Introduction to Data Science and Artificial Intelligence for Engineers: Concepts and Applications
Description
Introduction to Data Science and Artificial Intelligence for Engineers: Concepts and Applications gives first-year engineering students a structured, application-oriented introduction to data, artificial intelligence, and responsible engineering practice. It’s written for a single foundation course and follows one clear arc, from what data is through to what a responsible engineer does with it.
The early chapters cover data, its sources, the data science lifecycle, cleaning, privacy, and ethical visualization. From there the book moves through the core technologies: machine learning, deep learning, natural language processing, and computer vision. Later chapters open up generative AI, agentic AI systems, and the tools students will meet in industry.
A distinguishing feature is its emphasis on engineering judgment. AI systems are powerful, but they aren’t infallible. Chapters on hallucinations and deepfakes make the case for verification, and the discussion of agentic AI shows how systems increasingly act on their own. The book culminates in a framework for AI safety built on six principles: safety, fairness, transparency, privacy protection, accountability, and accessibility.
Designed for outcome-based education, the text integrates learning objectives, mini-projects, and application-based exercises that support active learning. Real-world examples across mechanical, civil, electrical, electronics, chemical, biomedical, automobile, and computer science engineering make sure every student sees their own discipline in the material. The result equips students to use AI tools well and to make informed, responsible decisions in their engineering careers.
Table of Contents
Preface v
About the Authors vii
Chapter 1 Introduction to Data Science 1
1.1 Why Data Science? 2
1.2 What Is Data Science? 3
1.3 What Is Data? 4
1.4 Sources of Data 5
1.4.1 Human-Generated Data 5
1.4.2 Machine-Generated Data 6
1.4.3 Organizational Data 7
1.5 Who Creates Data? 7
1.6 Example Datasets 7
1.6.1 Datasets in Computer Science Engineering 8
1.6.2 Datasets in Electronics Engineering 8
1.6.3 Datasets in Biotechnology Engineering 8
1.6.4 Datasets in Civil Engineering 9
1.6.5 Datasets in Mechanical Engineering 9
1.6.6 Datasets in Chemical Engineering 10
1.7 What Is Big Data? 11
1.8 The Five V’s of Big Data 11
1.8.1 Volume 11
1.8.2 Velocity 13
1.8.3 Variety 14
1.8.4 Veracity 15
1.8.5 Value 16
1.9 The Data Science Lifecycle 17
1.9.1 Data Collection 18
1.9.2 Data Cleaning 19
1.9.3 Data Analysis 19
1.9.4 Model Building 20
1.9.5 Visualization and Interpretation 20
Summary 21
Key Terms 21
Multiple Choice Questions (MCQ) 22
Review Questions 23
Application-Based Problems 24
Mini-Project 24
Answer Key 25
Chapter 2 Introduction to Artificial Intelligence 27
2.1 What Is Artificial Intelligence? 30
2.2 Everyday Examples of AI 31
2.3 The Need for Intelligent Systems 32
2.4 The Evolution of AI 34
2.4.1 The Dream of Thinking Machines (1940s–50s) 34
2.4.2 Early AI Programs (1960s–70s) 35
2.4.3 The First AI Winter (1970s–80s) 35
2.4.4 Expert Systems (1980s) 35
2.4.5 The Statistical Turn (1990s–2000s) 36
2.4.6 The Deep-Learning Revolution (2010s) 36
2.4.7 The Era of Generative AI (2020s) 36
2.5 Data Science and Artificial Intelligence 37
Summary 39
Key Terms 40
Multiple Choice Questions (MCQ) 40
Review Questions 41
Application-Based Problems 42
Mini-Project 43
Answer Key 43
Chapter 3 Data Collection 45
3.1 Historical and Real-Time Datasets 46
3.1.1 Historical Datasets 46
3.1.2 Real-Time Datasets 46
3.1.3 Comparing Historical and Real-Time Data 48
3.1.4 Sources of Data Across the Engineering Disciplines 48
3.2 Open-Source and Repository Data 49
3.2.1 Kaggle: The Social Network for Data 50
3.2.2 UCI Machine Learning Repository 50
3.2.3 GitHub: The Developer’s Warehouse 50
3.3 Ethical Data Practices 51
3.3.1 Respect the License 51
3.3.2 Proper Attribution and Citation 52
3.3.3 Privacy and Consent 52
3.4 Synthetic Data (Artificial Data) 53
3.4.1 Why Synthetic Data Are Used 54
3.4.2 Limitations to Bear in Mind 54
3.4.3 Engineering Examples 54
Summary 55
Key Terms 56
Multiple Choice Questions (MCQ) 56
Review Questions 57
Application-Based Problems 58
Mini-Project 59
Answer Key 59
Chapter 4 Data Cleaning 61
4.1 What Is Data Cleaning? 62
4.1.1 Handling Missing Values 63
4.1.2 Removing Duplicates 64
4.1.3 Fixing Structural Errors 65
4.1.4 Handling Outliers 66
4.2 Data Validation 67
Summary 69
Key Terms 69
Multiple Choice Questions (MCQ) 70
Review Questions 71
Application-Based Problems 72
Mini-Project 72
Answer Key 73
Chapter 5 Data Privacy and Safe Digital Practices 75
5.1 The Digital World and Personal Data 76
5.2 What Is Data Privacy? 76
5.3 Digital Privacy Risks 77
5.3.1 Phishing 78
5.3.2 Malware 79
5.3.3 Identity Theft 80
5.4 Safe Digital Practices 81
5.4.1 Safe Social-Media Use 82
5.5 Digital Ethics and Responsibility 83
5.6 Why Follow Safe Digital Practices? 85
Summary 86
Key Terms 86
Multiple Choice Questions (MCQ) 87
Review Questions 88
Application-Based Problems 88
Mini-Project 89
Answer Key 90
Chapter 6 Data Visualization with Ethical Design 91
6.1 What Is Data Visualization? 92
6.2 Why Data Visualization Is Important 93
6.3 What Is Ethical Design in Data Visualization? 94
6.3.1 Common Ethical Issues 94
6.3.2 Principles of Ethical Data Visualization 98
6.3.3 An Ethical Visualization Workflow 98
6.3.4 Bad Visualization versus Good Visualization 99
6.4 Worked Examples 99
6.4.1 Civil Engineering 99
6.4.2 Mechanical Engineering 100
6.4.3 Electronics Engineering 100
6.4.4 Computer Science 100
6.4.5 Biomedical Engineering 101
6.4.6 Automobile Engineering 101
Summary 101
Key Terms 102
Multiple Choice Questions (MCQ) 103
Review Questions 104
Application-Based Problems 104
Mini-Project 105
Answer Key 106
Chapter 7 Introduction to Machine Learning 107
7.1 What Is Machine Learning? 108
7.2 Types of Machine Learning 109
7.2.1 Supervised Learning 109
7.2.2 Unsupervised Learning 113
7.2.3 Reinforcement Learning 115
7.2.4 Semi-Supervised Learning 116
7.3 The Machine Learning Workflow 117
7.4 Engineering Applications of Machine Learning 118
Summary 119
Key Terms 119
Multiple Choice Questions (MCQ) 120
Review Questions 121
Application-Based Problems 121
Mini-Project 122
Answer Key 123
Chapter 8 Deep Learning 125
8.1 What Is Deep Learning? 126
8.1.1 How a Deep Network Works 128
8.2 Why Deep Learning Is Important 128
8.3 Relationship Between AI, Machine Learning, and Deep Learning 129
8.4 Real-Time Applications of Deep Learning 132
8.4.1 Object Detection and Image Recognition 132
8.4.2 Speech Recognition 133
8.4.3 Autonomous Vehicles 134
8.4.4 Healthcare and Medical Diagnosis 134
8.4.5 Applications Across Engineering Disciplines 135
8.5 Challenges of Deep Learning 137
8.6 The Future of Deep Learning 139
Summary 140
Key Terms 140
Multiple Choice Questions (MCQ) 141
Review Questions 142
Application-Based Problems 143
Mini-Project 143
Answer Key 145
Chapter 9 Introduction to Natural Language Processing 147
9.1 Everyday NLP 148
9.1.1 Voice Assistants 148
9.1.2 Chatbots and Virtual Support 149
9.1.3 E-mail Filtering 150
9.1.4 Language Translation 151
9.1.5 Sentiment Analysis 151
9.1.6 Text Summarization 152
9.1.7 Clinical Text Analysis 153
9.2 NLP Risks and Responsibilities 154
9.3 Major Risks in NLP 154
9.3.1 Bias and Unfairness 154
9.3.2 Misinterpretation of Language 155
9.3.3 Privacy Concerns 155
9.3.4 Misinformation and Fake Content 155
9.3.5 Over-Reliance on Automation 156
9.3.6 Language and Cultural Limitations 156
9.4 Responsibilities in NLP 157
Summary 158
Key Terms 159
Multiple Choice Questions (MCQ) 159
Review Questions 161
Application-Based Problems 161
Mini-Project 162
Answer Key 163
Chapter 10 Computer Vision 165
10.1 Basic Working Flow 166
10.2 Core Tasks in Computer Vision 167
10.2.1 Image Classification 168
10.2.2 Object Detection 169
10.2.3 Image Segmentation 169
10.2.4 Face Recognition 169
10.2.5 Civil Engineering: Structural Inspection 170
10.2.6 Mechanical Engineering: Quality Control on the Assembly Line 171
10.2.7 Electrical Engineering: Power-Line and Equipment Monitoring 171
10.2.8 Computer Science Engineering: Authentication and Access Control 172
10.2.9 Automobile Engineering: Perception in Self-Driving Cars 172
10.2.10 Biomedical Engineering: Analysis of Medical Images 174
10.2.11 Electronics and Communication Engineering: Satellite-Image Analysis 174
10.3 Major Risks in Computer Vision 176
10.4 Ethical Concerns in Computer Vision 179
Summary 181
Key Terms 182
Multiple Choice Questions (MCQ) 182
Review Questions 183
Application-Based Problems 184
Mini-Project 185
Answer Key 186
Chapter 11 Introduction to Generative AI 187
11.1 What Is Generative AI? 188
11.2 How Generative AI Works 189
11.3 Generative AI Models 190
11.4 Applications of Generative AI 191
11.4.1 Text Generation 191
11.4.2 Image Generation 192
11.4.3 Code Generation 192
11.4.4 Video and Media Generation 193
11.5 Engineering Applications of Generative AI 194
11.6 Challenges of Generative AI 195
11.7 The Future of Generative AI 197
Summary 198
Key Terms 198
Multiple Choice Questions (MCQ) 199
Review Questions 200
Application-Based Problems 200
Mini-Project 200
Answer Key 202
Chapter 12 Introduction to Agentic AI 203
12.1 What Is Agentic AI? 204
12.2 How Agentic AI Works 206
12.2.1 Perception 207
12.2.2 Reasoning 207
12.2.3 Planning 207
12.2.4 Action 207
12.2.5 Learning 207
12.3 Real-World Applications of Agentic AI 208
12.4 Intelligent Virtual Assistants 209
12.5 Engineering Applications of Agentic AI 211
12.6 Benefits of Agentic AI 213
12.7 Challenges of Agentic AI 214
12.8 Pathways to Safe and Responsible Agentic AI 215
12.8.1 Value Alignment 215
12.8.2 Mechanistic Interpretability 216
12.8.3 Robustness and Verification 216
12.8.4 Safeguarding and Mitigation 216
12.8.5 Ethics and Governance Frameworks 216
Summary 217
Key Terms 218
Multiple Choice Questions (MCQ) 218
Review Questions 219
Application-Based Problems 220
Mini-Project 221
Answer Key 222
Chapter 13 AI Tools 223
13.1 Grok 224
13.2 Getmulti 225
13.3 Wispr Flow 225
13.4 NotebookLM 226
13.5 Gemini 227
13.6 Kimi 228
13.7 Julius.ai 228
13.8 ChatGPT 229
13.9 Comet 230
13.10 n8n 230
13.11 Claude 231
13.12 Putting the Eleven Tools Together 232
Summary 233
Key Terms 233
Multiple Choice Questions (MCQ) 235
Review Questions 237
Application-Based Problems 237
Mini-Project 238
Answer Key 239
Chapter 14 AI Risks: Hallucinations and Deepfakes 241
14.1 AI Hallucinations 242
14.1.1 What an AI Hallucination Is 242
14.1.2 Why Hallucinations Happen 243
14.1.3 Two Kinds of Hallucination 244
14.1.4 Real Cases of AI Hallucination 244
14.2 Deepfakes 246
14.2.1 What a Deepfake Is 246
14.2.2 Useful Applications 246
14.3 Risks of Deepfakes 247
14.4 Reducing the Risks 247
14.5 A Message to the Engineering Student: Ethical Use and Legal Exposure 248
Summary 252
Key Terms 253
Multiple Choice Questions (MCQ) 255
Review Questions 256
Application-Based Problems 257
Mini-Project 258
Answer Key 259
Chapter 15 AI Safety for All 261
15.1 Safety 263
15.2 Fairness 264
15.3 Transparency 265
15.4 Privacy Protection 266
15.5 Accountability 267
15.6 Accessibility 268
15.7 Why AI Safety Is Important 269
15.7.1 It Prevents Direct Harm 269
15.7.2 It Sustains the Conditions for AI to Be Useful 270
15.7.3 It Defines What Kind of Profession Engineering Wants to Be 270
15.7.4 The Role of the Student 270
Summary 271
Key Terms 272
Multiple Choice Questions (MCQ) 274
Review Questions 275
Application-Based Problems 276
Mini-Project (Capstone Project) 276
Answer Key 278