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With the increasing availability and impact of data in our lives, we need to make quicker, more accurate, and intricate data-driven decisions. We can see and interact with data, and identify relevant features, trends, and outliers through visual data representations. In addition, the outcomes of data analysis reflect our cognitive processes, which are strongly influenced by the design of tools. To support visual and interactive data exploration, this thesis presents a systematic and minimalist approach. First, I present the Cognitive Exploration Framework, which identifies six distinct cognitive stages and provides a high-level structure to design guidelines, and evaluation of analysis tools. Next, in order to reduce decision-making complexities in creating effective interactive data visualizations, I present a minimal, yet expressive, model for tabular data using aggregated data summaries and linked selections. I demonstrate its application to common categorical, numerical, temporal, spatial, and set data types. Based on this model, I developed Keshif as an out-of-the-box, web-based tool to bootstrap the data exploration process. Then, I applied it to 160+ datasets across many domains, aiming to serve journalists, researchers, policy makers, businesses, and those tracking personal data. Using tools with novel designs and capabilities requires learning and help-seeking for both novices and experts. To provide self-service help for visual data interfaces, I present a data-driven contextual in-situ help system, HelpIn, which contrasts with separated and static videos and manuals. Lastly, I present an evaluation on design and graphical perception for dense visualization of sorted numeric data. I contrast the non-hierarchical treemaps against two multi-column chart designs, wrapped bars and piled bars. The results support that multi-column charts are perceptually more accurate than treemaps, and the unconventional piled bars may require more training to read effectively. This thesis contributes to our understanding on how to create effective data interfaces by systematically focusing on human-facing challenges through minimalist solutions. Future work to extend the power of data analysis to a broader public should continue to evaluate and improve design approaches to address many remaining cognitive, social, educational, and technical challenges.

Higher education courses with large student enrollments are commonly offered in multiple sections by multiple instructors. Monitoring consistency of teaching activities across sections is crucial in achieving equity for all students, and in developing strategies in response to emerging patterns and outliers. To address this need, we present an approach to analyze the multivariate data of sections, assignments and student submissions collected by a learning management system (LMS) using a new data exploration framework that we call linked data summaries. Data summaries are a unit of exploration with uncluttered, analytical, comprehensible visualizations of aggregations of data records attributes. Data browsers link multiple summaries and record lists, and enable flexible and rapid data analysis through tightly coupled interaction. Our analysis approach, developed in collaboration between analytics researchers and university instructors, reveals patterns across many aspects, including assignment and section structures, submission grading and timeliness. We present findings from an analysis of three semesters of an introductory oral communication course with over 1,750 students and 90 sections per semester.