Soraya Sedkaoui

Automated Data Analytics (eBook, PDF)

Combining Human Creativity and AI Power Using ChatGPT

• Format: PDF

Produktbeschreibung

The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models.

Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons.

This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 243
• Erscheinungstermin: 26. September 2024
• Englisch
• ISBN-13: 9781394325474
• Artikelnr.: 72249787

Autorenporträt

Soraya Sedkaoui is a professor at the University of Khemis Miliana, Algeria. She is also a data analyst and strategic consultant. Her research interests include Big Data and the development of algorithms and models for business applications.

Inhaltsangabe

Preface ix
Introduction xv
Chapter 1 Artificial Intelligence (AI) and Automated Data Analytics 1
1.1 The emergence of automated data analytics and the potential of
generative AI 2
1.1.1 The power unleashed by generative AI 2
1.1.2 Transforming the data analytics process 2
1.1.3 Redefining coding with the intelligent agent 3
1.1.4 Human-AI collaboration 5
1.1.5 The power of prompt engineering 5
1.1.6 An ethical North Star 6
1.2 Revolutionizing the data analytics process with ChatGPT 7
1.2.1 The numbers behind ChatGPT's potential 7
1.2.2 ChatGPT and the future of data analytics 9
1.3 Harmony between human creativity and automated analysis: the winning
duo 11
1.3.1 The value of human creativity 11
1.3.2 The complementary power of automated analysis 12
1.3.3 Navigating the partnership responsibly 13
1.4 Unlocking the secrets of prompt engineering for powerful results 15
1.4.1 The art of prompting 15
1.4.2 Optimizing prompts for efficient interaction with ChatGPT:
fundamental aspects 17
Chapter 2 ChatGPT for Data Analytics 21
2.1 Exploring the ChatGPT universe: history, presentation and capabilities
21
2.1.1 From GPT-1 to GPT-4: generative pre-trained transformers 21
2.1.2 ChatGPT in practice 24
2.2 Powerful features for intelligent data analytics: natural language at
your service 27
2.3 ChatGPT versus data scientists: an intelligence battle that looks like
an alliance 30
2.3.1 Seamless communication and key features of an effective partnership
30
2.3.2 ChatGPT, between ally and threat 33
2.3.3 Automating data science workflows: unleashing the potential of
ChatGPT plugins 35
2.4 Benefits and challenges of integrating ChatGPT into data analytics
workflows 37
2.4.1 Unlocking analytical potential and reducing costs 37
Chapter 3 Data Preparation for Analysis with ChatGPT 41
3.1 ChatGPT in charge of preparing our datasets 41
3.1.1 Data cleaning 42
3.1.2 Data transformation 43
3.1.3 Data formatting 43
3.2 Automated cleaning and pre-processing for optimum results with ChatGPT
45
3.3 Handling missing data, outliers and other common data issues 48
3.4 Using ChatGPT for data transformation, feature engineering and beyond
51
Chapter 4 Intuitive Query Creation with ChatGPT 57
4.1 The discovery of patterns, trends and insights through interactive
conversations 57
4.1.1 Key benefits 59
4.1.2 Discovering insights organically 60
4.1.3 Democratizing data analytics 60
4.1.4 The future of business intelligence 61
4.2 Creating natural language queries to analyze your data 62
4.3 The art of transforming analysis questions into SQL queries with
ChatGPT 65
4.4 Generating efficient and optimized queries: the key to your success
with ChatGPT 69
Chapter 5 ChatGPT: The Advanced Analysis Wizard 73
5.1 Exploring new horizons: ChatGPT for exploratory data analysis 73
5.2 Simplifying your analysis: automation tasks for increased efficiency 80
5.3 From statistics to predictions: ChatGPT as the partner of choice 82
5.3.1 Establishing statistical foundations 84
5.3.2 Building models with ChatGPT 84
5.3.3 Model deployment and monitoring models 85
5.3.4 Business impact 86
5.4 Deciphering feelings: text and sentiment analysis with ChatGPT 87
Chapter 6 Prediction and Modeling with ChatGPT 93
6.1 Automating the data analysis process with ChatGPT 94
6.2 ChatGPT for accurate and reboust modeling 96
6.3 Continuous improvement: optimizing model capabilities through feedback
loops 100
6.4 Trend and time series analysis 105
Chapter 7 ChatGPT at the Service of Machine Learning 109
7.1 Machine learning in the functional fabric of ChatGPT 110
7.2 Creating new machine learning approaches with ChatGPT 113
7.3 Boosting machine learning algorithms with ChatGPT 116
7.3.1 Optimizing machine learning engineering 117
7.3.2 Hybrid model innovation 117
7.4 Enhancing the potential of machine learning algorithms with ChatGPT 120
Chapter 8 Narrative Fascination: Data-driven Stories and Reports 127
8.1 ChatGPT for generating data storytelling plans 127
8.2 The bewitchment of words: automating for writing data-driven stories
131
8.3 Interactive dashboards and ChatGPT's ingenuity 134
8.4 Humans at the heart of protocols: the imprint of human ingenuity in
generative AI 137
Chapter 9 Power within Hands: Ethics, Orientation and Use 143
9.1 Understanding the limits of AI-generated analysis 144
9.2 Ethical harmony: ChatGPT in data analytics workflows 147
9.3 Providing iterative feedback to improve ChatGPT 151
9.4 Addressing ethical concerns and biases when using ChatGPT in data
analytics 155
9.5 Ensuring fairness, transparency and accountability in automated data
analytics 157
Conclusion 161
Appendix 1 167
Appendix 2 183
Appendix 3 185
References 191
Index 197