Interview Workshops Breakout Sessions Guide

Read this guide before participating in the Interview Workshop. You will use need the information and practice rubric from this page during the Breakout Sessions!

Download the PDF version here.

Overview

In this workshop, we will:

1. Discuss why employers might ask this question.
2. Provide a strategy for answering these types of questions.
3. Give you a chance to practice responding to the question and receive on-the-spot feedback in a safe learning environment.

Why do employers ask this question?

1. Do you use similar programs and techniques as them?
2. Is the work you enjoy related to the work you will be doing in the new job?
3. Can you back up the information listed on your resume/LinkedIn?

1. Answer the question.

2. Share an example using the STAR technique.

Example Response

Explain your favorite data visualization tools/techniques.

Job Candidate: My favorite data visualization tool is Tableau. I love how I can take a large data set and turn it into something actionable and visually appealing.

A few months ago, our Director announced that she was looking to expand our company to another major city by opening a new retail store (Situation).

My supervisor put me in charge of determining a list of possible locations (Task).

I pulled data from our online sales database to see which cities most of our customers were ordering from. I also collected location data from our email list. Lastly, I created 2 Filled Maps using Tableau to show the areas with our highest customers and potential customers and delivered it to my supervisor (Actions).

My supervisor used my maps in a presentation to the Director and they selected Houston, TX, which was in alignment with my reports! (Results)

A great way to prepare for questions like this is to think about your most impressive stories in advance. You can plan out the response you'd like to use before we practice by using the STAR Method. Try it now!

(Try to develop your own answer first. Then, scroll to the bottom to view some more example answers.)

Exercise 1: What is your favorite data visualization tool/technique?

a) Your Answer:

b) Your Example/Story:

SITUATION -

TASK -

ACTIONS -

RESULTS -

Exercise 2: What is the most effective way to visualize data for non-technical team members?

a) Your Answer:

b) Your Example/Story:

SITUATION -

TASK -

ACTIONS -

RESULTS -

1. In your breakout group, take 30 seconds to introduce yourself.
   1. Remember to turn your video on!
2. Assign each person to one of the 3 roles below. (Optional: If you're having a hard time dividing roles, use the tip in the parenthesis.)
   1. Job Candidate. You will answer the question, and ask clarifying questions, if needed. You may want a pen and a piece of paper to do some light math, if the workshop is on a technical interview topic. Remember to walk the interviewer through your thought process! (Optional: Assign to person with an upcoming birthday closest to today.)
   2. Interviewer. You will ask the question (see below) and provide answers to clarifying questions from the interviewee, if needed. (Optional: Assign to person with the next upcoming birthday.)
   3. Observer(s). You will observe the “interview.” You may take notes from an external perspective. Pay attention to the body language and actual answer - what feedback would you give to the job candidate to help them improve?
3. Hold the practice session for 10 minutes. Once roles have been assigned, the job candidate and interviewer should practice the question presented as the workshop topic as though participating in a real interview.
4. Review the rubric provided during the workshop against the practice session. Share observations and feedback with each other. Has anyone answered a similar question in a job interview? As the interviewer or observer, how would you have answered the question? Remember: We're all in similar places to each other when it comes to interviews, and we all want each other to succeed.
5. Repeat the session with each person in different roles, until it’s time to return to the main group. It’s okay if you have the same role more than once, but your session should allow each person the opportunity to be the job candidate.

Breakout Sessions Rubric

(Use this rubric to guide your feedback and observations during the Breakout Sessions.)

(1) Specificity of the answer

Check each item that accurately describes the candidate's answer:

• The candidate understood the question and discussed a relevant example.
• The candidate explained how they arrived at their answer.
• The candidate seemed to have a good grasp of the best practices for solving such a problem (ie: used best framework, applied data science skills, used right terminology).
• The answer addressed the question and included relevant clarification requests, step-by-step reasoning, or on-topic contextual information. All of this helps the interviewer build confidence in the candidate's mastery.

(2) Intelligibility

Could the candidate be heard and understood? Check any of the items below where the candidate could improve.

• The candidate's voice was too low to be heard comfortably.
• It was often difficult to understand the candidate's words because of the candidate's enunciation.
• It was often difficult to understand the candidate's words because of the candidate's word choice.
• The candidate's train of thought was difficult to follow.
• The candidate spoke clearly (volume, enunciation and word choice were OK).
• The candidate provided a well structured answer (train of thought was easy to follow).

(3) Body language

How well did the candidate engage with the interviewer non-verbally?

Check any of the items below where the candidate succeeded.

• There was an appropriate amount of eye contact.
• The candidate faced the general direction of the interviewer.
• The candidate directed an appropriate amount of smiling and/or positive emotion toward the interviewer.

Check if any of the items below are areas where the candidate could improve.

• Anxiety was strongly noticeable in the candidate's posture, facial expressions or voice.
• The candidate cut off the interviewer or was disrespectful toward the interviewer in other ways.
• The candidate distracted from the interview (e.g. talked excessively about something outside the interview, moved a lot, etc).

Sample Answers

We have provided sample answers to the practice questions for your reference.

What is the most effective way to visualize data for non-technical team members?

I believe the best way to visualize data for non-technical team members has to start with which visualization techniques match the project requirements and audience best. You can then decide how to simplify the content to make it more easily understandable from a non-technical perspective.

I actually had to do this recently. I usually do more high-level data analyst work, but our CEO recently announced that she wanted all of our departments to invest in some basic-level data analysis training for all employees. She wanted my department to develop in-person training as well as an online resource center (Situation).

I was tasked with creating the in-person training workshop (Task).

One principle for helping an audience grasp the meanings in data is to develop their understanding of the context in which the data originates. So I decided to have my workshop participants involved in creating the data that we would be visualizing and analyzing. I also decided we’d use a Google Sheet for gathering and visualizing our data, since most people have some knowledge of spreadsheet tools. I created a game to collect data by having participants shoot paper balls into a trashcan. The first round, they had to do it with their non-dominant hand. The second round, they had to do it with their dominant hand. Misses were 0 points, they got 1 point if they hit the can, and 3 if they made it. I asked each person to track their data in a shared Google Sheet. When we compared the two rounds, the 2nd round scores were of course much higher. To help them visualize our data effectively, I used some key principles of data visualization, such as using an appropriate kind of plot for the data type you have, using visual patterns, such as positional changes in a plot, that make it easiest for people to understand the key information, and creating a high data-to-ink ratio.With these concepts in mind, I chose a bar chart for my participants to view the data, where the relative heights of the bars highlighted the message in the data. We then went on to engage in some basic analysis of the data they had produced as well. (Actions).

After the activity, I was surprised at how much the attendees enjoyed the experience, and gave me feedback on how they learned from the data gathering, visualization, and analysis activities . My supervisor was impressed as well and there have been conversations about me facilitating this training with our incoming cohort of interns. (Results)

How do you design a visualization to represent data?

Designing a visualization is actually my favorite part. I make my decisions based on the data types in the project, which type of plot and which visual elements will best highlight the key meanings in the data, and who the audience is.

I recently completed a Nanodegree program from Udacity in Business Analytics. One of our assignments was to analyze New York Stock Exchange data (Situation).

We had to use spreadsheets to analyze and summarize the data, and then incorporate visualizations into the professional explanation of our key findings. (Task).

I designed a visualization that used principles of effective data storytelling, beginning with my choice of a line chart, as it shows changes over time very well. I also incorporated different colors to highlight key points in the data. Finally, I made sure to keep my ratio of data to ink high, as this is another key aspect of designing high-quality data visualizations. (Actions).

My instructor was impressed with how quickly I created this effective visualization and analysis to convey my findings, and it really got me excited about combining my experience in finance with my new data analysis skills in Microsoft Excel (Results).