"Artificial Intelligence (AI) is a discipline, like physics and mathematics, aiming to construct intelligent agents capable of independent reasoning, learning, and decision-making. AI focuses on developing theories and methodologies to create agents that can emulate human-like thinking and behavior." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"Data Storytelling allows you to fill the gap between simply visualizing data and communicating it to an audience. Data storytelling improves your communication skills and standardizes and simplifies the process of communicating results, making it easier for people to understand and remember information. Data storytelling also helps you learn to communicate more effectively with others, improving personal and professional relationships." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"[...] data storytelling builds compelling stories supported by data, allowing analysts and data scientists to present and share their insights interestingly and interactively. The ultimate goal of data storytelling is to engage the audience and inspire them to make decisions. In some cases, including business scenarios, you do not start from data but from a narrative or a hypothesis you have in mind and then search for data that confirm or negate it. In this case, you can still have data storytelling, but you must pay attention not to alter your data to support your hypothesis." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"Data storytelling is about more than just communicating data. It's about inspiring your audience and inviting them to take action. Good data storytelling requires a mix of art and science. Art comes in finding the right story, while science understands how to use data to support that story. When done well, data storytelling can be a potent tool for change." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"Data visualization is primarily concerned with presenting data in a way that is easy to understand, usually through charts and graphs. On the other hand, data storytelling is more concerned with using data to tell a story that will engage the audience and help them understand the problem. In addition, data storytelling often uses narratives to explain the data, while data visualization focuses on presenting the data itself. Anyway, good data storytelling includes visualization elements." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"Deep Learning (DL) is a subfield of ML that focuses on training artificial neural networks to process and understand complex patterns in data. Inspired by the human brain, DL models consist of multiple hidden layers of interconnected nodes, known as artificial neurons or units. The greater the number of layers, the more complex the data structure the network can recognize. DL is quite resource-intensive because it requires high-performance GPUs and large amounts of storage to train models. You can use DL models to solve tasks such as image and speech recognition, natural language processing, and even game-playing." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"Machine Learning (ML) is a subfield of AI that enables computers to acquire knowledge and improve performance without explicit programming. Different from traditional programming, where you must write the code line by line to provide instructions, ML allows machines to learn from data and make predictions or take actions based on that learned information. This approach empowers machines to discover patterns, relationships, and insights that may not be immediately apparent to human programmers. ML relies on two types of learning: supervised learning and unsupervised learning." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"Prompt Engineering is a discipline aiming at carefully crafting and refining the prompts to achieve the desired results. The goal of prompt engineering is to elicit responses that are coherent, relevant, and aligned with the desired outcome while minimizing any unintended biases or undesired behavior." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"RAG is an advanced Natural Language Processing (NLP) technique that combines elements of information retrieval and text generation. First, RAG performs a retrieval step, which queries an external knowledge source, such as a vast text corpus or a structured database. Next, RAG uses this knowledge source to enhance its response generation. RAG integrates the retrieved facts into its generated text." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
"To turn information into knowledge, add context. Context involves all circumstances around data, such as events, situations, etc. Adding context helps the audience to understand data better." (Angelica Lo Duca, "Data Storytelling with Generative AI: Using Python and Altair", 2024)
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