My name is Annie Cushing, and I’m passionate about all things data. Below is a summary of some of my work.
Table of Contents
AI App Code Samples (Python)
Machine Learning Code Samples (Python)
Automation Projects
Data Viz Samples (Tableau & Python)
SQL Samples
dbt Demo
Python Presentation Sample
Making Data Sexy
Tips
- Some portfolios contain videos as well as images. If it’s a video, there will be a play button in the lower-left corner.
- I recommend viewing both images and videos in full screen by selecting it from the lower-right corner.
Tip: Videos in the portfolios are best viewed by setting both the portfolio and the embedded video to full screen, as I demonstrate below.
- Move your mouse away from the video to view annotations, which I use to tell the story of a project.
- Press the esc key to exit full-screen mode.
AI Apps (4)
4 samples
I’m only including AI apps if I either built them from scratch solo or if I built a component of the app so as to not take credit for anyone else’s work.
I’ve also built out user journey maps for AI apps, after doing extensive research into offerings that are typically scattered throughout a client’s website and supplementary material.
I also maintain a comprehensive timeline of AI news and developments that dates back to 2019.
Each event has extensive notes for quick reference, with citations for additional research.
Machine Learning (7)
7 samples
I’ve worked with linear and logistic regression, k-means clustering, Naive Bayes classifiers, support vector machines, Random Forest, ridge regression, Gaussian mixture, and neural network models to date.
Automation Projects (3)
3 samples
These projects use a combination of Google Apps Script, Google Functions, Python, JavaScript, SQL, and Tableau. Whatever is necessary to automate tasks for clients and myself.
Data Visualization (34)
34 samples
Tableau (17)
17 samples
Although all dashboard tools are variations on a theme, my personal favorite is Tableau because of its ability to create highly dynamic dashboards. Below are some samples of my work.
Python
17 samples
Although Matplotlib is the de facto standard and I’ve worked with it, Plotly is my favorite Python graphing library because it’s interactive visualizations are very elegant. I’ve customized them so much, I have an 86-page document with notes, screenshots, and example files so that I can easily rinse and repeat.
SQL (12)
12 samples
Query Samples
I think SQL might be my fave programming language because of how rewarding it is to clean, transform, and massage data before it even hits the dashboard tool du jour. I especially enjoy creating segments on the fly that I can later use as an additional dimension in a chart and/or filter in a dashboard. Below are a few samples of some of the SQL queries I’ve written over the years.
Note: I normally comment out my code like it’s my job, but Tableau chokes on SQL code with comments and then flips on its back and wants a belly rub. 🙄 I now spawn off tables that I set to refresh on a regular cadence and reference the table in my dashboards to avoid this issue, but I still need to go back and clean up my old code.
dbt Demo (1)
1 sample
Because of the sheer number of redactions that would be required to share my dbt work with clients, I created a demo project using publicly available data in BigQuery. I demonstrate:
- A data source YAML file that dictates the tests (i.e., constraints) to run on select columns to ensure data integrity.
- Multiple models of varying complexity (i.e., staging, intermediate, and mart).
- The inclusion of a seed file to pull in country names (the data sources just had ID and who knew the country code for Austria was AT).
- Macros to merge and sum 20 columns using Pythonic logic in a Jinja template.
- The creation of dbt’s sexy lineage graph to visualize your data pipeline.
Python Presentation Sample (1)
1 sample
I’ve built out a custom workflow using a combination of five Python libraries (all free) and Jupyter Notebook extensions to create presentations with interactive charts that can be saved as an HTML file. Clients love this option because they can continue to dig into their data after I’ve presented it. For persistent data needs, I build out Tableau dashboards, but I do almost all ad hoc analyses in Python. I can’t share presentations I’ve created for clients, but below is an example presentation I did with The New Yorker‘s data because they weren’t a client.
Making Data Sexy Sample Chapter (44)
44 pages
I published two versions of Making Data Sexy: one for Mac and one for Windows. It provides tutorials as well as tips for visualizations that aren’t built in to Excel. Since those processes oftentimes vary significantly between Mac and Windows, it warranted two different books.
Download this sample chapter here.