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.

• Preface ixIntroduction xvChapter 1 Artificial Intelligence (AI) and Automated Data Analytics 11.1 The emergence of automated data analytics and the potential of generative AI 21.1.1 The power unleashed by generative AI 21.1.2 Transforming the data analytics process 21.1.3 Redefining coding with the intelligent agent 31.1.4 Human–AI collaboration 51.1.5 The power of prompt engineering 51.1.6 An ethical North Star 61.2 Revolutionizing the data analytics process with ChatGPT 71.2.1 The numbers behind ChatGPT’s potential 71.2.2 ChatGPT and the future of data analytics 91.3 Harmony between human creativity and automated analysis: the winning duo 111.3.1 The value of human creativity 111.3.2 The complementary power of automated analysis 121.3.3 Navigating the partnership responsibly 131.4 Unlocking the secrets of prompt engineering for powerful results 151.4.1 The art of prompting 151.4.2 Optimizing prompts for efficient interaction with ChatGPT: fundamental aspects 17Chapter 2 ChatGPT for Data Analytics 212.1 Exploring the ChatGPT universe: history, presentation and capabilities 212.1.1 From GPT-1 to GPT-4: generative pre-trained transformers 212.1.2 ChatGPT in practice 242.2 Powerful features for intelligent data analytics: natural language at your service 272.3 ChatGPT versus data scientists: an intelligence battle that looks like an alliance 302.3.1 Seamless communication and key features of an effective partnership 302.3.2 ChatGPT, between ally and threat 332.3.3 Automating data science workflows: unleashing the potential of ChatGPT plugins 352.4 Benefits and challenges of integrating ChatGPT into data analytics workflows 372.4.1 Unlocking analytical potential and reducing costs 37Chapter 3 Data Preparation for Analysis with ChatGPT 413.1 ChatGPT in charge of preparing our datasets 413.1.1 Data cleaning 423.1.2 Data transformation 433.1.3 Data formatting 433.2 Automated cleaning and pre-processing for optimum results with ChatGPT 453.3 Handling missing data, outliers and other common data issues 483.4 Using ChatGPT for data transformation, feature engineering and beyond 51Chapter 4 Intuitive Query Creation with ChatGPT 574.1 The discovery of patterns, trends and insights through interactive conversations 574.1.1 Key benefits 594.1.2 Discovering insights organically 604.1.3 Democratizing data analytics 604.1.4 The future of business intelligence 614.2 Creating natural language queries to analyze your data 624.3 The art of transforming analysis questions into SQL queries with ChatGPT 654.4 Generating efficient and optimized queries: the key to your success with ChatGPT 69Chapter 5 ChatGPT: The Advanced Analysis Wizard 735.1 Exploring new horizons: ChatGPT for exploratory data analysis 735.2 Simplifying your analysis: automation tasks for increased efficiency 805.3 From statistics to predictions: ChatGPT as the partner of choice 825.3.1 Establishing statistical foundations 845.3.2 Building models with ChatGPT 845.3.3 Model deployment and monitoring models 855.3.4 Business impact 865.4 Deciphering feelings: text and sentiment analysis with ChatGPT 87Chapter 6 Prediction and Modeling with ChatGPT 936.1 Automating the data analysis process with ChatGPT 946.2 ChatGPT for accurate and reboust modeling 966.3 Continuous improvement: optimizing model capabilities through feedback loops 1006.4 Trend and time series analysis 105Chapter 7 ChatGPT at the Service of Machine Learning 1097.1 Machine learning in the functional fabric of ChatGPT 1107.2 Creating new machine learning approaches with ChatGPT 1137.3 Boosting machine learning algorithms with ChatGPT 1167.3.1 Optimizing machine learning engineering 1177.3.2 Hybrid model innovation 1177.4 Enhancing the potential of machine learning algorithms with ChatGPT 120Chapter 8 Narrative Fascination: Data-driven Stories and Reports 1278.1 ChatGPT for generating data storytelling plans 1278.2 The bewitchment of words: automating for writing data-driven stories 1318.3 Interactive dashboards and ChatGPT’s ingenuity 1348.4 Humans at the heart of protocols: the imprint of human ingenuity in generative AI 137Chapter 9 Power within Hands: Ethics, Orientation and Use 1439.1 Understanding the limits of AI-generated analysis 1449.2 Ethical harmony: ChatGPT in data analytics workflows 1479.3 Providing iterative feedback to improve ChatGPT 1519.4 Addressing ethical concerns and biases when using ChatGPT in data analytics 1559.5 Ensuring fairness, transparency and accountability in automated data analytics 157Conclusion 161Appendix 1 167Appendix 2 183Appendix 3 185References 191Index 197

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.

• Preface ixIntroduction xvChapter 1 Artificial Intelligence (AI) and Automated Data Analytics 11.1 The emergence of automated data analytics and the potential of generative AI 21.1.1 The power unleashed by generative AI 21.1.2 Transforming the data analytics process 21.1.3 Redefining coding with the intelligent agent 31.1.4 Human–AI collaboration 51.1.5 The power of prompt engineering 51.1.6 An ethical North Star 61.2 Revolutionizing the data analytics process with ChatGPT 71.2.1 The numbers behind ChatGPT’s potential 71.2.2 ChatGPT and the future of data analytics 91.3 Harmony between human creativity and automated analysis: the winning duo 111.3.1 The value of human creativity 111.3.2 The complementary power of automated analysis 121.3.3 Navigating the partnership responsibly 131.4 Unlocking the secrets of prompt engineering for powerful results 151.4.1 The art of prompting 151.4.2 Optimizing prompts for efficient interaction with ChatGPT: fundamental aspects 17Chapter 2 ChatGPT for Data Analytics 212.1 Exploring the ChatGPT universe: history, presentation and capabilities 212.1.1 From GPT-1 to GPT-4: generative pre-trained transformers 212.1.2 ChatGPT in practice 242.2 Powerful features for intelligent data analytics: natural language at your service 272.3 ChatGPT versus data scientists: an intelligence battle that looks like an alliance 302.3.1 Seamless communication and key features of an effective partnership 302.3.2 ChatGPT, between ally and threat 332.3.3 Automating data science workflows: unleashing the potential of ChatGPT plugins 352.4 Benefits and challenges of integrating ChatGPT into data analytics workflows 372.4.1 Unlocking analytical potential and reducing costs 37Chapter 3 Data Preparation for Analysis with ChatGPT 413.1 ChatGPT in charge of preparing our datasets 413.1.1 Data cleaning 423.1.2 Data transformation 433.1.3 Data formatting 433.2 Automated cleaning and pre-processing for optimum results with ChatGPT 453.3 Handling missing data, outliers and other common data issues 483.4 Using ChatGPT for data transformation, feature engineering and beyond 51Chapter 4 Intuitive Query Creation with ChatGPT 574.1 The discovery of patterns, trends and insights through interactive conversations 574.1.1 Key benefits 594.1.2 Discovering insights organically 604.1.3 Democratizing data analytics 604.1.4 The future of business intelligence 614.2 Creating natural language queries to analyze your data 624.3 The art of transforming analysis questions into SQL queries with ChatGPT 654.4 Generating efficient and optimized queries: the key to your success with ChatGPT 69Chapter 5 ChatGPT: The Advanced Analysis Wizard 735.1 Exploring new horizons: ChatGPT for exploratory data analysis 735.2 Simplifying your analysis: automation tasks for increased efficiency 805.3 From statistics to predictions: ChatGPT as the partner of choice 825.3.1 Establishing statistical foundations 845.3.2 Building models with ChatGPT 845.3.3 Model deployment and monitoring models 855.3.4 Business impact 865.4 Deciphering feelings: text and sentiment analysis with ChatGPT 87Chapter 6 Prediction and Modeling with ChatGPT 936.1 Automating the data analysis process with ChatGPT 946.2 ChatGPT for accurate and reboust modeling 966.3 Continuous improvement: optimizing model capabilities through feedback loops 1006.4 Trend and time series analysis 105Chapter 7 ChatGPT at the Service of Machine Learning 1097.1 Machine learning in the functional fabric of ChatGPT 1107.2 Creating new machine learning approaches with ChatGPT 1137.3 Boosting machine learning algorithms with ChatGPT 1167.3.1 Optimizing machine learning engineering 1177.3.2 Hybrid model innovation 1177.4 Enhancing the potential of machine learning algorithms with ChatGPT 120Chapter 8 Narrative Fascination: Data-driven Stories and Reports 1278.1 ChatGPT for generating data storytelling plans 1278.2 The bewitchment of words: automating for writing data-driven stories 1318.3 Interactive dashboards and ChatGPT’s ingenuity 1348.4 Humans at the heart of protocols: the imprint of human ingenuity in generative AI 137Chapter 9 Power within Hands: Ethics, Orientation and Use 1439.1 Understanding the limits of AI-generated analysis 1449.2 Ethical harmony: ChatGPT in data analytics workflows 1479.3 Providing iterative feedback to improve ChatGPT 1519.4 Addressing ethical concerns and biases when using ChatGPT in data analytics 1559.5 Ensuring fairness, transparency and accountability in automated data analytics 157Conclusion 161Appendix 1 167Appendix 2 183Appendix 3 185References 191Index 197

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.

• Preface ixIntroduction xvChapter 1 Artificial Intelligence (AI) and Automated Data Analytics 11.1 The emergence of automated data analytics and the potential of generative AI 21.1.1 The power unleashed by generative AI 21.1.2 Transforming the data analytics process 21.1.3 Redefining coding with the intelligent agent 31.1.4 Human–AI collaboration 51.1.5 The power of prompt engineering 51.1.6 An ethical North Star 61.2 Revolutionizing the data analytics process with ChatGPT 71.2.1 The numbers behind ChatGPT’s potential 71.2.2 ChatGPT and the future of data analytics 91.3 Harmony between human creativity and automated analysis: the winning duo 111.3.1 The value of human creativity 111.3.2 The complementary power of automated analysis 121.3.3 Navigating the partnership responsibly 131.4 Unlocking the secrets of prompt engineering for powerful results 151.4.1 The art of prompting 151.4.2 Optimizing prompts for efficient interaction with ChatGPT: fundamental aspects 17Chapter 2 ChatGPT for Data Analytics 212.1 Exploring the ChatGPT universe: history, presentation and capabilities 212.1.1 From GPT-1 to GPT-4: generative pre-trained transformers 212.1.2 ChatGPT in practice 242.2 Powerful features for intelligent data analytics: natural language at your service 272.3 ChatGPT versus data scientists: an intelligence battle that looks like an alliance 302.3.1 Seamless communication and key features of an effective partnership 302.3.2 ChatGPT, between ally and threat 332.3.3 Automating data science workflows: unleashing the potential of ChatGPT plugins 352.4 Benefits and challenges of integrating ChatGPT into data analytics workflows 372.4.1 Unlocking analytical potential and reducing costs 37Chapter 3 Data Preparation for Analysis with ChatGPT 413.1 ChatGPT in charge of preparing our datasets 413.1.1 Data cleaning 423.1.2 Data transformation 433.1.3 Data formatting 433.2 Automated cleaning and pre-processing for optimum results with ChatGPT 453.3 Handling missing data, outliers and other common data issues 483.4 Using ChatGPT for data transformation, feature engineering and beyond 51Chapter 4 Intuitive Query Creation with ChatGPT 574.1 The discovery of patterns, trends and insights through interactive conversations 574.1.1 Key benefits 594.1.2 Discovering insights organically 604.1.3 Democratizing data analytics 604.1.4 The future of business intelligence 614.2 Creating natural language queries to analyze your data 624.3 The art of transforming analysis questions into SQL queries with ChatGPT 654.4 Generating efficient and optimized queries: the key to your success with ChatGPT 69Chapter 5 ChatGPT: The Advanced Analysis Wizard 735.1 Exploring new horizons: ChatGPT for exploratory data analysis 735.2 Simplifying your analysis: automation tasks for increased efficiency 805.3 From statistics to predictions: ChatGPT as the partner of choice 825.3.1 Establishing statistical foundations 845.3.2 Building models with ChatGPT 845.3.3 Model deployment and monitoring models 855.3.4 Business impact 865.4 Deciphering feelings: text and sentiment analysis with ChatGPT 87Chapter 6 Prediction and Modeling with ChatGPT 936.1 Automating the data analysis process with ChatGPT 946.2 ChatGPT for accurate and reboust modeling 966.3 Continuous improvement: optimizing model capabilities through feedback loops 1006.4 Trend and time series analysis 105Chapter 7 ChatGPT at the Service of Machine Learning 1097.1 Machine learning in the functional fabric of ChatGPT 1107.2 Creating new machine learning approaches with ChatGPT 1137.3 Boosting machine learning algorithms with ChatGPT 1167.3.1 Optimizing machine learning engineering 1177.3.2 Hybrid model innovation 1177.4 Enhancing the potential of machine learning algorithms with ChatGPT 120Chapter 8 Narrative Fascination: Data-driven Stories and Reports 1278.1 ChatGPT for generating data storytelling plans 1278.2 The bewitchment of words: automating for writing data-driven stories 1318.3 Interactive dashboards and ChatGPT’s ingenuity 1348.4 Humans at the heart of protocols: the imprint of human ingenuity in generative AI 137Chapter 9 Power within Hands: Ethics, Orientation and Use 1439.1 Understanding the limits of AI-generated analysis 1449.2 Ethical harmony: ChatGPT in data analytics workflows 1479.3 Providing iterative feedback to improve ChatGPT 1519.4 Addressing ethical concerns and biases when using ChatGPT in data analytics 1559.5 Ensuring fairness, transparency and accountability in automated data analytics 157Conclusion 161Appendix 1 167Appendix 2 183Appendix 3 185References 191Index 197