number Systems

Computers store and process data in binary, but programmers use several number systems.

System Base Digits Used Typical Use Python Example
Binary 2 0-1 Hardware, memory, bitwise operations 0b1100100
Octal 8 0-7 Unix file permissions 0o144
Decimal 10 0-9 Human-friendly math 100
Hexadecimal 16 0-9, A-F Memory, colors, debugging 0x64

Positional Notation

Each digit's value depends on its position and the base.

Digit Position Calculation Value
3 2 3 x 10^2 300
4 1 4 x 10^1 40
5 0 5 x 10^0 5
Total 345 
digit value = digit x (base ^ position)

Decimal to Binary Example

The decimal number 100 is equal to binary 1100100.

0b1100100
  ││││││└ 0 x 2^0 = 0
  │││││└─ 0 x 2^1 = 0
  ││││└── 1 x 2^2 = 4
  │││└─── 0 x 2^3 = 0
  ││└──── 0 x 2^4 = 0
  │└───── 1 x 2^5 = 32
  └────── 1 x 2^6 = 64
                           100

Character Encoding

Computers need a mapping between characters and numbers.

Term Meaning
Bit The smallest unit of data, either 0 or 1.
Byte A group of 8 bits.
ASCII An early character encoding for English letters, digits, and symbols.
Unicode A standard for representing text across writing systems.
UTF-8 A dominant Unicode encoding used across the web and modern systems.

ASCII Example

ASCII Code Chart 1972

The letter A is represented as decimal 65, which is binary 0b1000001.

Chapter 1 Vocabulary: Data Work

# Term Short Meaning
1 Data science Uses data, computing, statistics, and domain knowledge for decisions.
2 Data Recorded facts, measurements, observations, or values.
3 Domain expertise Knowledge of the business or management context.
4 Business analytics Data analysis used to support business decisions.
5 Business intelligence Reports and dashboards for monitoring performance.
6 Data analytics Cleaning, inspecting, transforming, and interpreting data.
7 Machine learning Learning patterns from data for prediction or automation.
8 Artificial intelligence Systems that perform tasks associated with human intelligence.
9 CRISP-DM A process model for data science and data mining projects.
10 Data preparation Cleaning and shaping data before analysis or modeling.
11 Modeling Building a representation of patterns in data.
12 Deployment Putting results, reports, or models into practical use.

Chapter 1 Vocabulary: Roles and Code

# Term Short Meaning
13 Data engineer Builds and maintains data pipelines and infrastructure.
14 Data analyst Explores data, reports patterns, and communicates findings.
15 Machine learning engineer Builds and deploys machine learning systems.
16 Data scientist Works across problem framing, analysis, modeling, and communication.
17 Programming Writing precise instructions that a computer can execute.
18 Algorithm A step-by-step procedure for solving a problem.
19 Natural language Everyday human language such as English or Spanish.
20 Formal language A language with precise rules for a specific purpose.
21 Syntax Formal rules for writing statements.
22 Interpreter Runs source code by translating and executing it.
23 Compiler Translates source code into another form before execution.
24 Bytecode Intermediate code that can run on a virtual machine.

Chapter 1 Vocabulary: Program Building Blocks

# Term Short Meaning
25 Sequence Instructions run one after another.
26 Selection A program chooses between alternative paths.
27 Iteration Repetition, usually controlled by a loop.
28 Variable A named storage location for a value.
29 Data type A value classification that shapes allowed operations.
30 Operator A symbol that performs an operation.
31 Expression Code that evaluates to a value.
32 Statement Code that performs an action or controls flow.
33 Binary A base-2 number system using 0 and 1.
34 Hexadecimal A base-16 number system used in computing contexts.
35 Bit The smallest unit of data, either 0 or 1.
36 Byte A group of 8 bits.
37 Character encoding A mapping between text characters and numeric values.
38 ASCII An early English-focused character encoding.
39 Unicode A standard for representing text across writing systems.
40 UTF-8 A dominant Unicode encoding used on the web.

Practice Path

Practice Type Purpose Chapter 1 Examples
Section exercises Practice one concept immediately after it is introduced. Formal languages, abstraction levels, execution models, constructs, expressions, number systems, encoding
Lab questions Extend and combine section exercises. Data workflow variables, tool categories, base conversion, arithmetic expressions, character encoding
Homework Reinforce the chapter after class. Review, application, and reflection

Lab questions should build from section exercises, so students practice first and then combine ideas in a longer assignment.

What To Carry Forward

After this chapter, you should be able to:

  • Frame data science as a decision workflow.
  • Connect roles and tools to stages of that workflow.
  • Explain why programming requires precision.
  • Distinguish expressions from statements.
  • Convert simple decimal numbers to binary.
  • Explain why text needs character encoding.

End of Chapter 1

Next: Chapter 2, Python Basics

Carry Forward

  • Use the section practice exercises to check runnable code skills.