• Contact Info

Bhatti, Shahzad

Teaching Associate Professor



courses taught

  • INFO 2301 - Quantitative Reasoning for Information Science
    Primary Instructor - Fall 2022 / Spring 2023
    Introduces methods for quantifying and analyzing different types of data, covering foundational concepts in discrete mathematics, probability, and predictive modeling, along with complementary computational skills to apply these concepts to real problems. Covers counting and combinatorics, logic, set theory, introductory probability, common probability distributions, regression, and model validation. Requires demonstrated proficiency with introductory computer programming.
  • INFO 3401 - Information Exploration
    Primary Instructor - Fall 2022 / Spring 2023
    Teaches students how to use information to identify interesting real world problems and to generate insight. Students will learn to find, collect, assemble and organize data to inspire new questions, make predictions, generate deliverables, and work towards solutions. They will learn to appropriately apply different methods (including computational, statistical and qualitative) for exploratory data analysis in a variety of domains.
  • INFO 4602 - Mastery in Information Science: Information Visualization
    Primary Instructor - Spring 2023
    Explores the design, development and evaluation of information visualizations. Covers visual representations of data and provides hands-on experience with using and building exploratory tools and data narratives. Students create visualizations for a variety of domains and applications, working with stakeholders and their data. Covers interactive systems, user-centered and graphic design, perception, data storytelling and analysis, and insight generation. Programming knowledge is strongly encouraged. Counts as Mastery in Information Science. Same as INFO 5602.
  • INFO 4604 - Mastery in Information Science: Applied Machine Learning
    Primary Instructor - Fall 2022
    Introduces algorithms and tools for building intelligent computational systems. Methods will be surveyed for classification, regression and clustering in the context of applications such as document filtering and image recognition. Students will learn the theoretical underpinnings of common algorithms (drawing from mathematical disciplines including statistics and optimization) as well as the skills to apply machine learning in practice. Counts as Mastery in Information Science. Same as INFO 5604.