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This is an archive of our past training offerings. We are looking to include workshops on topics not yet covered here. Is there something not currently on the list? Send us a proposal.

E.g., 20-Jan-18
E.g., 20-Jan-18
September 15, 2014
Coordinator:
Thomas L. Piazza

This workshop will cover the basic principles and methods of sampling.  Topics will include a discussion of the various types of samples,  the creation of sampling frames, the use of stratification, and basic methods of selecting samples.  Determining an appropriate sample size will also be discussed.

 

September 15, 2014
Coordinator:
Leora Lawton

While a doctoral degree from Cal is most helpful in securing a tenure-track position, the truth is that not everyone wants to go that route, and there aren’t enough positions for everyone who completes a PhD.  Non-academic careers offer a great deal of flexibility in terms of topic area, preferred geographic location, and compatibility with spousal careers.

September 10, 2014
Coordinator:
Nick Adams

This workshop will familiarize attendees with the Undergraduate Research Apprentice Program (URAP) and the benefits and challenges of working with undegraduate research assistants. Expect to learn about:

URAP logistics and bureaucracy

Recruiting undergraduates

August 27, 2014
Coordinator:
Zawadi Rucks-Ahidiana

Organizing and the Qualitative Data Analysis Process

August 26, 2014
Coordinator:
Zawadi Rucks-Ahidiana

Organization in the Qualitative Data Analysis Process

August 25, 2014
Coordinator:
Zawadi Rucks-Ahidiana

This introductory workshop will introduce attendees to the reason for using a qualitative data analysis package for the coding and analysis process.  We will begin by discussing the distinction between coding and analysis, the benefits of using a QDA package, and then view a brief demonstration of a specific program.

Workshop: R Bootcamp
August 23, 2014 to August 24, 2014
Coordinator:
Christopher Paciorek

The workshop will be an intensive two-day introduction to R using RStudio. Topics will include

August 21, 2014 to August 22, 2014
Coordinator:
Nick Adams

This intensive course will give users a familiarity with various automated text analysis approaches including classification (machine learning), clustering/topic modelling, dimension reduction, and regression. The intensive will begin with a theoretical explanation of each approach.

August 20, 2014 to August 22, 2014
Coordinator:
Orestes "Pat" Hastings

The workshop will cover (in a teaching computer lab environment, using the General Social Survey as a common dataset):
-Getting data into Stata,
-Stata specific tools and resources (do files, logs, help files, etc)
-Coding and cleaning data (making new variables from old variables; labeling variables and values, etc)
-Collapsing data

August 20, 2014
Coordinator:
Jon Stiles

This two-hour workshop will identify common uses by researchers for secondary data into their work, different “flavors” of secondary data and their strengths and weaknesses, where you can find secondary data, and Berkeley  resources to help you locate and use data.

August 20, 2014
Coordinator:
Nick Adams

This workshop will familiarize attendees with the Undergraduate Research Apprentice Program (URAP) and the benefits and challenges of working with undegraduate research assistants. Expect to learn about:

URAP logistics and bureaucracy

Recruiting undergraduates

August 19, 2014 to August 22, 2014
Coordinator:
Dav Clark

Get an orientation to the basics of working with social science data in python. We'll start from the ground up, from the basics of naming variables, making decisions, and repeating operations to making informative (or at least fun) pictures on your screen.

August 18, 2014
Coordinator:
Dav Clark

An intro to the basics that instructors often assume you know, but probably never had good instruction on! After this course, you should be able to more easily start learning to program (i.e., in R or python), or communicate better with your collaborators who are programming.

Workshop: ASA Datathon
August 15, 2014 to August 16, 2014
Coordinator:
Laura Nelson

Big cities, big data:
Big opportunity for computational social science
UC-Berkeley D-Lab // Hilton Union Square
Sign up by August 1

July 14, 2014 to July 18, 2014
Coordinator:
Dominik Hangartner, Marco Steenbergen

D-Lab will be hosting two workshops of the ICPSR Summer Program in Quantitative Methods of Social Research. Payment and registration are handled by ICPSR.

June 23, 2014 to June 27, 2014
Coordinator:
John Fox

D-Lab will be hosting two workshops of the ICPSR Summer Program in Quantitative Methods of Social Research. Payment and registration are handled by ICPSR.

June 10, 2014 to July 8, 2014
Coordinator:
Leora Lawton

This workshop will be open to anyone interested in having the guidance, feedback and structure for writing a grant. Potential participants could be faculty who have not written an NIH grant before, postdocs or adjunct faculty, advanced graduate students, or even early stage graduate students who want to put together a dissertation grant.

June 4, 2014 to June 5, 2014
Coordinator:
Zawadi Rucks-Ahidiana

This two part series includes how to organize and analyze qualitative data, and how to use Atlas.TI in the process. Researchers new to qualitative data analysis are encouraged to attend both sessions. More experienced researchers may choose to attend the second session only to learn more advanced techniques with Atlas.TI.

June 3, 2014 to June 5, 2014
Coordinator:
Pat Hastings

The workshop will cover (in a teaching computer lab environment, using the the General Social Survey as a common dataset): Getting data into StataStata specific tools and resources (do files, logs, help files, etc)Coding and cleaning data (making new variables from old variables; labeling variables and values, etc) Collapsing dataDescriptive statistics (tables and graphs)CrosstabsCorrelation ta

June 3, 2014 to June 4, 2014
Coordinator:
Laura Nelson

This INTENSIVE course will give users a familiarity with various automated text analysis approaches including classification (machine learning), clustering/topic modelling, dimension reduction, and regression.

The INTENSIVE will begin with a theoretical explanation of each approach and then demonstrate each in R, finishing with some practice work for participants. 

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