Class Type: Molecular Biology

This is part 1 of a four-part Python Bootcamp (August 6 to 9, 2024).

Description: Join us for our premiere Python Bootcamp Week, offering a comprehensive dive into Python programming. Mirroring the approach of its R counterpart, the bootcamp unfolds through a sequence of dedicated sessions that encompass the full data analysis workflow. This includes importing and cleaning data, moving on to complex data manipulation, and employing advanced visualization techniques with the pandas and seaborn libraries. Each day's workshop builds on the last, ensuring a comprehensive understanding and practical application of Python for data analysis.

Day 1: In "Introduction to Python, Anaconda Navigator, and Jupyter Notebook," you'll familiarize yourself with the Anaconda Navigator interface and learn to create integrated Jupyter notebooks.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning Python programming for data importation, exploration, cleaning, and visualization.

This is part 2 of a four-part Python Bootcamp (August 6 to 9, 2024).

Description: Join us for our premiere Python Bootcamp Week, offering a comprehensive dive into Python programming. Mirroring the approach of its R counterpart, the bootcamp unfolds through a sequence of dedicated sessions that encompass the full data analysis workflow. This includes importing and cleaning data, moving on to complex data manipulation, and employing advanced visualization techniques with the pandas and seaborn libraries. Each day's workshop builds on the last, ensuring a comprehensive understanding and practical application of Python for data analysis.

Day 2: "Data Centric Python with pandas" delves into Python's diverse data structures and unveils the analytical might of the pandas library, enhancing your data analysis skills.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning Python programming for data importation, exploration, cleaning, and visualization.

This is part 3 of a four-part Python Bootcamp (August 6 to 9, 2024).

Description: Join us for our premiere Python Bootcamp Week, offering a comprehensive dive into Python programming. Mirroring the approach of its R counterpart, the bootcamp unfolds through a sequence of dedicated sessions that encompass the full data analysis workflow. This includes importing and cleaning data, moving on to complex data manipulation, and employing advanced visualization techniques with the pandas and seaborn libraries. Each day's workshop builds on the last, ensuring a comprehensive understanding and practical application of Python for data analysis.

Day 3: Advance your skills in "Data Exploration with pandas and Seaborn," focusing on data summarization and the art of visualization with Seaborn.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning Python programming for data importation, exploration, cleaning, and visualization.

This is part 4 of a four-part Python Bootcamp (August 6 to 9, 2024).

Description: Join us for our premiere Python Bootcamp Week, offering a comprehensive dive into Python programming. Mirroring the approach of its R counterpart, the bootcamp unfolds through a sequence of dedicated sessions that encompass the full data analysis workflow. This includes importing and cleaning data, moving on to complex data manipulation, and employing advanced visualization techniques with the pandas and seaborn libraries. Each day's workshop builds on the last, ensuring a comprehensive understanding and practical application of Python for data analysis.

Day 4: Complete your bootcamp with "Data Wrangling with pandas," mastering sophisticated data manipulation and cleaning techniques to tackle more complex data challenges.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning Python programming for data importation, exploration, cleaning, and visualization.

This is part 1 of a four-part R Bootcamp  (June 11 to 14, 2024).

Description: Participate in our very first R Bootcamp Week for a deep dive into R programming. The bootcamp unfolds through a series of focused sessions that cover the entire data analysis process, from importing and cleaning data to sophisticated data manipulation and visualization techniques using the tidyverse package. Each workshop builds on the last, ensuring a comprehensive understanding and practical application of R for data analysis.

Day 1: In "Introduction to R, RStudio, and Quarto Markdown," you'll familiarize yourself with the RStudio interface and learn to create integrated Quarto Markdown documents.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning R programming for data importation, exploration, cleaning, and visualization.

Prerequisites: None 

Overall learning objectives for the series: 

By the end of this workshop series, learners should be able to: 

• Use the RStudio Server to create Quarto Markdown documents that combine R code with output and text 

• Use R to import and examine data 

• Produce numerical and graphical summaries of a data set and its variables 

• Perform basic data cleaning and management with R 

Day 1: Introduction to R, RStudio, and Quarto Markdown 

• Navigate the R/RStudio interface 

• Describe basic components of R code 

• Describe the basic functionality of Quarto Markdown 

• Use RStudio to create a new Quarto Markdown document 

Day 2: Data Centric R with tidyverse 

• Describe various data types in R 

• Use functions to examine a data set 

• Import data sets from various sources 

• Export data to flat files 

Day 3: Data Exploration in R with tidyverse 

• Use functions to create numerical summaries of data 

• Describe the “grammar” of graphics in ggplot2 

• Create visualizations of data of various types 

Day 4: Data Wrangling in R with tidyverse 

• Filter and subset data by rows or columns 

• Create new variables 

• Transform data from long to wide and from wide to long 

• Identify and appropriately handle missing values 

• Merge data sets 

Each workshop builds on the last, ensuring a comprehensive understanding and practical application of R for data analysis. 

This is part 2 of a four-part R Bootcamp (June 11 to 14, 2024).

Description: Participate in our very first R Bootcamp Week for a deep dive into R programming. The bootcamp unfolds through a series of focused sessions that cover the entire data analysis process, from importing and cleaning data to sophisticated data manipulation and visualization techniques using the tidyverse package. Each workshop builds on the last, ensuring a comprehensive understanding and practical application of R for data analysis.

Day 2: "Data Centric R with tidyverse" offers an exploration into R's data types and the analytical prowess of the tidyverse package, broadening your data analysis capabilities.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning R programming for data importation, exploration, cleaning, and visualization.

This is part 3 of a four-part R Bootcamp (June 11 to 14, 2024).

Description: Participate in our very first R Bootcamp Week for a deep dive into R programming. The bootcamp unfolds through a series of focused sessions that cover the entire data analysis process, from importing and cleaning data to sophisticated data manipulation and visualization techniques using the tidyverse package. Each workshop builds on the last, ensuring a comprehensive understanding and practical application of R for data analysis.

Day 3: Advance your skills in "Data Exploration in R with tidyverse," focusing on data summarization and the art of visualization with ggplot2.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning R programming for data importation, exploration, cleaning, and visualization.

This is part 4 of a four-part R Bootcamp (June 11 to 14,2024)

Description: Participate in our very first R Bootcamp Week for a deep dive into R programming. The bootcamp unfolds through a series of focused sessions that cover the entire data analysis process, from importing and cleaning data to sophisticated data manipulation and visualization techniques using the tidyverse package. Each workshop builds on the last, ensuring a comprehensive understanding and practical application of R for data analysis.

Day 4: Complete your Bootcamp with "Data Wrangling in R with tidyverse," mastering sophisticated data manipulation and cleaning techniques to tackle more complex data challenges.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning R programming for data importation, exploration, cleaning, and visualization.

The workshop is dedicated to exploring the intricate biology concealed within lists of differentially expressed genes. Participants will delve into an array of publicly accessible pathway enrichment analysis resources, such as the Gene Ontology (GO), Molecular Signature Database (MsigDB), Reactome, KEGG, and WikiPathways. Utilizing tools like GSEA and g:Profiler, the session will guide attendees in mining these databases effectively. Additionally, the workshop will provide comprehensive instruction on creating enrichment maps from GSEA and g:Profiler results using Cytoscape, thereby enhancing the participants' ability to visualize and interpret complex biological data.

In this workshop, we will focus on the PCSK9 gene and its connection to LDL levels for participants in the All of Us database. 

Using Electronic Health Record (EHR) data and provided genomic information, we will showcase a comprehensive approach that encompasses data cleaning and filtering to isolate specific genomic regions of interest. In addition to these preparatory steps, we will conduct a logistic regression analysis, utilizing disease status as the outcome variable and a single genetic variant as the predictor. These analytical skills serve as a solid foundation for future genome-wide association studies (GWAS) and provide a crucial resource for researchers. 

Target Audience: Researchers interested in the application of statistical methods to biological, genetic, and health-related data.