Analyzing Data From Our “Intro to Data Science in R” Workshop

Thanks for stopping by our website and checking us out! You’re probably wondering if our “Intro to Data Science in R” workshop is the right course for you, and if you’re coming in with the right skills to succeed. Luckily, we’ve crunched our survey response data to answer those exact questions. (In case you’re curious, our R code for the bar graphs is included at the bottom of this blog post.)

We’ve had 43 students attend our “Intro to Data Science in R” workshops at 1871 this past year. Ninety-one percent of them filled out a survey for us at the end of the workshop.

Would You Recommend The Course to a Friend?

After going through the two-day workshop, 100% of the survey respondants said they were likely to recommend us to a friend, with 73% saying they were very likely to do so.

Likes and Dislikes

We asked students what they liked about the workshop.


Students liked how the instructors were helpful, answered questions and provided good examples.

We asked students what they didn’t like about the workshop.


Most students disliked little or nothing. Some felt that parts of the workshop, such as the portion on statistics was a bit challenging.

Skills Coming Into The Workshop

We asked students about their analysis and programming skills coming into the workshop.

Data Analysis and Data Science Skills


We know that these two terms - “data analysis” and “data science” - can mean two very different things depending on where you work. This is a discussion we have on the first day of our workshop as well. That said, looking at the survey data and chatting with our students, we found that most people who come into the workshop work with data on a day-to-day basis, but want to take their skills to the next level with data science.

Tools Experience (Coding, R, Excel)


While we know that coding is a generic term that can range from familiarity in SQL to expertise in Java, we wanted to get a better idea of our students’ programming backgrounds. It turns out that we had students across the board, from no programming experience to deep experience. We also saw a clear indirect relationship of the skills of students in Excel and R. Most people coming into the workshop had a lot of experience working with Excel and had heard of or dabbled in R before, but wanted a more structured learning experience.

Difficulty & Pace

Finally, how did the students feel about the difficulty and pace of the class?


Throughout the two-day workshop, we walk through about 15 hours of content covering topics in statistics, programming and analysis. Some concepts are harder to grasp than others, but we strive to go at a pace that is best for the whole group.

We were happy to find that the majority of the students felt that the difficulty and pace of the workshop were “just right”.

We had a few students tell us the difficulty and pace were a bit fast. We want to make sure we are providing our students with the necessary content to get a solid understanding on data science. That said, we understand there is a lot to cover, which is why we always have three instructors on hand, one teaching and two answering questions throughout the weekend.

Final Thoughts

Overall, we are very happy with and proud of the curriculum that we’ve created for the “Intro to Data Science in R” workshop. We (and our former students) believe that it provides a comprehensive overview of data science along with relatable and applicable examples.

We’ve had a great time meeting and connecting with students from all around the Chicago area over the past year.

For those of you interested, our next workshop will be at 1871 on Saturday, July 23rd and Sunday, July 24th. We hope to see you there!

Code Sample

As promised, below is the R code we used to create the data analysis graph above.

library('ggplot2') # Make sure you install first with install.packages(‘ggplot’) 

# read and view the data
data = read.csv('/path/to/file/skills_rating.csv')
# data analysis graph
da_graph = ggplot(data, aes(factor(Data.Analysis))) +  # Data to graph
  geom_bar(fill = "purple") +  # Bar Colors
  ggtitle(label=paste('Skill: Data Analysis' )) + # Title
  labs(x='Level of Experience', y='Number of Ratings') + # X and Y axis labels
 scale_x_discrete(breaks=c(1,2,3,4,5), labels=c("None", "Little", "Some", "Moderate","Deep")) # X axis labels