Week 1: Introduction

Meet your instructors*

*That’s right – you have two!

 

To get to know you better, we have a few questions for you . . .

Would you choose to watch a documentary about the mysteries of the universe or the depths of the ocean over a popular TV series?

  1. Definitely yes

 

  1. Neutral

 

  1. Definitely not

Do you prefer watching live sports events, such as basketball or football games, over educational programs about nature or history?

  1. Definitely yes

 

  1. Neutral

 

  1. Definitely not

If you come across a news article about a new scientific discovery or technology, are you likely to read it immediately?

  1. Definitely yes

 

  1. Neutral

 

  1. Definitely not

Would you rather watch reality TV shows or celebrity gossip programs instead of documentaries on nature or scientific topics?

  1. Definitely yes

 

  1. Neutral

 

  1. Definitely not

Thank you. Now onto the lecture.

Data IRL

The Oakland A’s

The A’s go on to win the American League West with one of the lowest payrolls in baseball.

How? Data!

The team uses in-game data to drive team management decisions:

  • sign players because they have high stats (e.g., on base percentage) for a good value

  • tell players to look for certain pitches because they tend to hit those pitches better

Data-driven decision making is a process in which decisions are based on data and analysis rather than on intuition or personal experience.

The objective of this course is to introduce this process of transforming data into actionable insights.

This process requires understanding how to read figures (e.g., graphs, charts, and maps), discern between “good” figures and “bad” figures, and tell effective stories using data-derived figures.

Let’s start with a few examples

  1. What does the figure tell you?

  2. Is it clear what the author’s did to produce the figure?

  3. Was the research a shock or exactly as you expected?

 

Example 1: Good Data Project

Example 2: Sustainable Happiness

Example 3: Mapping Segregation

Sharpening your data visualization tools

By the end of this course, you might not be producing interactive maps for the NY Times (although we would fully endorse this)…

But, you will learn the tools to produce high-quality figures.

Example 1: Student 1

Example 2: Student 2

Agenda

 

Syllabus and course orientation

 

Introduction to D\(^3\)M

 

Introduction to R

 

Introduction to Tableau

How to succeed in this course

  1. Attend class lectures (W) and labs (F) (5% of grade).
  2. Don’t miss the quizzes (20% of grade).
  3. Review project assignments in advance. There are four projects (75% of grade).

 

Dr. Bayham’s Office Hours: Wednesday 2-3 pm, Rm 201

Dr. Chenarides’ Office Hours: Friday 3-4 pm, Rm 203

Zarif’s Office Hours: Thursday 12-1 pm, Rm 165

D\(^3\)M

 

Data-driven decision making is a process in which decisions are based on data and analysis rather than on intuition or personal experience. It involves collecting data, analyzing it, and using the insights gained from the analysis to inform the decision-making process. The goal of data-driven decision making is to use data to make more informed, objective decisions that are likely to lead to better outcomes. This approach is based on the idea that data can provide a more accurate and complete picture of a situation than personal experience or intuition alone. Data-driven decision making is often used in business and government to improve efficiency, reduce costs, and make more effective decisions.

— ChatGPT

D\(^3\)M Introduction Exercise

Turn to a neighbor and…

  1. Introduce yourself - name, major, fun fact.
  2. Find some common ground - find one shared characteristic. This must be something you did not previously know and something that is not visible.
  3. Discuss real-world examples of D\(^3\)M with your neighbor.
  4. Share one example related to ag or natural resources with the class.

Google Human Resources

Google’s Project Oxygen: Google surveys employees about their managers and connects the responses to team output to figure out what attributes or management practices lead to more productive teams.

 

Google’s PiLab: Google conducts applied experiments to determine the most effective approaches for managing people, including increasing productivity, increasing happiness, and improving health.

 

Diversity at Google: Google’s people analytics team conducts analyses to identify the root causes of weak diversity recruiting, retention, and promotions. These analyses have led to efforts to reduce bias in job descriptions, develop diversity-oriented mentorship programs, and produce annual diversity reports.

Amazon

Amazon uses a dizzying array of metrics to track your behavior with their platform in order to recommend products that they think you will buy.

Amazon Poll 1

     

What information does Amazon use to collect data on users?

Amazon Poll 1

What information does Amazon use to collect data?

  • Purchases

  • Wishlist items

  • Shopping cart saves

  • IP address, login credentials, computer location

  • Clicked URLs; Mouse hovering

  • Timing of scrolling and clicking

  • Alexa

  • Kindle highlighting

Amazon Poll 2

     

How does Amazon use the data it collects? (in other words: what decisions does data inform?)

Amazon Poll 2

     

How does Amazon use the data it collects?

  • Personalized recommendation system

  • Anticipatory shipping model

  • Book recommendations

  • Prices (Amazon changes their prices an average of 2.5 million times a day)

  • Suggested add-ons

  • More scrolling!

Summary and Reminders

 

What is D\(^3\)M?

 

What are the course objectives?

 

Up Next: What is R? What is Tableau?

What is R?

What is Tableau?