Over the years, I have acquired a variety of technical skills that I have used in support of various projects.

Excel

Microsoft Excel may not be considered a traditional programming language, but it is a very useful data management and analysis software. Most people I have come across with limited data management experience still have some familiarity with Excel, so this is a broad-reaching and relatively intuitive platform. For this reason, I helped develop two Excel workshops (Intro and Advanced) to offer students while working as Data Analyst. These were some of the most well-attended workshops that we have had at the uOttawa Library.    

I have also made use of Excel’s VLOOKUP function to join two or more tables for a variety of projects, including the Postal Code Conversion File (PCCF) and work with Heather Morisson’s research team working on open access. 

Python

During my work as GIS Analyst at Fisheries and Oceans Canada (Small Craft Harbours branch), my team needed to create “harbour profiles” for over 100 harbours. This would include a lot of different information and would require a major time investment to do one-by-one (likely weeks of work). However, I spent a few days developing a Python script that automatically ran through a dataset, line by line, and generated a new series of maps for each one and extracted them, as well as data, into custom PDF/JPG harbour profiles. This script took about two minutes to run and generate all the harbour profiles that normally would have taken weeks to produce manually. Further, the script could also be modified should additional or different information be required for future purposes and all 100+ profiles be recreated with the right modifications in a relatively short amount of time.

This example serves to show the power of programming, particularly when dealing with a series of repetitive tasks.  

  • View the python script that was used to generate the sample harbour profile below (note: redactions were added for confidentiality):

Statistical Programs

During my work as Data Analyst and during my MIS, I have also learned to use the following statistical programs: SAS, SPSS, and Stata.

These programs are not traditional programming languages like Python, but are used to conduct advanced statistical analyses on large databases. These would normally crash Excel.

SAS

SAS is the most robust of these softwares and can handle the largest datasets with millions of records. It is commonly used by health care professionals and in medical research. To support the Real Time Remote Access (RTRA) service, I needed to help users write SAS code. To better learn this software, I registered for Epidemiological Research Using Large Databases (EPI 5143) where I learned to use SAS and used it on sample hospital data. SAS code is very similar to programming and has its own syntax (i.e. rules such as following each line with a semi-colon).

SPSS

SPSS is the most user-friendly of these statistical softwares and has the most intuitive interface. However, it cannot handle the same-sized data files as SAS could. It is often used in social science research. I learned to use SPSS through the following workshop I attended and the resources I found on the Ressources for SPSS Users research guide.

Stata

Stata walks the line between SAS and SPSS in that it can handle relatively large datasets and is somewhat intuitive to use. Stata is often used by economists. I learned to use Stata while taking Quantitative Research Methods (API 6319) and learned how to run inferential statistics such as simple, multiple, and logistic regression analyses. See below for a few assignments for this class where I assessed what variables are correlated with social media use in Canada.