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This course is an intermediate-level course designed for learners who want to continue their data visualization journey with Qlik Sense, a powerful sophisticated Business Intelligence tool. Data preparation and ingestion is a key prerequisite for data visualization, and this course not only dives deep into that concept, but other important intermediate topics such as filters, expressions, and personalization. The second of a series of three courses on Qlik Sense emphasizes the significance of moving beyond basic chart creation. In today's data-driven landscape, businesses and professionals require more than simple visualizations to gain a competitive edge. They need to develop a system of highly customized and tailored dashboards that precisely align with their unique data needs. These dashboards serve as powerful tools for effective decision-making and data-driven insights. For example, marketing teams can benefit from dashboards that provide real-time metrics on campaign performance, audience engagement, and conversion rates. This course provides a comprehensive understanding of data visualization with Qlik Sense. It begins with data ingestion and visualization, including how to load data from various sources, how to clean data, and how to build data models. The course then goes through how to create sophisticated charts and dashboards, with intermediate concepts like filters and expression. Finally, the course demonstrates how to apply personalization to tailor the functionality and look of the dashboard. This course is tailored for those looking to advance their data visualization skills with Qlik Sense. It's ideal for data analysts seeking to craft compelling narratives or arguments through data, as well as individuals aiming to effortlessly transform datasets into actionable graphs using Qlik Sense. To excel in this course, intermediate-level learners should have the following prerequisites: a basic grasp of Qlik Sense, familiarity with simple Excel functions or similar spreadsheet software, a foundational understanding of data statistics (e.g., mean, sum, etc.), and an enthusiastic, proactive learning approach.