Available for Employment: Finishing studies and projects shortly and will be applying for positions. Hiring managers can contact me at ejw.data@gmail.com.
I am
Erin James Wills – engineer turned data enthusiast
In my free time, I have a variety of interests but I truly enjoy developing curriculum for technical fields (specifically for adults) and exploring the different ways data can be used to solve problems - whether it be through estimation/simulation or through modeling with large datasets - are two of main interests. I also enjoy reading, kayaking, cooking, and attending as many Chicago neighborhood festivals as possible.
Professional Statement: I am a versatile engineer that has typically worked on operations related problems by either reducing cost (improving efficiency) or increasing production (overcoming output limitations) or ideally both. I am often entrusted with tasks that require me to solve sensitive problems that require tact, technical knowledge, executing past uncertainty, and developing consensus among technical and non-technical peers.
Professional Goals: I have often been required to use many different technologies which could be a strength or a weakness in the job market - it shows I can learn quickly and have broad experiences but it also means that I don't have extended use of one technology. All the places I have worked needed someone to help implement projects for others and the nature of that job is to move from need to need to keep organizational goals moving forward. I do not mind this type of work and to this day I use the title engineer because the nature of my work closely aligns to that role. In the future, I would like to spend more time becoming a specialist and developing a more consistent knowledge base in a single field - specifically data science.
Why I Did Not Choose Formal Data Science Training
In 2019, I decided to invest more time to learn modern data skills. I did not think a masters program was a good fit for me since I already had many of the technical skills needed (coding, statistics, web technologies, databases, etc.) and spending $50k on classes that quickly cover a topic in a matter of a few days seemed nonsensical. I was also not thrilled that most programs emphasized the use of R while most established data scientists that I met at Meetup.com meetings used Python more often since they worked with software developers.
When looking at several master programs, I saw several commmon features. Each program consisted of about 12 classes with 10 to 16 weeks of content depending on their academic calendar. Most of the programs had seven required classes but the content and structure was unique to each program. I used these programs as a guide for how I would design my own curriculum.
My Curriculum
A comparison of common master program courses to my personal curriculum and experiences can be found on my curriculum page.
- Mathematics - includes Linear Algebra and Ordinary Differential Equations
- Statistics - engineering, social science, and industry experience
- Coding - Python, SQL, Javascript, Matlab, R, VBA, etc…
- Database & ETL - Pandas, SQL, RDBMS, MongoDB, Athena, SQLAlchemy
- Machine Learning - Regression, Classification, Clustering, Association Mining, and Neural Networks
- Decision Analytics - Discrete Event Simulations, Process Simulations, and Linear Optimizations
- Leadership & Management - Six Sigma, Agile Scrum, project and team management
- API - creating and using APIs with Flask, requests, REST, SOAP, GraphQL
- Python Visualizations - Pandas, Matplotlib, Plotly, Seaborn, Bokeh, hvplot
- Big Data and Cloud - pySpark, SageMaker, Databricks, Splunk, Lambda
Content not typically found in a Data Science degree program:
- DevOps - git automations, data & model pipelines, unit testing, synthetic monitoring
- Data Sources - web scraping, API (xml, json), databases, flat files (csv, tsv, etc), pdf, images
- Algorithms - recursion, routing, heuristic, binary search, sorting
- Geo - QGIS, postGIS, geopandas, gmaps, hvplot, Plotly, Leaflet.js, Folium
- WebDev - HTML, CSS, Javascript, Bootstrap, Jinja, Liquid, image editing, SVG development
- Visualization Tools - Excel PivotTables, Tableau, Looker, Power BI
- Javascript Visualizations - Leaflet.js, Plotly.js, D3.js, Highcharts, charts.js
Recent Projects
- Chicago Food Inspection Analysis
- Linear Optimization - Production Scheduling
- Discrete Event Simulation
- Synthetic Monitoring
- Machine Learning
- pySpark ETL
- Spacial Queries of Census Data
- AP Reported Vote Tally ETL
Upcoming Projects
- AWS DevOps
- OOP Financial Estimator
- Tableau Visualization from Cloud Webscrape
- PowerBI and Qlik and Looker Review
- Custom Routing Algorithms
- Reinforcement Machine Learning
Industries
Below are several industries that might be interested in my background and skills that would also often want the skillset that I am developing. This is not an all inclusive list but industries that I have some opertional familiarity.
Potential Long-term Professional Development
Based on trends that I see, I think there will be continued need for people to have these skillsets. In many cases, master's programs emphasize traditional topics and these topics are left as short electives but I think there are undiscovered opportunities using these methods.
- Geospacial Analysis
- Time Series Analysis
- Process Simulations
- Reinforcement Learning
Photo by Education
BS Chemical Engineering
Purdue University
MS Education
University of Tennessee, Knoxville
Certificate in Data Science
and Visualization
Northwestern University