NH4361Sp25

     Tableau empowers users to harvest patterns, trends, and relationships in data that are relatively likely to go undetected with a layman's analysis. Data visualization makes data more approachable, leading to better decision-making, more effective communication of findings, and improved operations. The chapter also notes that effective visualizations can lend credibility, enabling professionals to make data-driven cases clearly. Tableau is a very essential tool of UI in the present workplace, as simple practical examples of analyzing the trends of UFO sightings and share market trends have shown that the intuitive interface of Tableau, wherein the user can improve data representation in the form of different appealing charts, dashboards, and dynamic reports.      Artificial intelligence (AI) computerizes assignments, improves choices, and offers prescriptive bits of knowledge in addition to information visualization. AI is speeding up change in the work...

  Panel 1: Arrival in Nice Scene:  The young stud steps off a luxurious airplane, wearing sunglasses, a stylish blazer, and carrying a designer bag. Behind him, the stunning French Riviera is visible. Dialogue: 💬  Stud (thinking):  "Nice, France—where romance, elegance, and adventure await… and so do the ladies." Panel 2: Strutting Through the Streets Scene:  The stud confidently walks along the Promenade des Anglais, winking at women, expecting their admiration. The background features charming French cafés and palm trees. Dialogue: 💬  Stud (thinking):  "Time to charm the French beauties. Bonjour, mademoiselles." 👩‍🦳  Old French Lady:  "Oh la la… Such confidence!" (giggles) 👩  Young Woman (whispering to her friend):  "Is he serious?" (amused) Panel 3: Trying to Order at a Café Scene:  The stud sits at a fancy French café, trying to order a meal. The waiter looks unimpressed as the stud butchers French pronunciation. Dialo...

  A New York Times article about the CEO of Klarna, Sebastian Siemiatkowski, and his proclamations around artificial intelligence (AI) supplanting human contributors underscores an ongoing trend among tech companies. According to Siemiatkowski, AI has the potential to free staff from repetitive work, enabling people to concentrate on more important tasks. But critics say those kinds of comments are often just ways for companies to grab investor attention, especially with Klarna looking toward an IPO. This conversation is part of larger questions over the role of A.I. in transforming the job market—if it makes things work better or simply replaces jobs. Chapter 6 of "Discovering Data" mentions spreadsheet and business intelligence tools such as Tableau and how data visualization techniques can help convert that raw data into usable information, as discussed in this chapter. In contrast to the Klarna CEO’s fixation on AI-enabled automation, this chapter reinforces that business...

Today, companies use data to make well-informed decisions, improve operations, and stay competitive. This approach allows businesses to understand customer behaviors and preferences to develop targeted marketing campaigns that increase engagement and drive sales. Predictive analytics allows businesses to forecast trends, ensure proper inventory management, and predict demand, resulting in smoother operations. Moreover, customer segmentation also aids the companies in designing and promoting their products among their particular target audience, which eventually enhances retention and customer satisfaction. The power of data helps organizations minimize risks and create more effective decisions that optimize supply chains and resource allocation. We are seeing more and more AI being applied to business, and it is significantly changing industries with the automation of tasks and more efficient workflows. AI-powered companion tools like GitHub Copilot allow professionals to complete work...

In the modern digital landscape, data goes through an intricate lifecycle, starting from its origin to its processing and utilization. Discovering Data, Ch 3 emphasized that data comes from many sources — structured (e.g. databases) to unstructured (e.g. text, images). The foundation of informed decision-making, thus, is built from internal sources such as customer relationship management (CRM) systems and sales records supplemented with external data such as government datasets, social media insights, and the like. However, data collection is just the beginning; the data must go through various quality checks including accuracy, completeness, and consistency, in order to serve for reliable analysis and actionable insights. Focusing on these dimensions creates a solid foundation for an organization to leverage data. As discussed in Chapter 4, the data journey doesn’t end when it’s collected; it has to be stored and transformed in modern infrastructure. Data pipelines help transpo...

 Generative AI and predictive AI are the two primary forms of artificial intelligence driving today's technological advancements. The newer of the two, generative AI, is built to produce outputs in response to user-generated prompts, providing images, text, and even code. Generative AI has become widely accessible, empowering users and leveling the playing field across various industries. Predictive AI, often referred to as traditional AI, analyzes historical data to forecast future outcomes, with applications ranging from stock market predictions to self-driving cars. While generative AI is what we see so much of in our daily lives, predictive AI is the underlying technology that drives most of what generative AI does. Predictive AI is built using this topology of neural networks and deep data learning, where systems find patterns and make decisions based on huge data sets. These capabilities have disrupted multiple industries, from marketing and inventory management to computer v...