If you are looking to get started in Data Science, or in the entry-level to intermediate level, this book is just the right fit for you. The “Hands-On Introduction to Data Science” newly published book by our lab director, Dr. Shah, is filled with hands-on examples, a wide range of practices and real-life applications that will help you develop a solid understanding of the subject. No prior technical background or computing knowledge needed for this book
If you are instructors and looking for a good textbook for your class, the book also provides end-to-end support for teaching a data science course. The book provides curriculum suggestions, slides for each chapter, datasets, program scripts, and solutions to each exercise, as well as sample exams and projects.
Reviews & Endorsements ‘Dr. Shah has written a fabulous introduction to data science for a broad audience. His book offers many learning opportunities, including explanations of core principles, thought-provoking conceptual questions, and hands-on examples and exercises. It will help readers gain proficiency in this important area and quickly start deriving insights from data.’ Ryen W. White, Microsoft Research AI.
Book Summary: This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.
Almost everything in the book is accompanied with examples and practice – both in-chapter and end-of-chapter so students are more engaged because they can use hands-on experiences to see how theories relate to solving practical problems
Assumes no prior technical background or computing knowledge and lowers the barrier for entering the field of data science so that students from a range of disciplines can benefit from a more accessible introduction to data science
Supplemented by a generous set of material for instructors, including curriculum suggestions and syllabi, slides for each chapter, datasets, program scripts, answers and solutions to each exercise, as well as sample exams and projects which gives instructors end-to-end support for teaching a data science course
To tackle the complex search tasks, we try to identify task’ states and study the connection between states and search behaviors. We found that the task state can be predicted from the user’s search behaviors. Read about our study in this article by Jiqun Liu, Shawon Sarkar, and Chirag Shah:
Complex search tasks that involve uncertain solution space and multi-round search iterations are integral to everyday life and information-intensive workplace practices, affecting how people learn, work, and resolve problematic situations. However, current search systems still face plenty of challenges when applied in supporting users engaging in complex search tasks. To address this issue, we seek to explore the dynamic nature of complex search tasks from process-oriented perspective by identifying and predicting implicit task states. Specifically, based upon the Web search logs and user annotation data (regarding information seeking intentions in local search steps, in-situ search problems, and help needed) collected from 132 search sessions in two controlled lab studies, we developed two task state frameworks based on intention state and problem-help state respectively and examined the connection between task states and search behaviors. We report that (1) complex search tasks of different types can be deconstructed and disambiguated based on the associated nonlinear state transition patterns; and (2) the identified task states that cover multiple subtle factors of user cognition can be predicted from search behavioral signals using supervised learning algorithms. This study reveals the way in which complex search tasks are unfolded and manifested in users’ search interactions and paves the way for developing state-aware adaptive search supports and system evaluation frameworks.
It’s getting increasingly more important to understand, evaluate, and perhaps rethink our search results as they continue to show bias of various kinds. Given that so much of our decision-making relies on search engine results, this is a problem that touches almost all aspects of our lives. Read about some of our new works in a new article by InfoSeekers Ruoyuan Gao and Chirag Shah:
With the increasing popularity and social influence of search engines in IR, various studies have raised concerns on the presence of bias in search engines and the social responsibilities of IR systems. As an essential component of search engine, ranking is a crucial mechanism in presenting the search results or recommending items in a fair fashion. In this article, we focus on the top-k diversity fairness ranking in terms of statistical parity fairness and disparate impact fairness. The former fairness definition provides a balanced overview of search results where the number of documents from different groups are equal; The latter enables a realistic overview where the proportion of documents from different groups reflect the overall proportion. Using 100 queries and top 100 results per query from Google as the data, we first demonstrate how topical diversity bias is present in the top web search results. Then, with our proposed entropy-based metrics for measuring the degree of bias, we reveal that the top search results are unbalanced and disproportionate to their overall diversity distribution. We explore several fairness ranking strategies to investigate the relationship between fairness, diversity, novelty and relevance. Our experimental results show that using a variant of fair ε-greedy strategy, we could bring more fairness and enhance diversity in search results without a cost of relevance. In fact, we can improve the relevance and diversity by introducing the diversity fairness. Additional experiments with TREC datasets containing 50 queries demonstrate the robustness of our proposed strategies and our findings on the impact of fairness. We present a series of correlation analysis on the amount of fairness and diversity, showing that statistical parity fairness highly correlates with diversity while disparate impact fairness does not. This provides clear and tangible implications for future works where one would want to balance fairness, diversity and relevance in search results.
A new article published by InfoSeekers Shawon Sarkar, Matt Mitsui, Jiqun Liu, and Chirag Shah in the Journal of Information Processing and Management (IP&M), shows how we could use behavioral signals from a user in a search episode to explicate their information need, their perceived problems, and the potential help they may need.
Here are some highlights.
The amount of time spent on previous search results could be an indicator of potential problems in articulation of needs into queries, perceiving useless results, and not getting useful sources in the following search stage in an information search process.
While performing social tasks, users mostly searched with an entirely new query, whereas, for cognitive and moderate to high complexity tasks, users used both new and substituted queries as well.
From users’ search behaviors, it is possible to predict the potential problem that they are going to face in the future.
User’s search behaviors can map an information searcher’s situational need, along with his/her perception of barriers and helps in different stages of an information search process.
By combining perceived problem(s) and search behavioral features, it is possible to infer users’ needed help(s) in search with a certain level of accuracy (78%).