Machine Learning & Data Analytics

Module 02 — Workplace Makeover, Class Discussion Questions

Meeting Photo by Campaign Creators on Unsplash

Questions

You're about to go into a strategy meeting with the CEO, Vice President of Human Resources, and Vice President of Finance. They want to make sure you have the data required to answer the questions they're most interested in.

Be prepared to answer the following questions:

Employee Satisfaction

Cecil, the VP of Human Resources asks:

My biggest concern right now is whether or not employees are satisfied with the culture here and what they're being asked to do.

Given the data we have, what do we need to do to get an accurate measurement of how employee satisfaction has changed over time?

Based on your initial analysis of the data, your team feels:

  1. We have the necessary data in the correct form to answer this question.
  2. The data we have cannot answer that question, we need to collect more data.
  3. We could use the data we have, but we'll have to normalize some of the features, and/or encode some of them differently.
  4. Answering this question would be a violation of ethics and/or privacy laws.

Work-Life Balance

William, the VP of Finance asks:

My biggest concern is the rising costs from employee sick leave and tardiness. I've noticed a lot of this seems to center around employees with children, especially single mothers.

Do we have a way to tell from our data if there is some correlation between being an older employee with a family and absenteeism? If so, I'd like to use that information to adjust how HR screens new hires.

Based on your initial analysis of the data, your team feels:

  1. We have the necessary data in the correct form to answer this question.
  2. The data we have cannot answer that question, we need to collect more data.
  3. We could use the data we have, but we'll have to normalize some of the features, and/or encode some of them differently.
  4. Answering this question would be a violation of ethics and/or privacy laws.

Company Diversity

Devon, the CEO asks:

I'm concerned that our workforce has the proper balance of diversity. The board would like us to make sure we're using effective methods of recruiting women and minorities.

Do we have a way to tell from our data whether or not our current methods are working, and if not, can we track what the most effective methods have been to date?

Based on your initial analysis of the data, your team feels:

  1. We have the necessary data in the correct form to answer this question.
  2. The data we have cannot answer that question, we need to collect more data.
  3. We could use the data we have, but we'll have to normalize some of the features, and/or encode some of them differently.
  4. Answering this question would be a violation of ethics and/or privacy laws.

Equitable Pay

Cecil, the VP of Human Resources adds:

Recruiting women and minorities is a great start, but we need to make sure we're retaining them.

One key to that is to make sure our salaries are fair and equitable across race and gender boundaries. Can our data tell us if we're doing a good job there?

Based on your initial analysis of the data, your team feels:

  1. We have the necessary data in the correct form to answer this question.
  2. The data we have cannot answer that question, we need to collect more data.
  3. We could use the data we have, but we'll have to normalize some of the features, and/or encode some of them differently.
  4. Answering this question would be a violation of ethics and/or privacy laws.

Data Analysis

Johnny, the data science intern asks:

I've heard that clustering is a big thing these days.

Do you think we could help answer one of these questions using clustering?

Based on your initial analysis of the data, your team feels:

  1. Question 1 would benefit the most from clustering.
  2. Question 2 would benefit the most from clustering.
  3. Question 3 would benefit the most from clustering.
  4. Question 4 would benefit the most from clustering.

  1. CEO photo by Oz Seyrek on Unsplash  

  2. VP of HR photo by Christina @ wocintechchat.com 

  3. VP of Finance photo by steffen Wienberg on Unsplash 

  4. Data Science Intern photo by Fábio Lucas on Unsplash