Winter 2025 All Workshops Survey Responses

Number of responses

Code 
library(tidyverse)
library(bslib)
library(shiny)
library(bsicons)
source("scripts/helper_functions.R")

# list of workshop IDs to filter results
workshops <- c("2025-02-25-ucsb-ml", "2025-02-18-ucsb-intermediater", "2025-02-03-ucsb-computing", "2025-01-23-ucsb-geospatial", "2025-01-15-ucsb-containers", "2025-01-14-ucsb-python")

results <- read_csv("data-joined/all_workshops.csv") %>% 
  filter(workshop %in% workshops)
  
# Fix comma separator
results <- results %>% 
  mutate(findout_select.pre = str_replace_all(
  findout_select.pre, 
  "Twitter, Facebook, etc.", 
  "Twitter; Facebook; etc."))

pre_survey <- results %>%
  select(ends_with(".pre"))

post_survey <- results %>%
  select(ends_with(".post"))

n_pre <- sum(apply(post_survey, 1, function(row) all(is.na(row))))
n_post <- sum(apply(pre_survey, 1, function(row) all(is.na(row))))
n_total <- nrow(results)
n_both <- nrow(results) - n_pre - n_post

layout_columns(
  value_box(
    title = "Total responses", value = n_total, ,
    theme = NULL, showcase = bs_icon("people-fill"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Both pre- and post-", value = n_both, , theme = NULL,
    showcase = bs_icon("arrows-expand-vertical"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Only pre-workshop", value = n_pre, ,
    theme = NULL, showcase = bs_icon("arrow-left-short"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Only post-workshop", value = n_post, , theme = NULL,
    showcase = bs_icon("arrow-right-short"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  )
)

Total responses

126

Both pre- and post-

30

Only pre-workshop

77

Only post-workshop

19

Departments

Code 
depts <- results %>% select(dept_select.pre) %>% 
  separate_rows(dept_select.pre, sep=",") %>%
  mutate(dept_select.pre = str_trim(dept_select.pre)) %>%
  count(dept_select.pre, name = "count") %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(depts, aes(y=reorder(dept_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) +  
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(depts$count)*1.1))

“Other” Departments

Code 
other_depts <- results %>% 
  count(dept_other.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(other_depts, aes(y=reorder(dept_other.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(other_depts$count)*1.1))

Current occupation / Career stage

Code 
ocup <- results %>% select(occupation.pre) %>% 
  separate_rows(occupation.pre, sep=",") %>%
  mutate(occupation.pre = str_trim(occupation.pre)) %>%
  count(occupation.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(ocup, aes(y=reorder(occupation.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(ocup$count)*1.2))

Motivation - Why are you participating in this workshop?

Code 
motiv <- results %>% select(motivation_select.pre) %>% 
  separate_rows(motivation_select.pre, sep=",")  %>% 
  mutate(motivation_select.pre = str_trim(motivation_select.pre)) %>%
  count(motivation_select.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(motiv, aes(y=reorder(motivation_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(motiv$count)*1.2))

How did you find out about this workshop?

Code 
findw <- results %>% select(findout_select.pre) %>% 
  separate_rows(findout_select.pre, sep=",")  %>% 
  mutate(findout_select.pre = str_trim(findout_select.pre)) %>%
  count(findout_select.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(findw, aes(y=reorder(findout_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(findw$count)*1.2))

What you most hope to learn?

Code 
results %>% group_by(workshop) %>% 
  select(workshop, hopes.pre) %>% 
  drop_na()
workshop hopes.pre
2025-01-14-ucsb-python I hope to learn Python and apply it to my current research
2025-01-15-ucsb-containers I hope to learn how to use available tools to ensure that my workflows that I create are easily replicated on a coworker’s computer or even for me on a new computer. And I hope to learn how more about what it means to do this in a container-driven way!
2025-01-14-ucsb-python Gaining a foundation and guided introduction in using these tools in tandem (git/version control, SQL+python/R and geospatial data)
2025-01-14-ucsb-python I have a Microsoft Access database, and I wish to learn R and Python.
2025-01-14-ucsb-python I would like to sharpen my python skills and increase the application I have to data analysis
2025-01-14-ucsb-python Very basic proficiency in organizing and analyzing data in python. Ideally enough to build a simple automated pipeline for routine mass spectrometry data manipulation
2025-01-14-ucsb-python How to apply Python in library work assisting patrons/researchers directly
2025-01-14-ucsb-python How to organize and manipulate data with Python.
2025-01-14-ucsb-python To gain new skills and get exposure to this type of work.
2025-01-14-ucsb-python how to use python for my projects.
2025-01-14-ucsb-python Better understanding of data analysis
2025-01-14-ucsb-python how to analyze my data more efficiently and make complex graphs
2025-01-14-ucsb-python I hope to at least have an idea of how Python works and how to do very basic analysis that could help in a variety of fields.
2025-01-14-ucsb-python learn basics of python programming to use in my research
2025-01-14-ucsb-python I hope to learn adequate Python skills to help with job opportunities
2025-01-14-ucsb-python I hope to gain some familiarity and confidence with the Python programming system.
2025-01-14-ucsb-python If a career in data analytics would be right for me
2025-01-14-ucsb-python Preparation for using Python for remote sensing analyses.
2025-01-14-ucsb-python Learn how to apply python programming knowledge into data analysis.
2025-01-14-ucsb-python Familiarize myself with new technologies. I am a beginner. But I work with the IT Internship program as the program manager. I like to be familiar with the different concepts that the students work with. Thank you!
2025-01-14-ucsb-python General knowledge about Python!
2025-01-14-ucsb-python how to most efficiently set up data pipelines
2025-01-14-ucsb-python I hope to gain basic understanding about the skills and practice the practical skills so as to master them and be able to further polish them on my own
2025-01-23-ucsb-geospatial I’m interested in learning how to process, analyze, and visualize geospatial data from remote sensors. Ultimately, I’m trying to build skills toward effectively gathering and processing multiband sonar data of near-shore bathymetry for visualization, analysis, and modeling phenomenon.
2025-01-23-ucsb-geospatial Refresh skills in R and learn how to analyze spatial data
2025-01-23-ucsb-geospatial the R language and data management environments like SQL
2025-01-23-ucsb-geospatial Ability to handle high resolution spatial data within R
2025-01-23-ucsb-geospatial how to maneuver Geospatial data
2025-01-23-ucsb-geospatial I hope to become more confident in working with raster data. I am hoping that I can take what I learn during this workshop and make maps/figures using the information that I gain during the workshop.
2025-01-23-ucsb-geospatial more on handling raster spatial data
2025-01-23-ucsb-geospatial Confident in my skills :)
2025-01-23-ucsb-geospatial I hope to learn a project based system on how to do data analytics
2025-01-23-ucsb-geospatial To make maps in r without hating my data organization skills
2025-01-23-ucsb-geospatial I hope to learn at least some basic skills that can help with geospatial data
2025-01-23-ucsb-geospatial I hope to learn new skills and deepen my understanding of data structures and applications
2025-01-23-ucsb-geospatial I hope to better my understanding of R especially as it relates to geographical analysis
2025-01-23-ucsb-geospatial I hope to be able to use R when doing GIS
2025-01-15-ucsb-containers Better management of multiple containers in a project
2025-02-03-ucsb-computing new stuff, networking
2025-02-03-ucsb-computing I am somewhat aware of the computing resources that exist on campus, but I don’t think our research group is utilizing them as effectively as we could be, so I hope to learn some of the areas where we could improve
2025-02-03-ucsb-computing Available computing resources
2025-02-03-ucsb-computing info about the computations I run (nodes vs cores vs tasks, how to choose these), general data storage best practices
2025-02-03-ucsb-computing I have never used University-wide computing resources and would like to have a better familiarity of what’s out there in case I need it for future work. I also would like to brush up on fundamental concepts in computing.
2025-02-03-ucsb-computing How do you deal with hundreds of GB of data for processing, parallelizing, etc., and how can the university resources support this.
2025-02-03-ucsb-computing A sanity check that I sort of understand best practices
2025-02-03-ucsb-computing Ways to store and access large data bases at UCSB, with backup. Efficient command line processes to query+interface with these data bases; running processes with large data on UCSB clusters + other resources
2025-02-03-ucsb-computing How to use grit and other resources
2025-02-03-ucsb-computing Learning to get stronger computational resources.
2025-02-03-ucsb-computing Getting a better understanding of HPC and how it can help me improve my data processing workflows
2025-02-03-ucsb-computing I have an upcoming bioacoustic project that will generate multiple terabytes of data that I need to figure out how to manage. I am hoping that this workshop can help me figure out a workflow.
2025-02-03-ucsb-computing How to use supercomputer systems to process large datasets – I’d like to work through an example, prepping it for processing and understanding how to book/rent a supercomputer
2025-02-03-ucsb-computing How to store large amount of imaging data, analyze data extracted from images and make raw data and processed data available to users.
2025-02-03-ucsb-computing I hope to gain a better understanding on how to store and process data on my own devices > school/administrative computers and repositories. Can’t wait!!!
2025-02-03-ucsb-computing Learn about basic computing concepts, HPCs, what GPU/core hours are and how many I need
2025-02-03-ucsb-computing Access the servers to perform heavy computing
2025-02-18-ucsb-intermediater I want to increase my comfort with R coding language and skills.
2025-02-18-ucsb-intermediater Further advance my comprehension of R and how to leverage it for Data Analysis as it applies to Project Managment and Data Wrangling.
2025-02-18-ucsb-intermediater Advance my R skills and feel more comfortable processing statistical analysis using R
2025-02-18-ucsb-intermediater I hope to become more familiar with manipulating raw data in R (such as pulling out certain columns of data, averaging raw values and plotting means with standard errors, etc) and using new libraries for simple statistical analysis (ANOVA, regressions)
2025-02-18-ucsb-intermediater becoming proficient at For Loops
2025-02-18-ucsb-intermediater I’d like to learn how to better store and access the various files that I am working with and creating, especially when I am learning how to write the code and my script files are often filled with my trials and errors. I want to gain confidence to delete the stuff that didn’t work as intended
2025-02-18-ucsb-intermediater skills in R that will help me get a new job
2025-02-18-ucsb-intermediater To gain confidence with writing for loops for diverse scenarios
2025-02-25-ucsb-ml Main tools and techniques to do machine learning in Python.
2025-02-25-ucsb-ml How different ML paradigms compare for solving different problems, and when to use them
2025-02-25-ucsb-ml I would like to learn how to use machine learning with python
2025-02-25-ucsb-ml Better understanding of the best scenarios to use machine learning and how to best implement the analysis in R.
2025-02-25-ucsb-ml A general understanding/basic foundation in machine learning to later apply to hyperspectral remote sensing analyses to detect drought and heat stress in plants.
2025-02-25-ucsb-ml Some new machine learning concepts that I can take and make a project with
2025-02-25-ucsb-ml SciKitLearn’s API, parallelization techniques
2025-02-25-ucsb-ml I need to incorporate machine learning processes into my research analysis. I am hoping that this workshop will serve as a good introduction.
2025-02-25-ucsb-ml I’ve never actually implemented any ML and would like to know how to do that
2025-02-25-ucsb-ml fundamentals of machine learning in python
2025-02-25-ucsb-ml python
2025-02-25-ucsb-ml Efficient ways to train, fine-tune, and run models
2025-02-25-ucsb-ml use satellite images to train a model. Also, use this workshop as a gateway for my future projects.

Learning environment in the workshop

Code 
orderedq <- c("Strongly Disagree", "Somewhat Disagree", "Neither Agree or Disagree","Somewhat Agree", "Strongly Agree")
addNA(orderedq)
Code 
agree_questions <- results %>% 
  select(join_key, agree_apply.post,    agree_comfortable.post, agree_clearanswers.post,
         agree_instr_enthusiasm.post, agree_instr_interaction.post, agree_instr_knowledge.post
) %>% 
  filter(!if_all(-join_key, is.na))

n_agree_questions <- nrow(agree_questions)
  
agree_questions <- agree_questions %>%
  pivot_longer(cols = -join_key, names_to = "Question", values_to = "Response") %>% 
  mutate(Response = factor(Response, levels = orderedq),
         Question = recode(Question,
                     "agree_apply.post" = "Can immediatly apply 
 what they learned",
                     "agree_comfortable.post" = "Comfortable learning in 
 the workshop environment",
                     "agree_clearanswers.post" = "Got clear answers 
 from instructors",
                     "agree_instr_enthusiasm.post" = "Instructors were enthusiastic",
                     "agree_instr_interaction.post" = "Comfortable interacting 
 with instructors",
                     "agree_instr_knowledge.post" = "Instructors were knowledgeable 
 about the material"
      ))

summary_data <- agree_questions %>%
  count(Question, Response, name = "count") %>% 
  mutate(percent = (count / n_agree_questions) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(summary_data, aes(x = Question, y = count, fill = Response)) +
  geom_col(position = "fill", color = "black", show.legend = TRUE) +
  scale_y_continuous(labels = scales::percent_format()) + 
  scale_fill_manual(values = c("Strongly Disagree" = "#d01c8b", 
                               "Somewhat Disagree" = "#f1b6da", 
                               "Neither Agree or Disagree" = "#f7f7f7", 
                               "Somewhat Agree" = "#b8e186", 
                               "Strongly Agree" = "#4dac26"), 
                    na.translate = TRUE, na.value = "#cccccc", 
                    breaks = orderedq, drop = FALSE) +
  geom_text(aes(label = text), size = 3,
             position = position_fill(vjust = 0.5)) +
  labs(y = "# respondents (Percentage)", x = element_blank(), fill = "Responses",
       subtitle = paste0("Number of responses: ", n_agree_questions)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.subtitle = element_text(hjust = 0.5, size = 12))

How an instructor or helper affected your learning experience

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, instructor_example.post) %>%
  drop_na()
workshop instructor_example.post
2025-01-14-ucsb-python Using the blue and red stickies, and constantly reminding us to use it
2025-01-14-ucsb-python Was able to answer all of my questions and make the material more clear when I felt that it needed further explanation.
2025-01-14-ucsb-python If there was a moment when I had a hiccup or an error instructors helped clarify where an error occurred to help me get back on track
2025-01-14-ucsb-python When I got some problem with the challenge, they came immediately and were really willing to help me solving the problem.
2025-01-23-ucsb-geospatial For somebody coming outside of the field of geospatial science, the instructors were helpful in communicating and relating geographic metadata and other concepts.
2025-01-23-ucsb-geospatial Sigrid was great at addressing the comments we made on the purple and yellow stickies!
2025-01-23-ucsb-geospatial Helped me whenever I put up the colored sticky note
2025-01-23-ucsb-geospatial I ran into small issues with my code as a result of typing errors. Instructors were very helpful at identifying these errors and providing helpful tips along the way to avoid errors in the future
2025-01-23-ucsb-geospatial Explaining commands
2025-01-23-ucsb-geospatial They were extremely helpful and created a lighthearted learning environment. It was clear that they respect each other as well as the students. They did a fantastic job.
2025-01-15-ucsb-containers Ronald was enthusiastic and clear
2025-02-03-ucsb-computing I don’t think it was one particular instructor that helped the experience, but rather that there were people there to represent the many different campus resources.
2025-02-03-ucsb-computing Provided concrete examples of how to use some of the resources
2025-02-03-ucsb-computing instructors were open to questions at any point during the seminar
2025-02-03-ucsb-computing I think the instructors gave a good job of providing examples of when an individual may need to switch between the different types of computing systems and conceptualized what that would entail in a way that was accessible. I feel more confident in my understanding of research computing and the situations or instances when this resources could be beneficial. More importantly, I am aware of where to go on campus to ask questions and gain access to these resources.
2025-02-18-ucsb-intermediater The instructors did a good job of slowly going through the code and providing a few tips that novices can use.
2025-02-18-ucsb-intermediater The instructors were so familiar with the material that it was hard for them to step outside the mindset of a programmer and see how someone without full training in coding might need additional steps
2025-02-18-ucsb-intermediater I gained more familiarity with the structure of for loops and the instances when a function may be better than a for loop and when using functions in dplyr may actually achieve coding goals quicker than a for loop.
2025-02-25-ucsb-ml They were really friendly and enthusiastic.
2025-02-25-ucsb-ml i asked one question and they confirmed my thoughts
2025-02-25-ucsb-ml I wasn’t affraid to let instructors know if I was stuck on a step. Some of the instructors who weren’t directly presenting were still able to provide helpful input on the potential applications of workshop materials to additional fields of research.
2025-02-25-ucsb-ml One of the instructors provided a clear and concise explanation of a complex machine learning concept by breaking it down into smaller steps. For example, when covering decision trees, they used a real-world analogy related to everyday decision-making, which made it much easier to understand. Additionally, a helper guided me through debugging a coding error, ensuring I grasped the logic behind the solution rather than just fixing the issue. Their support significantly enhanced my learning experience.
2025-01-14-ucsb-python Any question I had was immediately answered and with enthusiasm, Jean especially was a very enthusiastic and helpful person!
2025-01-14-ucsb-python No, I did not encounter any accessibility issues. Everything was great, and there were no barriers to my participation.”
2025-01-14-ucsb-python I am a very slow learner (new to Python; and any language for that matter), Sigrid and Jose were very patient with me. If the material sped along too quickly, they were here to offer support to get me back-on-track.
2025-01-23-ucsb-geospatial hands on help(esp when things went wrong->stopped me from panicing and shutting down in frustration)
2025-01-23-ucsb-geospatial They were all super attentitve and fast in providing help
2025-01-23-ucsb-geospatial Great! Helping me determine what coding issues I was encountering rapidly.
2025-01-23-ucsb-geospatial Plenty of people to offer one on one help so it was a very supportive and comfortable working environment that moved at a good slow pace for everyone too follow along. If I ever had trouble, I could put up a yellow sticky note and someone would come work with me to figure out my problem or catch up if I got behind.
2025-01-23-ucsb-geospatial I didn’t show up the second to last day but when I came in the final day, one of the instructors got me all caught up on the code I needed which was super helpful
2025-02-03-ucsb-computing Clear slides and familiarity with the topic
2025-02-03-ucsb-computing Very engaging presentations by nice presenters!
2025-02-03-ucsb-computing answered my specific questions
2025-01-15-ucsb-containers There were a couple of moments where the code was not working, and they found the solution every time.
2025-02-18-ucsb-intermediater one on one time and super friendly
2025-02-18-ucsb-intermediater They had clear and concise lessons and had an effective system for ensuring everyone was keeping up with the material.

Skills and perception comparison

Code 
# Calculate mean scores and make graph for all respondents (only_matched=FALSE)
tryCatch(
  {
mean_nresp <- get_mean_scores_nresp(results, only_matched=FALSE)
graph_pre_post(mean_nresp$mean_scores, mean_nresp$n_resp_pre, mean_nresp$n_resp_post, mean_nresp$n_resp_pre_post, only_matched=FALSE)
},
error = function(cond) {
message("Could not do the plots as there are no pre or post results to show")
}
)

Code 
# Calculate mean scores and make graph for only matched respondents in pre and post (only_matched=TRUE)
tryCatch(
  {
mean_nresp <- get_mean_scores_nresp(results, only_matched=TRUE)
graph_pre_post(mean_nresp$mean_scores, mean_nresp$n_resp_pre, mean_nresp$n_resp_post, mean_nresp$n_resp_pre_post, only_matched=TRUE)
},
error = function(cond) {
message("Could not do the plots as there are no pre or post results to show")
}
)

Workshop Strengths

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, workshop_strengths.post) %>% 
  drop_na()
workshop workshop_strengths.post
2025-01-14-ucsb-python It’s a comfortable pace, and the instructors are very helpful.
2025-01-14-ucsb-python Joins, plots, importing, exporting, shortcuts
2025-01-14-ucsb-python clear and easy to follow examples
2025-01-14-ucsb-python I really appreciated the challenges. I feel like this was the strongest strength of the workshop and helped give participants time to practice skills presented during the workshop.
2025-01-23-ucsb-geospatial Organization, pacing.
2025-01-23-ucsb-geospatial A lot of support throughout the workshop with the lecture and helpers
2025-01-23-ucsb-geospatial Real-time communication with sticky note colors
2025-01-23-ucsb-geospatial applicability
2025-01-23-ucsb-geospatial Loved the sticky note system, it is much easier to stick a yellow note on your laptop than to interrupt and raise a hand. Loved the visual elements and direct applications during the workshop.
2025-01-15-ucsb-containers The written instructions paired with the walk through was helpful
2025-01-15-ucsb-containers Covers a lot of material, very thorough and easy to apply
2025-02-03-ucsb-computing I think the workshop did a good job of introduction to all of the campus resources.
2025-02-03-ucsb-computing Synthesizing information. Providing examples. Starting from the basics.
2025-02-03-ucsb-computing great overview of campus resources
2025-02-03-ucsb-computing I think the workshop was paced well and each “part” got adequate time to explain and step through.
2025-02-18-ucsb-intermediater The opportunity to see R in action and think through how to solve problems. There was help when you needed it.
2025-02-18-ucsb-intermediater friendly, responsive to questions
2025-02-18-ucsb-intermediater I liked that the workshop alternated instructors to provide variety in the way information was shared and taught. I appreciated that there were challenges and also a short break during the workshop.
2025-02-25-ucsb-ml The instructors take the time to manually write the code and explain it line by line.
2025-02-25-ucsb-ml The hands-on learning was useful
2025-02-25-ucsb-ml opening picture of what ML is and setup for environment and hand holding through typing
2025-02-25-ucsb-ml Good continuation of the intro to data management in Python workshop. Gave a general/basic overview of machine learning. Kept things simple to facilitate a broad audience with varying experience in related topics.
2025-02-25-ucsb-ml Well-Structured Content – The workshop followed a clear and logical progression, making it easy to build upon foundational concepts.; Engaging and Supportive Instructors – The instructors and helpers were knowledgeable, patient, and responsive to questions.; Hands-On Learning Approach – Practical coding exercises reinforced key machine learning concepts, allowing participants to apply what they learned immediately.; Accessible and Well-Prepared Materials – The provided resources, including notebooks and example datasets, were well-organized and easy to follow.; Encouraging Environment – The collaborative and inclusive atmosphere fostered engagement and active participation.
2025-01-14-ucsb-python Covers a wide range of python functions and usage
2025-01-14-ucsb-python nothing
2025-01-14-ucsb-python Computer literacy! A safe space for learning something new! Expanding my understanding of terminology used regularly in my work setting! Highly recommend to others (even if not planning to use regularly it’s really great to have a better understanding of programming).
2025-01-23-ucsb-geospatial i loved it all tbh
2025-01-23-ucsb-geospatial helping us navigate step-by-step with the functions and building up from every episode. the way it was organized throughout 4 days really helped. Perhaps it could be spread out into more days even.
2025-01-23-ucsb-geospatial Instructors & Pacing
2025-01-23-ucsb-geospatial Lots of staff to help troubleshoot and interesting data. Also explored a variety of data types
2025-01-23-ucsb-geospatial This workshop really helped me with ggplot. I know so much now
2025-01-15-ucsb-containers clear document to follow
2025-02-03-ucsb-computing Exposure to the levels of computing available at UCSB
2025-02-03-ucsb-computing It felt approachable and general enough for all of the different backgrounds that the attendees had. Listed resources that folks could go to for additional help.
2025-01-15-ucsb-containers The material to follow the workshop.
2025-02-18-ucsb-intermediater just glad you have them. keep em coming
2025-02-18-ucsb-intermediater Simple yet effective lessons, used real world data with practical applications as a basis for example.

Ways to improve the workshop

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, workshop_improved.post) %>% 
  drop_na()
workshop workshop_improved.post
2025-01-14-ucsb-python More lessons, maybe 5 classes instead of 3 classes.
2025-01-14-ucsb-python Being able to access this code after the workshop or being able to download everything for your own computer and work.
2025-01-14-ucsb-python more explanation of how the functions work and why you need to include certain syntax
2025-01-14-ucsb-python I think one more workshop day could be included that goes through a flow through with the data that combines all the skills use so kinda like a problem set.
2025-01-23-ucsb-geospatial More information about data sets and information being visualized, and information about data collection techniques.
2025-01-23-ucsb-geospatial Talk more about the data we are working with, what all of the standards mean, what plot are we actually making?
2025-01-23-ucsb-geospatial I think more could be done to contextualize the data and explain the options of all of the function options. For example, in some ggplots, we added titles and subtitles, but I would appreciate a deeper explanation of all of the options, perhaps by making one very fancy graph
2025-01-23-ucsb-geospatial More in-depth explanations of what is going on behind the scenes or detailed descriptions of rasters etc.
2025-01-15-ucsb-containers Maybe one more little break
2025-02-03-ucsb-computing I think the workshop did not do enough to demystify some of the work that goes into a user using one of these campus resources.
2025-02-03-ucsb-computing A lot of information was given, but I feel that focusing on one specific resource and navigating it could have been more practical.
2025-02-03-ucsb-computing The last part of the workshop had a lot of things written in the slides, I don’t think it was that clear what we should take away from it.
2025-02-03-ucsb-computing I wanted to go through the workflow of using supercomputers with an actual example of how to write code and submit a job to a cluster and see how the results were outputted
2025-02-03-ucsb-computing Examples of past research projects that used these resources could add more context for each computing resource.
2025-02-18-ucsb-intermediater I have a lot of experience with data analysis (in another coding language), so I was able to follow pretty closely what was going on. I felt like others might not have had as much experience, so they were lost as to what the goal of some of the commands were. It might be good to think carefully about where some people might not know what the goal of a command is. Perhaps you expect people to come in with that knowledge, which is fine, but it was the sense I got from some of the questions that were asked.
2025-02-18-ucsb-intermediater if an instructor was coming from a non-coding background they might be able to present the material in a different way. Also it was hard for me to think about immediate applications for my data project. I would like to come prepared with my own data and write code that is pertinent, as i had a hard time making the leap of how I would apply it easily
2025-02-18-ucsb-intermediater I think something that could be useful for folks to walk away with are more examples utilizing for loops, functions and if and else statements. So maybe, creating like a part 2 for the workshop to get into more details and examples.
2025-02-25-ucsb-ml The material itself could be improved. It would be helpful to go through a real life example from beginning to end without over simplifying the problem.
2025-02-25-ucsb-ml I wish there was more about using ML for things other than handling large data sets. For example, as an engineer, I’m most interested in ML for design optimization. I know there’s lots of different implementations of this out there, so a review of the strengths and weaknesses of different ML paradigms would be useful
2025-02-25-ucsb-ml pacing felt weird? didn’t learn that much conceptual stuff or when we did it was very slow? Then typing I guess is not slow it’s fine, but sometimes the way we talked about the typing was slow, or rather inefficient/not fluid teaching wise. Like somehow more awkward than it should have been?
2025-02-25-ucsb-ml I would have like to dive a little deeper into additional examples of machine learning. Perhaps using an additional dataset, or at least exploring the use of multiple predictor values rather than only the appache score. Some more basic code was overexplained, while the arguments of more advanced methods/functions related to the sci-kit library were kind of glossed over/rushed. Less time could be spent answering questions/comments from participants that weren’t really in the scope of what we’re learning. EX: spending 5-10 minutes to talk about whether certain predictor variables in the dataset were statistically sound to include because they are correlated with/calculated based on other predictor variables (we didn’t even end up using the variable of question and that participant didn’t even attend the second day).
2025-02-25-ucsb-ml 1. Pacing Adjustments – Some sections felt a bit fast-paced, especially for beginners. Providing additional time for complex topics could enhance understanding.; 2. More Real-World Case Studies – Including more practical, real-world examples of machine learning applications would help connect concepts to industry use cases.; 3. Pre-Workshop Preparation Materials – Offering optional pre-workshop readings or short tutorials could help participants with varying backgrounds start on the same level.; 4. More Breaks – Shorter but more frequent breaks could improve focus and prevent fatigue, especially during intensive coding sessions.; 5. Advanced Follow-Up Sessions – A follow-up session or additional resources for those who want to go beyond the basics would be beneficial.
2025-01-14-ucsb-python N/A
2025-01-14-ucsb-python I found the workshop to be very informative and engaging. However, one potential improvement could be providing more time for Q&A or group discussions to allow for deeper exploration of the topics. Additionally, having more interactive activities could enhance participant engagement
2025-01-14-ucsb-python N/A
2025-01-23-ucsb-geospatial more days:)
2025-01-23-ucsb-geospatial I would consider adding one more day, the last day felt a bit rushed in the last part. Explain the functions in more details, for instance, why we are using a function in a certain line – sometimes this was said, but not often.
2025-01-23-ucsb-geospatial More checkpoints through out the workshop
2025-01-23-ucsb-geospatial Explain certain acronyms or functions a little better when we use them even if they were explained once a few days ago
2025-01-23-ucsb-geospatial I think we should copy and paste code more often because it was a bit tedious especially with all the ggplot setup
2025-01-15-ucsb-containers a bit long, but seems necessary to be this long
2025-02-03-ucsb-computing On of the speakers spoke fast and was harder to follow and some words were unclear, I would say speak slower and distinctly.
2025-02-03-ucsb-computing Maybe splitting up into groups for Q&A session for folks that had questions about CSC, HPC, data storage, etc.
2025-01-15-ucsb-containers More applied examples of how to work in a project with big data sets and share the projects.
2025-02-18-ucsb-intermediater more of them, evenings might also be good
2025-02-18-ucsb-intermediater I understand the focus was on developing coding skills, but I hope in future lessons we’re able to follow through on the example to see the end result of the practical application for the skills we’ve learned.

How likely are you to recommend this workshop? Scale 0 - 10

Code 
orderedq <- c("Detractor", "Passive", "Promoter")

nps <- results %>% 
  count(recommend_group.post, recommende_score.post, name = "count") %>% 
  drop_na() %>% 
  mutate(recommend_group.post = factor(recommend_group.post, levels = orderedq),
         percent = (count/sum(count)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

nps %>% 
ggplot(aes(x=recommende_score.post, y=count, fill=recommend_group.post)) +
  geom_col(color="black", show.legend = TRUE) +
  scale_fill_manual(values = c("Detractor" = "#af8dc3", "Passive" = "#f7f7f7", "Promoter" = "#7fbf7b"), breaks = c("Detractor", "Passive", "Promoter"), drop = FALSE) +
  geom_label(aes(label = text, vjust = -0.5), fill = "white", size= 3) +
  scale_x_continuous(breaks = 1:10) +
  labs(x = "NPS Score", y = "# respondents", subtitle = paste0("Number of responses: ", sum(nps$count), "
 Mean score: ", format(weighted.mean(nps$recommende_score.post, nps$count), digits = 3))) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    plot.subtitle = element_text(hjust = 0.5, size = 12)
  ) +
  expand_limits(x = c(1,10),
                y = c(0, max(nps$count)*1.1))

Topic Suggestions

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, suggest_topics.post) %>% 
  drop_na()
workshop suggest_topics.post
2025-01-14-ucsb-python Geospatial R one will be good
2025-01-14-ucsb-python more about plotting would be helpful for me
2025-01-14-ucsb-python N/A
2025-01-23-ucsb-geospatial No, thank you!
2025-01-23-ucsb-geospatial Git and GitHub version control as well as working in iOS, windows, and Linus environments
2025-01-23-ucsb-geospatial Would love a more career focused workshop that goes over skills that are used in tech/business industry
2025-02-03-ucsb-computing I feel like it might be helpful to have something like an office hours for these types of computational resources where people can get help with specific issues. I don’t exactly know how this would work in practice.
2025-02-03-ucsb-computing More hands-on the doing. More practical on how to use a specific resource. It could be a series of workshops to make people familiar with a resource that helps their needs (after this first workshop that was introductory and showing all the different resources).
2025-02-03-ucsb-computing Introduction to python
2025-02-03-ucsb-computing show step by step how to use the servers
2025-02-03-ucsb-computing workshop where we actually go through an example of submitting a job to a supercomputer
2025-02-03-ucsb-computing Bayesian modeling in R
2025-02-18-ucsb-intermediater Any useful and potentially complex commands could be good to focus on. I thought that this was well-paced and didn’t try to do too much. I found it helpful.
2025-02-18-ucsb-intermediater programming for non-programmers? :)
2025-02-25-ucsb-ml neural networks
2025-02-25-ucsb-ml Hyperspectral data processing and related machine learning would be awesome!
2025-02-25-ucsb-ml 1. Advanced Machine Learning Techniques – A deeper dive into topics like deep learning, neural networks, or ensemble methods.; 2. Data Preprocessing and Feature Engineering – Best practices for handling missing data, scaling, encoding, and selecting important features.; 3. Explainable AI and Model Interpretability – Understanding how machine learning models make decisions using SHAP, LIME, and other interpretability techniques.; 4. Time Series Analysis with Machine Learning – Practical applications for forecasting and analyzing trends in time-dependent data.; 5. Reinforcement Learning Basics – An introduction to reinforcement learning concepts with hands-on coding exercises.; 6. Machine Learning for Environmental and Climate Science – Applying ML techniques to real-world problems related to sustainability, water resources, and climate change.; 7. Automating Machine Learning with AutoML – Exploring tools and techniques for automating model selection, hyperparameter tuning, and feature engineering.
2025-01-14-ucsb-python Maybe a workshop on how to get into careers with Python? I’d love to be more involved and work on this myself
2025-01-14-ucsb-python case studies
2025-01-14-ucsb-python Power BI
2025-01-23-ucsb-geospatial keep the env data vibes
2025-01-23-ucsb-geospatial GIS/ Remote Sensing
2025-01-23-ucsb-geospatial Utilizing Kaggle Data
2025-01-23-ucsb-geospatial Intermediate Python Data Analysis
2025-01-23-ucsb-geospatial None right now
2025-01-15-ucsb-containers dealing with large dataset in python (e.g. using xarray)
2025-02-03-ucsb-computing Storage was touched on at this session, and use, access, and management of storage would be helpful. I know there are better naming conventions and I am concerned about the managment of duplicate files.
2025-02-03-ucsb-computing How SLURM works, how to choose nodes/tasks/cores/etc. on campus computing resources
2025-01-15-ucsb-containers More workshops.
2025-02-18-ucsb-intermediater none at this time, thank you
2025-02-18-ucsb-intermediater Intermediate / advanced topics pertaining to geospatial and environmental data analysis. Combining R with geospatial softeare like ArcGIS and QGIS. Rendering models and point clouds from photogrammetric / LiDAR survey. Data collection and preparation of raw sensor data.