All posts in Professional Development

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.

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

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

MIT Sloan Sports Analytics Conference

MIT Sloan Analytics Conference

March 1 – 2, 2019.  Boston, Massachusetts.

Conference Mission:

The conference goal is to provide a forum for industry professionals (executives and leading researchers) and students to discuss the increasing role of analytics in the global sports industry. MIT Sloan is dedicated to fostering growth and innovation in this arena, and the conference enriches opportunities for learning about the sports business world.

Read my conference recap.

Datathon at Brock


About the Datathon:

The amount of data our world produces has exploded! Some estimates put the total amount of data generated each day at 2.5 quintillion bytes.​

Organizations have been eagerly adopting big data analytics for their ambitious goals. We’re only just starting to see how revolutionary big data can be, and we can expect even more changes on the horizon.​​

The DATATHON Educational Conference provides students with the opportunities to learn about the increasingly important field of data analytics.

Canadian Open Data Summit

canadian open data summit

About the conference:

CODS18 is the latest in a long series of annual conferences held across Canada to convene and bolster the Open Data movement.  Taking place this year from November 7th to 9th at the Fallsview Casino Resort and Hilton Hotel in scenic Niagara Falls, Ontario, Canada, CODS18 convenes upwards of 300 registrants.

Read more about the conference agenda.

Business Analytics – Harvard Business School Online

harvard business online

About the course:

Business Analytics will help demystify data and equip you with concrete skills you can apply in your everyday decision-making. Beginning with basic descriptive statistics and progressing to regression analysis, you’ll learn business analytics through real-world examples, from performing A/B testing on a website to using sampling to check warehouse inventory.

Length: Approximately 40 hours of content over an eight week period.
Completed: June 2018

  • Interpret data to inform business decisions
  • Recognize trends, detect outliers, and summarize data sets
  • Analyze relationships between variables
  • Develop and test hypotheses
  • Craft sound survey questions and draw conclusions from population samples
  • Implement regression analysis and other analytical techniques in Excel

Big Data Toronto

big data toronto

About the conference:

Big Data Toronto is Canada’s #1 Big Data and Analytics Conference & Expo. Join the Canadian data industry on June 12-13, 2018 at the Metro Toronto Convention Centre in downtown Toronto for two days full of education, networking and product demos to help take your data and analysis to the next level. The conference will focus on technical and practical verticals including use cases around predictive analytics, advanced machine learning, data governance, privacy, cybersecurity, Smart Home & IoT, digital transformation, Hadoop, cloud analytics and cloud computing.

2018 is the year of intelligence and Big Data continues to be a driving force in innovative Canadian businesses. This year, Big Data Toronto is co-located with the AI Toronto Conference to provide you with a unique 2-in-1 learning experience that is engineered to meet your data needs and challenges.

With more than 4000 participants, 100 speakers, and 60 exhibiting brands, Big Data x AI Toronto provides you with the tools, skills, and networking opportunities that you need to guide your company to a new dimension!

Read more about my conference recap.