About

The course will teach principles and techniques of data science to prepare students in the humanities and sciences to solve problems with data. Students will learn methods of exploration, prediction and inference to detect patterns, determine unknown information from known information, and quantify uncertainties. Programming and query languages will enable students to manipulate data and implement models. Through a focus on examples, students will gain experience with applications to provide context for data science.

The instructor is Christopher Policastro. Lecture will be held on Mondays and Wednesdays 4:55-6:10pm in GCASL C95. Labs will be held once a week on either Tuesdays 3:55pm-4:45pm, Thursdays 3:55pm-4:45pm or Fridays 11am-11:50am/12:55pm-1:45pm in 60th 5th Avenue.

In the undergraduate program for data science, Introduction to Data Science succeeds Data Science for Everyone and precedes Causal Inference, Responsible Data Science, and Advanced Topics in Data Science. For questions about the program please contact Andrea Jones-Rooy.

Familiarity with programming is a prerequisite. Students can gain experience through courses such as Introduction to Computer Programming (CSCI-UA 2 or CSCI-UA 3) or Introduction to Computer Science (CSCI-UA 101). For questions about registration, please contact Tim Baker

The lessons, labs and assignments throughout the semester will help prepare students for courses on databases, statistics or machine learning–particularly within the data science program. Experience with programming and query languages will enable students to pursue a career in data science. The computational and statistical methods will empower students to gain practical insights through data.

Formulating Problems

Before we can understand a collection of data, we have to phrase the problem. The phrasing will allow us to compare different hypotheses according to metrics for evaluation. Whether through a census, a survey, an experiment, or observations, we try to gather information from the data. We will learn methods for randomly and non-randomly gathering… Read more Formulating Problems

Posted on August 17, 2019August 17, 2019 by Christopher Policastro

Handling Data

The data will inform the problems. Limits on the data will impose limits on the methods. Without methods we cannot approach the problems. The back-and -forth requires that we learn to acquire, manipulate and query the data. We will employ tools to organize and access the data in a tabular format. The tabular format will… Read more Handling Data

Posted on August 17, 2019August 17, 2019 by Christopher Policastro

Exploring Information

We will use packages to plot data. The graphs and charts will help us to look for what we believe is there and what we believe is not there. Exploration of the data will inform the approach to the problem. However, the relevance of the data to the problem needs assessment. We will learn to… Read more Exploring Information

Posted on August 17, 2019August 17, 2019 by Christopher Policastro

Ethical Issues

We will use data to fit models that will allow us to make predictions and inferences. Having learned metrics for accuracy and inaccuracy, we can assess the models. If the model generalizes to different situations, then we have confidence in the findings. However, what do the findings say about the world? If the data… Read more Ethical Issues

Posted on August 17, 2019August 17, 2019 by Christopher Policastro