Automated Data Analytics

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 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