Modules

This page contains links to the lectures and materials organized into modules. Modules typically correspond to a week of the course, but may span multiple weeks or be shorter than a week.

• Week 1: Course Introduction

  Introduction to the course and setting up software.
  🖥 Slides
  💻 Lab
  📝 Lab Assignment
  

• Week 2: Asking the Right Questions

  Lecture will focus on a framework for data-driven decision making and how to ask the right questions. Lab will focus on finding data to answer your questions.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Puntoni and De Langhe. Better Decisions with Data
  • Heidari. Analytics starts by asking the right questions
  • Raoufy. The Art of Asking the Right Questions
  
  

• Week 3: Data Processing Part 1

  Lecture will focus on the importance of data processing for overall analysis.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Data Processing Reader
  
  Additional Materials:
  • corn_yield_missing.csv
  • corn_wide.csv
  • dm_state_2000-2025.csv
  
  

• Week 4: Data Processing Part 2

  Lecture will complete the unit on data processing with a focus on merging or joining.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Data Processing Reader (Merging Datasets)
  • Using AI Tools to Learn and Write Code
  
  Additional Materials:
  • BEA_farm_gdp_2000-2024.csv
  • corn_yield_2000-2025.csv
  • dm_state_2000-2025.csv
  
  

• Week 5: Exploratory Data Analysis

  This module will focus on what exploratory data analysis is, why it is imporant, and how to do it in R and Power BI.
  🖥 Slides
  💻 Lab
  Additional Materials:
  • final_merged_data.csv
  • drought_weekly.csv
  
  

• Week 6: Forecasting

  This module will focus on forecasting methods and how to do forecasting in R.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Forecasting: Principles and Practice (Ch 1-3 & 5.7)
  
  

• Week 7-8: Regression

  This module will span two weeks and focus on regression methods to inform decision making.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Introduction to Regression Analysis
  • A Short Introduction to Econometrics with R
  • Yusuke Kuwayama, Alexandra Thompson, Richard Bernknopf, Benjamin Zaitchik, Peter Vail. Estimating the Impact of Drought on Agriculture Using the U.S. Drought Monitor, American Journal of Agricultural Economics, Volume 101, Issue 1, January 2019, Pages 193–210.
  • Lobell, David B., Jillian M. Deines, and Stefania Di Tommaso. Changes in the drought sensitivity of US maize yields. Nature Food 1.11 (2020): 729-735.
  
  Additional Materials:
  • Video: Regression Explanation
  • Video: Uncertainty and Hypothesis Testing, Part 1
  • Video: Uncertainty and Hypothesis Testing, Part 2
  
  

• Week 10: Midterm Review

  Midterm review
  🖥 Slides
  

• Week 11: Storytelling with Data

  This module provides an overview of the second part of the course on communicating your data-driven insights. Note that you must access the book through the CSU library. Choose the version by O'Reilly Online Learning.
  🖥 Slides
  📖 Readings:
  • Storytelling with Data: Introduction
  
  

• Week 12: Choosing the Right Visualization

  This module focuses on how to choose the right data visualization to communicate your insights.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Storytelling with Data: Chapter 2: Choosing an Effective Visual
  
  Additional Materials:
  • Farm-Level Data for Lab
  
  

• Week 13: Spatial Data in R and PowerBI

  In this module, we will cover the basics of spatial data and how to create spatial visualizations in R and Power BI.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Geocomputation with R: Sections 2.1 (Introduction) - 2.2.5 (Vector Data up to 2.2.5, but not 2.2.6)
  • Tips and tricks for Power BI map visualizations
  
  

• Week 14: Refining Data Visuals

  This module focuses on reducing clutter, creating visual order, and directing audience attention so visuals communicate business-relevant takeaways clearly.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Storytelling with Data: Chapter 3: Clutter is your enemy
  • Storytelling with Data: Chapter 4: focus your audience's attention
  
  

• Week 15: Storytelling and Presenting Data-Driven Insights

  This module focuses on how to present your data-driven insights in a compelling way.
  🖥 Slides
  💻 Lab
  📖 Readings:
  • Storytelling with Data: Chapter 7: Lessons in Storytelling