STATISTICS AND
DATA SCIENCE
(SDS)

In Freshman Seminar I, students will take a cross-cultural perspective on human existence in the context of the many worlds we all occupy: natural, social and existential. Students will explore human existence in the context of post-Darwinian understandings of what it means to be a human animal. Through exploring the work of both Confucian philosophers and evolutionary theories of ethics, they will ask what it means to be a social animal. And they will explore the rich traditions of existentialism to explore what it means to find ourselves here, evolved beings living in society with each other, conscious of our limitations, our freedom and our death. This Seminar I course will give students the ability to move between radically different frameworks of understanding to derive rich and complex insights into the human experience.

English Composition I focuses on the foundation skills of university-level writing. The course will take students through all the steps of the expository writing process, from pre-writing to revising and proofreading. Students will work through a series of expository essays, including narratives and descriptive pieces. The course will support them in acquiring appropriate critical reading skills, as well as following conventions of standard English in writing. Students will also be introduced to selecting, using, and correctly referencing sources.

This course is an introduction to differential calculus and is designed to meet the needs of Statistics and Data Science students. Topics will cover functions, limits, derivatives and applications. Basic concept of integration is also included.

This course is an introduction to Microeconomics from a very broad perspective. Microeconomics is the science of how people use resources. A large part is about decision making: Which is the best route for going to school, and should I walk or take the bus? Can I get a dog as a pet, should I buy vegetables in the market in the morning or in the afternoon, and why are the prices different in different cities? This course is a foundation course which is needed in order to follow higher level courses in the Economics module.

In Freshman Seminar II, students will explore questions about human knowledge and understanding. This seminar course will build on Freshman Seminar I, to ask challenging questions about our knowledge of ourselves, each other and the world we inhabit. It will equip students to engage more deeply with questions of knowledge, its uses and its misuses. And it will develop student’s critical awareness of different ways of approaching the question of what it means to know.

English Composition II focuses on the process of argumentative writing, from initial development through drafting and revising to the final product. This course focuses on students' ability to use sources to form strong arguments in academic writing. In this course, students will design their own arguments using sources to write and present their ideas effectively.

This course is a continuation of Calculus I. The topics cover integration and its applications, series and sequences.

This course provides an introduction to the quantitative tools for monitoring, analyzing data, and evaluating data. Through practical and real-world applications, students learn statistical methods that can be used in quantitative analysis of real-world problems. This course focuses both on concepts underlying statistical methods as well as problem solving through the use of STATA, a popular statistical software package.

In Sophomore Seminar 1, we will explore how difference is socially and historically constructed, what it can mean to us, and how it can act on us. We will also ask critical questions on what we already know about differences among humans in today’s world. The Seminar will start with exploring the concept of “the Other”, which is one aspect of difference. We will look into different views on “the Other” from different parts of the world.

This is an introduction to probability for statistics and data science students. There is no prerequisite for this course. In addition to major students, this course is relevant and useful for non-major students.

This course is an introduction to Macroeconomics from a very broad perspective. Macroeconomics analyzes topics such as economic growth, inflation and unemployment. How can the central bank influence the economy in times of crises? What should the government do in order to counteract environmental issues? Why do countries trade with each other? Though ultimately based on the actions of individuals and firms (which we will analyze in Microeconomics), Macroeconomics is concerned with developments on the aggregate level: countries as a whole, government spending, taxation and Central Bank policies. This course is a foundation course which is needed in order to follow higher level courses in the Economics module.

This course is an introduction to programming with Python for students without any previous programming experience. Throughout the class, we will cover different data types, writing functions, using packages like Numpy and Python and creating data visualizations. We will also use version control with Git.

Sophomore Seminar II introduces students to urgent contemporary questions about our shared human future. Drawing on both literature and theory, students will be challenged to imagine and reimagine the possibilities for our collective and individual futures. The seminar will begin by looking at notions of utopia and dystopia, placing these in the context of the present: in particular in the social and economic frameworks of capitalism. Students will explore the ways in which imagining the future throws light on our present concerns and dilemmas.

The course is structured in two parts: Data Communication and Data Ethics. Data Communication is the last and the most visible step in the data pipeline. It is important to apply ethical thinking there but also holistically in the whole pipeline.

This course is an introduction to the techniques of linear algebra. Topics covered include, systems of linear equations, Gaussian elimination, vectors in Rn, matrices, inverses, determinants, eigenvalues and vector geometry.

The course is a broad introduction to the discipline of political science. Students will be introduced to subfields within the discipline: political theory, comparative politics, and international relations. Then, the bulk of the course examines major themes in comparative politics, including the formation of nation-states, political regimes, and political violence. We will also examine themes that cut across the subfields, including globalization, populism, and human rights.

This course is an overview over important algorithmic concepts and how efficient algorithms depend on the design of suitable data structures.

This course is an introduction to Machine Learning. Requirements for this class are completion of basic Mathematics and Statistics modules, as well as the Introduction to Python. Throughout the class, we will cover the data preprocessing process as well as different types of machine learning models from the realms of supervised and unsupervised learning together with model evaluation metrics.

This course will introduce students with higher level statistical programming using R, data visualization with base graphics and ggplot2, reproducible reports with Markdown, and developing dashboards with Shiny.

In this course, students will explore the challenges of writing nonfiction in forms that go beyond the academic essay. In particular, they will explore the following:

1. The craft of memoir and the transformative power of personal storytelling

2. The art of literary reportage

3. The literary essay — bridging the personal and the political.

This course aims to take students from first steps in writing creative nonfiction to engaging with the global marketplace for creative nonfiction writing.

This course is an overview over important algorithmic concepts and how efficient algorithms depend on the design of suitable data structures.

The course will introduce students to a basic introduction to modern time series analysis. The course will cover topics such as time series regression and exploratory data analysis, Fourier analysis, ARMA/ARIMA models, model identification/estimation/linear operators, spectral estimation, and state space models. The analyses will be performed using a freely available package astsa. Both R and RStudio will be required for this class.

This course is an in-depth dive into probabilities, distributions, random processes, and simulation.

In this course we will confront some of the major ethical issues that arise in our society—the treatment of animals (vegetarianism, experimentation), the beginning of life (abortion, in vitro, PGD testing), the ethics of war (when to go, how to wage), the ethics of politics (what ought our representatives do), the end of life (right to die, suicide, euthanasia), fear of death, the ethics of food, and the environment. In Ethics, we seek not simply opinions or personal positions on these contentious problems, but hope to make a broader claim about right and wrong.

Senior Capstone is a year-long project-based/research-based course that all senior students have to take and complete. Senior Capstone I is taken in the first semester of their senior year and Capstone II is taken in the second semester of their senior year. Senior Capstone projects are always associated with student’s declared majors.

This course covers a number of more advanced Machine Learning algorithms and their applications. Requirements for this class are completion of basic Mathematics and Statistics modules, as well as the Introduction to Python and the Introduction to Machine Learning. Throughout the class, we will cover a range of supervised, unsupervised and reinforcement learning algorithms as well as directly apply them to relevant data. We will also cover hyperparameter tuning as well as model evaluation.

Operating Systems (OS) are the intermediaries between the user and the computer. Learning about the basic functions and architecture broadens the abilities and horizon of the user, working with the machines more efficiently and effectively. Networks are at the heart of today's web and define how data is communicated.

The course is a broad introduction to the discipline of political science. Students will be introduced to subfields within the discipline: political theory, comparative politics, and international relations. Then, the bulk of the course examines major themes in comparative politics, including the formation of nation-states, political regimes, and political violence. We will also examine themes that cut across the subfields, including globalization, populism, and human rights.

Senior Capstone is a year-long project-based/research-based course that all senior students have to take and complete. Senior Capstone I is taken in the first semester of their senior year and Capstone II is taken in the second semester of their senior year. Senior Capstone projects are always associated with student’s declared majors.

This course gives an overview over different text mining algorithms and related disciplines such as web scraping. Requirements for this class are completion of basic Mathematics and Statistics modules, as well as the Introduction to Python and Introduction to Machine Learning. Throughout the class, we will cover the specifics of text data and learn how to pre-process it to make it usable in algorithms. We will cover two different techniques for gathering text data, namely web scraping and working with APIs. And we will see some algorithms for Natural Language Processing.

This course is an introduction to Bayesian statistics with an emphasis on practical applications to inference. Students will learn both how set up and analyze problems in the Bayesian framework. The course will teach students to use R and JAGS software for modelling well as the Markov Chains Monte Carlo method (MCMC) for computation. The main topics to be covered include: Bayes’ theorem, prior and posterior distributions, inference for discrete and continuous random variables, hypothesis testing and model selection and linear regression. The course will consist of lectures and in class computer sessions will be devoted to doing the modelling and estimation. Participation in these online working sessions is required.

In his 2014 State of the Union address, President Obama bemoaned that in the United States women get paid only 77% of the wages earned by men for comparable work, a claim that involves comparing two population means and tested using sample data. While such a comparison might seem straightforward, it is actually hard because other factors—how sample data are collected, which individuals choose to participate in the labor market, variations in their education and family background etc.—are likely to affect the sample means. Using sophisticated methods, econometrics allows analysts to cleverly isolate the effect of the wage gap due to gender alone. It is this last aspect that makes econometrics both challenging and exciting, and this course promises to take you on an exciting sleuthing journey at the end of which you could expect to develop the skills of a thoughtful data detective.