Introduction to Research Methods

Grade: A

Introduction to the scientific method. Methods of knowledge acquisition, research questions, hypotheses, operational definitions, variables, control. Observation, levels of measurement, reliability, validity. Experimentation and design: between-groups, within-subjects, quasi-experimental, factorial, single-subject. Correlational and observational studies. Ethics in research

Theoretical Foundations of Data Science I

Grade: A

The sequence introduces the theoretical foundations of data science. The course exposes students to the mathematical theory underlying fundamental topics in machine learning. Topics include empirical risk minimization, optimization, regression, classification, and discrete probability. Students practice creative problem-solving while learning how to rigorously justify and communicate mathematical ideas

Ethics and Society II

Grade: A

An examination of a single set of major contemporary social, political, or economic issues (e.g., environmental ethics, international ethics) in light of ethical and moral principles and values

Introduction to Statistical Analysis

Grade: A

Introduction to descriptive and inferential statistics. Tables, graphs, measures of central tendency and variability. Distributions, Z-scores, correlation, regression. Probability, sampling, logic of inferential statistics, hypothesis testing, decision theory. T-test, one and two-way Anova, nonparametric tests (Chi-square)

Theoretical Foundations of Data Science II

Grade: A-

The sequence DSC 40A-B introduces the theoretical foundations of data science. DSC 40B, the second course in the sequence, covers the fundamentals of computer science with applications to data science. Topics include time complexity analysis, the analysis of recursive algorithms, graph theory, and graph search algorithms

Statistical Methods

Grade: A

Introduction to probability. Discrete and continuous random variables–binomial, Poisson and Gaussian distributions. Central limit theorem. Data analysis and inferential statistics: graphical techniques, confidence intervals, hypothesis tests, curve fitting

Practice of Data Science

Grade: A-

The marriage of data, computation, and inferential thinking, or “data science,” is redefining how people and organizations solve challenging problems and understand the world. This course bridges lower- and upper-division data science courses as well as methods courses in other fields. Students master the data science life-cycle and learn many of the fundamental principles and techniques of data science spanning algorithms, statistics, machine learning, visualization, and data systems

Great Performances on Film

Grade: A

Course examines major accomplishments in screen acting from the work of actors in films or in film genres

Creating the Role of "Leader"

Grade: A

Acting and leadership require “choices” to play a role. The role of leader requires authenticity, collaboration, listening, presence, vision, and influence. Students will explore characteristics of strong leaders, and utilizing traditional acting techniques build powerful capabilities of leadership

Cult Films: Weirdly Dramatic

Grade: A

A select survey of eight to ten exceptional offbeat, frequently low-budget films from the last sixty years that have attained cult status

Topics in Theater and Film: Hayao Miyazaki + Studio Ghibli

Grade: A+

Great films and the performance of the actors in them are analyzed in their historical, cinematic, or theatrical contexts. This course examines the actor’s contribution to classic cinema and the social and aesthetic forces at work in film

Intro to Data Management

Grade: A

This course is an introduction to storage and management of large-scale data using classical relational (SQL) systems, with an eye toward applications in data science. The course covers topics including the SQL data model and query language, relational data modeling and schema design, elements of cost-based query optimizations, relational data base architecture, and database-backed applications

Recommender Systems and Web Mining

Grade: A+

Current methods for data mining and predictive analytics. Emphasis is on studying real-world data sets, building working systems, and putting current ideas from machine learning research into practice

Data Science in Practice

Grade: A+

Data science is multidisciplinary, covering computer science, statistics, cognitive science and psychology, data visualization, artificial intelligence and machine learning, among others. This course teaches critical skills needed to pursue a data science career using hands-on programming and experimental challenges

Probabilistic Modeling and Machine Learning

Grade: A

The course covers learning and using probabilistic models for knowledge representation and decision-making. Topics covered include graphical models, temporal models, and online learning, as well as applications to natural language processing, adversarial learning, computational biology, and robotics

Systems for Scalable Analytics

Grade: A+

This course introduces the principles of computing systems and infrastructure for scaling analytics to large datasets. Topics include memory hierarchy, distributed systems, model selection, heterogeneous datasets, and deployment at scale. The course will also discuss the design of systems such as MapReduce/Hadoop and Spark, in conjunction with their implementation. Students will also learn how dataflow operations can be used to perform data preparation, cleaning, and feature engineering

Data Visualization

Grade: A

Data visualization helps explore and interpret data through interaction. This course introduces the principles, techniques, and algorithms for creating effective visualizations. The course draws on the knowledge from several disciplines including computer graphics, human-computer interaction, cognitive psychology, design, and statistical graphics and synthesizes relevant ideas. Students will design visualization systems using D3 or other web-based software and evaluate their effectiveness

Data Analysis and Inference

Grade: A

An introduction to various quantitative methods and statistical techniques for analyzing data—in particular big data. Quick review of probability continuing to topics of how to process, analyze, and visualize data using statistical language R. Further topics include basic inference, sampling, hypothesis testing, bootstrap methods, and regression and diagnostics. Offers conceptual explanation of techniques, along with opportunities to examine, implement, and practice them in real and simulated data

Intro to Machine Learning

Grade: A+

Broad introduction to machine learning. The topics include some topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting, and perceptrons; and topics in unsupervised learning, such as k-means and hierarchical clustering. In addition to the actual algorithms, the course focuses on the principles behind the algorithms

Modeling and Data Analysis

Grade: A

Exposure to the basic computational methods useful throughout cognitive science. Computing basic statistics, modeling learning individuals, evolving populations, communicating agents, and corpus-based linguistics will be considered

Data Science Project I

Grade: A

In this two-course sequence students will investigate a topic and design a system to produce statistically informed output. The investigation will span the entire lifecycle, including assessing the problem, learning domain knowledge, collecting/cleaning data, creating a model, addressing ethical issues, designing the system, analyzing the output, and presenting the results

Practical Data Science in R

Grade: A+

Learn coding for data analysis using the R programming language. Course focus will be on practical and applied skills in asking data-informed questions, data wrangling, data visualization, building statistical learning models, and communicating your findings to advance your career

Real Estate and Development Market Analysis

Grade: A+

This course examines the analysis of demand for real estate products and site-specific real estate development projects. Consideration is given to relevant factors such as economic change, social attitudes, and changing laws

Films of Spike Lee

Grade: A+

Students view eight to ten films of this important filmmaker to examine style; genre; screenwriting; directing; cinematography; recurring themes; the place of this work in (African) American history; race and movie industry politics; and critical responses

Data Science Project II

Grade: A

In this two-course sequence students will investigate a topic and design a system to produce statistically informed output. The investigation will span the entire lifecycle, including assessing the problem, learning domain knowledge, collecting/cleaning data, creating a model, addressing ethical issues, designing the system, analyzing the output, and presenting the results

Computer Applications to Data Management

Grade: A

Develop skills in computer management and analysis of sociological data. Practical experience with data produced by sociological research. Students will develop competency in the analysis of sociological data, by extensive acquaintance with computer software used for data analysis and management

Cyborgs Now and in the Future

Grade: A+

Covers the theories of situated, distributed, enactive, and embodied cognition. Explains how cyborgs are a natural consequence of our current understanding of embodied minds embedded in culturally shaped niches; how mental systems can be distributed over other people and things

Data and Society

Grade: A

This course explores the roles, challenges, and impacts of data and information technologies in contemporary societies. Information regarding discussion section is to be discussed in the first week of class

Sensation and Perception

Grade: A+

An introduction to the experimental study of cognition with a focus on sensation and perception

Gospel Choir

Grade: P