Rejwana Haque

CMPUT 191 - Introduction to Data Science

Introduction to data acquisition, basic data manipulation (cleaning, outlier detection), analysis (regression, clustering, classification), basic statistics and machine learning tools, information visualization to communicate information from data. Prerequisite: Math 30-1. This course cannot be taken for credit if credit has been obtained in CMPUT 174, 175, 195, 274, 275, or ENCMP 100.

CMPUT 195 - Introduction to Principles and Techniques of Data Science

This course introduces data science to students with prior computing experience. It covers the basics of data acquisition, manipulation, transformation, and cleaning, as well as data analysis (e.g., regression, clustering, classification) and visualization. Students learn principles and techniques of efficient data-driven communication and decision-making in various domains using industry-standard tools. Credit cannot be obtained for both CMPUT 191 and CMPUT 195. Prerequisite: CMPUT 174 or 274.

CMPUT 200 - Ethics of Data Science and Artificial Intelligence

This course focuses on ethics issues in Artificial Intelligence (AI) and Data Science (DS). The main themes are privacy, fairness/bias, and explainability in DS. The objectives are to learn how to identify and measure these aspects in outputs of algorithms, and how to build algorithms that correct for these issues. The course will follow a case-studies based approach, where we will examine these aspects by considering real-world case studies for each of these ethics issues. The concepts will be introduced through a humanities perspective by using case studies with an emphasis on a technical treatment including implementation work. Prerequisite: one of CMPUT 191 or 195, or one of CMPUT 174 or 274 and one of STAT 151, 161, 181, 235, 265, SCI 151, MATH 181, or CMPUT 267.

INT D 491 - Data Science Capstone

Students will experience the challenges of working in a team to collect, prepare, and analyze real-world data sets in a particular application domain. Students will work with a domain expert to help discover meaningful insights in the data. Students will also apply best practices in teamwork, effective communication, and technical writing. Project experiences will be shared among the teams, to provide an interdisciplinary perspective on the uses of data science in different domains. Prerequisites: one of CMPUT 191 or 195, one of CMPUT 200, NS 115, or PHIL 385, and three of CMPUT 267, CMPUT 291, CMPUT 328, CMPUT 361, CMPUT 367, CMPUT 461, CMPUT 466, BIOIN 301, BIOIN 401, BIOL 330, BIOL 331, BIOL 332, BIOL 380, BIOL 430, BIOL 471, IMIN 410, MA SC 475, EAS 221, EAS 351, EAS 364, EAS 372, GEOPH 426, GEOPH 431, GEOPH 438, PHYS 234, PHYS 295, PHYS 420, STAT 441, STAT 471, STAT 479, AREC 313, REN R 201, REN R 426, REN R 480, FIN 440, MARK 312, OM 420, or SEM 330.

Browse more courses taught by Rejwana Haque

Lecture Notes in Computer Science. 2022 August; 13262 10.1007/978-3-031-11488-5_13

international conference on electrical, computer and communication engineering (ECCE). 2019 February; 10.1109/ECACE.2019.8679409

Journal of Bioinformatics and Genomics. 2017 May; 3 (1) 10.18454/jbg.2017.1.3.1