Get GSU Data Ready! Badge Micro-Credentials for completing these workshops to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
The R workshop series will introduce participants to the fundamentals of using the R programming language and associated tools for the purposes of performing common data analysis tasks. The R programming language is 100% free to use and is extremely popular amongst researchers in both academia, business, and non-profits. It is especially useful for conducting statistical analysis.
This series consists of four workshops. For individuals who are new to R, coding, or data analysis, it is highly recommended that the workshops be attended in sequential order. Additionally, while these workshops are taught exclusively using code (i.e. there are no point-and-click methods), attendees do not need to have any prior experience with programming, coding, or scripting. All are welcome.
Skill Requirements: None.
Software Requirements for Hands-on Participation:
For participants wishing to follow along with the “hands-on” portion of the workshop, please see the directions at the following url: https://research.library.gsu.edu/R/workshop
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Get a GSU Data Ready! Badge Micro-Credential for completing these workshops to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
The Python & Data workshop series will introduce participants to the fundamentals of using the Python programming language and associated tools for the purposes of performing common data analysis tasks. Python is an extremely popular programming language used by analysts, researchers, and scientists in many different disciplines.
This series consists of three workshops. For individuals who are new to Python, coding, or data analysis, it is highly recommended that the workshops be attended in sequential order. Additionally, while these workshops are taught exclusively using code (i.e. there are no point-and-click methods), attendees do not need to have any prior experience with programming, coding, or scripting. All are welcome.
Skill Requirements: None.
Software Requirements for Hands-on Participation:
This video-only recorded workshop provides a short, high-level overview of Google Colab and how it relates to the other Python workshops. There is no live version of this workshop; it is solely available as a recorded version on our recorded workshops page linked above.
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Get a GSU Data Ready! Badge Micro-Credential for completing these workshops to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
This applied Machine Learning (ML) series introduces participants to the fundamentals of supervised learning and provides experience in applying several ML algorithms in Python. Participants will gain experience in regression modeling; assessing model adequacy, prediction precision, and computational performance; and learn several tools for visualizing each step of the process.
This series consists of three (3) workshops. For individuals who are new to Python and/or Google Colab, it is highly recommended that you first complete the prerequisite Python & Data Workshop Series 0-3 workshops. For those who are new to Machine Learning, it is highly recommended that the workshops in this series be attended in sequential order. While these workshops are taught exclusively using code (i.e., there are no point-and-click methods), attendees do not need to have any prior experience with programming, coding, or scripting. All are welcome.
Software Requirements for Hands-on Participation:
For participants wishing to follow along with the “hands-on” portion of the workshop, please see the directions here.
Fundamentals of supervised learning in Python; applying a rudimentary ML model using univariate linear regression (i.e., one feature).
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Prerequisites: Python & Data Workshop Series 0-3: https://lib.gsu.edu/rds-recordings
Fundamentals of supervised learning in Python; applying an ML model using multivariate regression (i.e., multiple features).
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Prerequisites: Python & Data Workshop Series 0-3: https://lib.gsu.edu/rds-recordings and Python for Machine Learning (ML) 1: Univariate Linear Regression Workshop.
Fundamentals of supervised learning in Python; applying an ML model using logistic regression (e.g., classification prediction).
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Prerequisites: Python & Data Workshop Series 0-3: https://lib.gsu.edu/rds-recordings, Python for Machine Learning (ML) 1: Univariate Linear Regression Workshop, and Python for Machine Learning (ML) 2: Multivariate Linear Regression Workshop.
The size and volume of textual data available to academic researchers is absolutely immense. Consequently, for some researchers, having the skills to process, transform, and analyze text data using computational tools is increasingly necessary for certain types of research. This workshop will introduce the fundamentals of working with and manipulating text data using scripting languages (e.g. Python, R). This includes loading, processing, and preparing text data for use with quantitative models. Although advanced natural language processing (NLP) models are not included in this workshop, some possible applications may be demonstrated if time permits. No special background knowledge or skills are required to attend. All are welcome.
Skill Requirements: Basic familiarity with Python, R, or any scripting language preferred.
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Get a GSU Data Ready! Badge Micro-Credential for completing these workshops to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
The SPSS 1 and SPSS 2 workshops in this two-part series focus on using the point-and-click method for using SPSS; the syntax/code method is introduced briefly.
This workshop is the first of a two-part series on SPSS, a statistical software package that is widely used by scientists throughout the social sciences for analysis of quantitative data.
Please note: This workshop focuses on using the point-and-click method for using SPSS; the syntax/code method is introduced briefly.
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Prerequisites: None.
This workshop is the second of a two-part series on SPSS, a statistical software package that is widely used by scientists throughout the social sciences for analysis of quantitative data.
Please note: This workshop focuses on using the point-and-click method for using SPSS; the syntax/code method is introduced briefly.
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Prerequisites: Attendance at SPSS 1 preferred, or completion of Parts 1-6 of the Lynda.com "SPSS Statistics Essential Training" tutorial.
Get a GSU Data Ready! Badge Micro-Credential for completing these workshops to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
This workshop series completes all analysis using code. No previous knowledge of coding is required. This series is for the Windows version of SAS.
This is the first SAS workshop in a two-part series. This interactive workshop will introduce users to the SAS system. Applied, hands on, examples using real data will be used. NOTE: This workshop is aimed at people who do not have experience using the SAS system. Those who have used SAS in the past may find this workshop too foundational and are encouraged to attend our forthcoming advanced SAS sessions.
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Prerequisites: No prior experience with SAS is required. Basic understanding of univariate and bivariate statistics is helpful but not required.
This is the second SAS workshop in a two-part series. In this interactive workshop, SAS users will go beyond the basics to develop comfort with more advanced statistical analyses using the SAS system. Applied, hands on, examples using real data will be used. NOTE: Basic knowledge of the SAS system will be helpful for those who want to participate in the applied portion of the workshop.
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Prerequisites: Basic knowledge of the SAS system will be helpful for those who want to participate in the applied portion of the workshop. Basic understanding of bivariate and multivariable statistics is helpful.
Get a GSU Data Ready! Badge Micro-Credential for completing these workshops to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
This workshop series completes all analysis using code. No previous knowledge of coding is required. This series is for the Windows version of Stata. See the Stata research guide here.
This workshop is the first of a three-part series on Stata. Stata is a statistical software package. Stata is widely used by scientists throughout the social sciences for analysis of quantitative data ranging from simple descriptive analysis to complex statistical modeling.
Please note: This workshop completes all analysis using code. No previous knowledge of coding is required.
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Prerequisites: None.
This workshop is the second in a three-part series on Stata. Stata is a statistical software package. Stata is widely used by scientists throughout the social sciences for analysis of quantitative data ranging from simple descriptive analysis to complex statistical modeling.
Please note: This workshop completes all analysis using code. No previous knowledge of coding is required.
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Prerequisites: Stata 1 or basic knowledge of Stata.
This workshop is the third in a three-part series on Stata. Stata is a statistical software package. Stata is widely used by scientists throughout the social sciences for analysis of quantitative data ranging from simple descriptive analysis to complex statistical modeling.
Please note: This workshop completes all analysis using code. No previous knowledge of coding is required.
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Prerequisites: Stata 1 and Stata 2 or moderate knowledge of Stata.
Get a GSU Data Ready! Badge Micro-Credential for completing these workshops to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
This workshop is for the *WINDOWS* version of NVivo. The Mac version differs significantly from the Windows; consequently, attending the Windows workshop if you will be using the Mac version is not recommended. We do not offer workshops on the Mac version due to having no Mac labs to do so. If you need one-on-one training for the Mac version of NVivo, please directly contact Mandy Swygart-Hobaugh, Ph.D. See the NVivo research guide here.
This is the first workshop in a two-part series on NVivo qualitative data analysis software.
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Prerequisites: Basic understanding of qualitative research methods is suggested, but not required. Watching this short tutorial on coding qualitative data before attending the workshop is recommended.
This is the second workshop in a two-part series on NVivo qualitative data analysis software.
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Prerequisites: NVivo 1
Get a GSU Data Ready! Badge Micro-Credential for completing this workshop plus the NVivo 1 workshop to show others your commitment to learning data skills! Learn more at lib.gsu.edu/data-ready
This workshop is for the *WINDOWS* version of NVivo. The Mac version differs significantly from the Windows; consequently, attending the Windows workshop if you will be using the Mac version is not recommended. We do not offer workshops on the Mac version due to having no Mac labs to do so. If you need one-on-one training for the Mac version of NVivo, please directly contact Mandy Swygart-Hobaugh, Ph.D. See the NVivo research guide here.
This workshop is on using NVivo qualitative data analysis software to do team work / team coding in NVivo involving 2 or more people. It will cover various strategies for tracking team members’ work in NVivo and comparing coding, including generating inter-rater reliability measures.
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Prerequisites: Attendance at minimally the NVivo 1 workshop (live or recorded) and preferably the NVivo 2 workshop (live or recorded).