Summer 2018 Courses

Title
Course Number
Times & Locations
Description 
Instructor
Units
Foundations of Data Science (Data 8)

STAT C8/ COMPSCI C8

CCN: 14344

Summer C: 

MTWTF

9-10 Dwinelle 155

Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

Vinitra Swamy, Fahad Kamran, Deborah Nolan

4
Environmental Health and Development

ESPM c167/ PBHLTH c160

CCN: 14966

Summer D:

MF 9-11

W 9-11:30 

Haviland 12

The health effects of environmental alterations caused by development programs and other human activities in both developing and developed areas. Case studies will contextualize methodological information and incorporate a global perspective on environmentally mediated diseases in diverse populations. Topics include water management; population change; toxics; energy development; air pollution; climate change; chemical use, etc. 

TBD

4
Demographic Methods: Introduction to Population Analysis

DEMOG 110

CCN: 14956

Summer A:

MTWTh

12-2

Barrows 170

Measures and methods of Demography. Life tables, fertility and nuptiality measures, age pyramids, population projection, measures of fertility control. 

TBD

3
Social Networks

DEMOG 180

CCN: 14961

Summer A:

The science of social networks focuses on measuring, modeling, and understanding the different ways that people are connected to one another. We will use a broad toolkit of theories and methods drawn from the social, natural, and mathematical sciences to learn what a social network is, to understand how to work with social network data, and to illustrate some of the ways that social networks can be useful in theory and in practice. We will see that network ideas are powerful enough to be used everywhere from UNAIDS, where network models help epidemiologists prevent the spread of HIV, to Silicon Valley, where data scientists use network ideas to build products that enable people all across the globe to connect with one another.

TBD

3