Loyola University Maryland

Messina

Stories We Tell Course Pairing

Data’s Untold Stories: An Introduction to Computer Science (CS151)

What stories are data itching to tell us? How do we discover those stories and share them with the world? Students in this course will learn how to solve interesting and relevant data science problems using the Python programming language. Students will also gain insights into major areas of computer science, including software engineering, computer hardware, artificial intelligence, and ethical and societal issues in computing. No prior computer science knowledge is expected. Satisfies one math/science core requirement.

Faculty Biographies

Section CS151.01S - Dawn Lawrie is a professor in the department of computer science and an adjunct researcher at Johns Hopkins University. She holds a PhD from the University of Massachusetts and was recognized at the Loyola University’s Twelfth Annual Dean’s Symposium for outstanding achievement in research, teaching, and service. Her areas of expertise include information retrieval, natural language processing, and software engineering. Her current research focuses on creating algorithms that identify facts about people, places, and organizations so a machine can reason about those facts.

Section CS151.02S - Sibren Isaacman is an Assistant Professor of Computer Science at Loyola University Maryland. He received his Ph.D. from Princeton University in 2012. His research interests focus on the opportunities and challenges presented by mobile devices.

Introduction to Statistics: Stories Data Tell (ST210)

Consumers are continuously inundated with statistical information, from which car to purchase, which candidate one should vote for, and whether or not a new cancer treatment is effective. To understand the story the data is telling, a sound background in statistics is necessary.  This course will introduce students to the application of statistics in diverse fields.  We will extract the story from data using graphical and numerical methods as well as statistical inference procedures, and use the computer to efficiently perform computations and construct graphs. Statistical methods are motivated through real data sets. This is a non-calculus-based course covering descriptive statistics, simple linear regression, normal, binomial, and sampling distributions, estimation, and hypothesis testing. The course will use a new approach to understanding statistical inference using the computationally intensive resampling approach.

Students planning on majoring in computer science, mathematics, or statistics are encouraged to take this Messina pairing. Of course, other majors are welcome.  These two courses can count as two of the three required science core courses.

Faculty Biography

Dr. Morrell has a Ph.D. in statistics from the University of Wisconsin-Madison.  He had been a faculty member at Loyola over 30 years.  In addition to teaching undergraduate statistics, Dr. Morrell works with researchers at the National Institute on Aging to study how various characteristics of people change as they age. He is now also the director of the new Data Science master’s program. Dr. Morrell has been an advisor of first year students for many years.

Mentor Biography

Patrick Durkin is Assistant Director of Event Services.  A Loyola graduate from the class of 2001, Patrick has been working Full Time at the University in the Event Services department for the last 14 years.  He is originally from Buffalo, NY (Go Sabres!) where he graduated from Canisius High School.  He spends as much time as he can with his two young daughters, who are already looking forward to being part of Loyola’s classes of 2031 and 2034!

I am an Assistant Professor of Computer Science at Loyola University Maryland. I received my Ph.D. from Princeton University in 2012. My research interests focus on the opportunities and challenges presented by mobile devices.

The rapid and near universal adoption of mobile devices from laptops to cell phones means that we have a new way to think about computing. My research has tackled the use of these devices from two different angles. First, I have researched exploiting the ubiquity of the devices to gain insight into how people move. To this end, I have used cell phones as mobility sensors to understand an model human mobility patterns. Second, I have looked at using mobile devices as a low-overhead method of delivering data even to remote, network challenged areas of the world.

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