Events

Data Wrangling in Stata

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

May 20th, 10:00am–3:00pm

4218 Sewell Social Sciences

REGISTRATION IS REQUIRED FOR THIS EVENT. TO REGISTER, PLEASE VISIT: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3065 "Data Wrangling" is the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else data needs before it can be analyzed. Data wrangling is a critical skill for research. In this class you'll learn how to wrangle data using Stata.

Data Wrangling in Stata

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

May 21st, 10:00am–3:00pm

4218 Sewell Social Sciences

REGISTRATION IS REQUIRED FOR THIS EVENT. TO REGISTER, PLEASE VISIT: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3065 "Data Wrangling" is the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else data needs before it can be analyzed. Data wrangling is a critical skill for research. In this class you'll learn how to wrangle data using Stata.

NVivo Coding and Analysis

Disciplinary expertise and interdisciplinary connections

May 21st, 1:00pm–3:00pm

3218 Sewell Social Sciences

In this course, you will learn more about coding data and doing analysis in NVivo. We will cover the basics of coding types and qualitative methods used in coding. Additionally we will discuss creating cases and case classifications to capture demographic or descriptive attributes of your data, working with focus groups, and advanced data querying. This is a hands-on course where we will be working with sample data to practice the skills learned. A basic familiarity with NVivo is suggested.

Data Wrangling in Python

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

May 26th, 10:00am–3:00pm

3218 Sewell Social Sciences

REGISTRATION FOR THIS COURSE IS REQUIRED. TO REGISTER, PLEASE VISIT: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3067 "Data Wrangling" is the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else data needs before it can be analyzed. Data wrangling is a critical skill for research. This course teaches wrangling skills using mostly the data wrangling tools of the Pandas package.

Data Wrangling in Python

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

May 27th, 10:00am–3:00pm

3218 Sewell Social Sciences

REGISTRATION FOR THIS COURSE IS REQUIRED. TO REGISTER, PLEASE VISIT: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3067 "Data Wrangling" is the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else data needs before it can be analyzed. Data wrangling is a critical skill for research. This course teaches wrangling skills using mostly the data wrangling tools of the Pandas package.

Data Wrangling in Python

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

May 28th, 10:00am–3:00pm

3218 Sewell Social Sciences

REGISTRATION FOR THIS COURSE IS REQUIRED. TO REGISTER, PLEASE VISIT: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3067 "Data Wrangling" is the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else data needs before it can be analyzed. Data wrangling is a critical skill for research. This course teaches wrangling skills using mostly the data wrangling tools of the Pandas package.

Data Wrangling in Python

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

May 29th, 10:00am–3:00pm

3218 Sewell Social Sciences

REGISTRATION FOR THIS COURSE IS REQUIRED. TO REGISTER, PLEASE VISIT: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3067 "Data Wrangling" is the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else data needs before it can be analyzed. Data wrangling is a critical skill for research. This course teaches wrangling skills using mostly the data wrangling tools of the Pandas package.

Introduction to R

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

June 8th, 10:00am–3:00pm

3218 Sewell Social Sciences

REGISTRATION IS REQUIRED FOR THIS COURSE: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3068 An introduction to the basics of the RStudio interface and the R language, with topics including creating and running scripts, saving your work, using functions, and installing packages. There will be opportunities to apply what we learn during class time.

Data Wrangling in R

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

June 9th, 10:00am–3:00pm

3218 Sewell Social Sciences

REGISTRATION IS REQUIRED FOR THIS EVENT. TO REGISTER PLEASE GO TO: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3069 "Data wrangling" is the process of preparing data for analysis. This is a hands-on class with time devoted to practicing essential data wrangling skills. This course will first cover tools needed to work with different types of data. Then we will apply all of these in the context of datasets to create, transform, and clean variables.

Data Wrangling in R

Disciplinary expertise and interdisciplinary connections Inquiry, discovery, and creation

June 10th, 10:00am–3:00pm

3218 Sewell Social Sciences

REGISTRATION IS REQUIRED FOR THIS EVENT. TO REGISTER PLEASE GO TO: https://sscc.wisc.edu/sscc_jsp/training/details.jsp?class_id=3069 "Data wrangling" is the process of preparing data for analysis. This is a hands-on class with time devoted to practicing essential data wrangling skills. This course will first cover tools needed to work with different types of data. Then we will apply all of these in the context of datasets to create, transform, and clean variables.