The Blog

Data Science – Capstone


For the final course of the HarvardX Data Science program, our final project was to create a machine learning project. For this project I chose to create a model that predicts house prices based on a publicly available dataset of actual sales in a region near Seattle.

The attached document was written in R and knitted together into a PDF.

Collaborative Technology Project

One of the courses that extensively uses technology in the classroom is BPCS9705 – E-Commerce Applications.  In this course, students work in groups to build a functioning e-commerce website using the platform Shopify.

The example below shows a site that sells model cars, including images and descriptions of the cars, as well as the functionality of the site from the administrator’s perspective.

Excel Recap Assignment

assignment 1

In this assignment, I ask students to work with some of the most common functionality in Excel.  This lays the ground work for the following assignment which is a deeper analysis of a real world data set.

Assignment Overview:

Microsoft Excel is a powerful, accessible tool that is widely accessible. The goal of this assignment is to become more familiar with Excel as a tool for data analysis. You will review how to import, prepare, and analyze real data.
The video tutorial that accompanies this project can be found at:

Assignment Information Sheet:

Assignment information sheet.


Video Tutorial:

List of Resources:

Instructional Practices

SAP video

Teaching complex software to large groups can be difficult.  Some students move through the processes very quickly, while others need more time to fully grasp the concepts.  Pacing is incredibly important when teaching software to ensure that no one is left behind while at the same time not moving too slowly for those faster learners.

For a course that was focused on logistics software (SAP) I created a video for each one of the modules that we covered in class.  Students could watch these videos before the lecture and come to class prepared with questions, but could also watch after the lecture while they were working through the projects.  This allowed students to supplement the in-class learning while moving at their own pace.

If you wanted to get ahead, or if you wanted a refresher, you can use these videos, in addition to the exercise documents to complete the Sales Order Process.  If you do choose to work through these, please email me a screenshot of the final screen.  You can work with a group to help each other get the concepts, but each individual needs to complete the exercise and show me (or email me) the screenshot.

Course Revision

teaching and learning plan

There were three main goals when updating HOTL9680:

  1.  Include concepts of analytics
  2. Revise the evaluation methods to include projects and active learning rather than strictly tests
  3. Focus the content on sports rather than the broader hospitality sector

After consultation with the program coordinator, previous instructors, and the associate dean, we made the following changes:

Old Teaching and Learning Plan – HOTL9680
Updated Teaching and Learning Plan – HOTL9680

Data Science – HarvardX

hbx data science

Program overview:

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

In each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.

Throughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem.


Data Science: R Basics

  • 1–2 hours per week, for 8 weeks
  • Build a foundation in R and learn how to wrangle, analyze, and visualize data.
  • Completed: May 2019

Data Science: Visualization

  • 2–4 hours per week, for 8 weeks
  • Learn basic data visualization principles and how to apply them using ggplot2.
  • Completed: May 2019

Data Science: Probability

  • 2–4 hours per week, for 8 weeks
  • Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.
  • Completed: June 2019

Data Science: Inference and Modeling

  • 2–4 hours per week, for 8 weeks
  • Learn inference and modeling, two of the most widely used statistical tools in data analysis.
  • Completed: September 2019

Data Science: Productivity Tools

  • 1–2 hours per week, for 8 weeks
  • Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.
  • Completed: September 2019

Data Science: Wrangling

  • 1–2 hours per week, for 8 weeks
  • Learn to process and convert raw data into formats needed for analysis.
  • Status: Currently enrolled

Data Science: Linear Regression

  • 2–4 hours per week, for 8 weeks
  • Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.
  • Completed: June 2019

Data Science: Machine Learning

  • 2–4 hours per week, for 8 weeks
  • Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
  • Status: Not started

Data Science: Capstone

  • 15–20 hours per week, for 2 weeks
  • Show what you’ve learned from the Professional Certificate Program in Data Science.
  • Status: Not started

Technology Assignment


In the course BICG9303 – International Market Research, I created specific labs that use tools like SPSS to actively build on concepts.  In this specific assignment, we use SPSS to build scatterplots to show three variables.

Step by step assignment instructions: Lab 8 Scatterplots

Teaching and Learning Plans

The course Teaching and Learning Plan (TLP) is a vitally important communication tool between you and your students. Your Teaching and Learning Plan will help your students to understand the intended learning outcomes for the course in the form of the knowledge, skills and habits of mind that will be deliberately cultivated in the course, as well as when and how they will be required to demonstrate their learning through assessments. It can also help students to understand how your course functions in relation to the other courses in their program of study.

BICG9303 – International Marketing Research

HOTL9680 – The Global Economy in Sports

Teaching Philosophy

Inspired by the book Drive by Daniel Pink, my teaching philosophy focuses on three main concepts.

AUTONOMY Recognize that learners want the ability to direct their own lives, and structure courses in a way where students are empowered to take responsibility for their own learning.

MASTERY Ensure that courses are relevant, recognizing that students are more likely to want to master something that actually matters to them.

PURPOSE Understand that students want to be a part of something bigger than themselves. Leverage peer work, and where possible larger
projects to instill a sense of purpose and responsibility.

LinkedIn Learning

linkedin learning

Video courses taught by industry experts.

SPSS Statistics Essential Training

  • Length: 4h 57m
  • Completed: April 27, 2019

Shopify Essential Training (2018)

  • Length: 3h 39m
  • Completed: March 30, 2019

The Data Science of Sports Management, with Barton Poulson

  • Length: 1h 2m
  • Completed: November 26, 2018

The Data Science of Retail, Sales, and Commerce

  • Length: 1h 10m
  • Completed: November 23, 2018

Visualizing Geospatial Data with Power Map in Excel

  • Length: 37m
  • Completed: October 3, 2018

Tableau Essential Training (2018)

  • Length: 4h 18m
  • Completed: September 30, 2018

Algorithmic Trading and Stocks Essential Training

  • Length: 1h 29m
  • Completed: September 14, 2018