Welcome…
If you’re reading this it means I reached another human! (at least, hopefully you’re a human)
Let me introduce myself:
I’m a programming and data science nerd who loves learning new ways to work with data! I’m currently working at RBC and learning new things every day. As I was working on my paper last summer I took a step back and thought: I’ve come a long way throughout my research on the road to publication, so why not create a page to keep track of all the “invisible learning” that’s happened behind the scenes? In the process I get to do what I love: programming and working with data while hopefully teaching others. Told you, I am a nerd.
Posts
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Useful Statistical Correlation Techniques: Pearson, Spearman, and Kendall
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Using Visualizations to Analyze Canadian Foreign Direct Investment
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PROJECT: Building PlantTime.com - a Plant Recommendation System (Using AWS DynamoDB, Cloudformation, Lambda, and a CI/CD Github Workflow)
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Granger Causality and Cross-correlation in Python: Determining if the VIX Index is a Reliable Predictor of Foreign Direct Investment in Canada
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Let’s talk Data Structures, Algorithms, and Python (Stacks, Heaps)
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Using PostgreSQL to Build an Object-relational Database and Write Parametric Queries
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Using Python to Build an XGBoost Prediction Model: Penguin Weight Prediction
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Window Functions, Variables, Date Manipulation, Imputation, and Loops in SQL Server
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Using MS SQL and Power BI: The Correlation Between Home Runs and Total Career Earnings
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Why XGBoost is The Best Machine Learning Tool (Intro)
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Using MySQL to Create a Simple Database and Write Queries
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A Short Python Tutorial For My Favourite Visualization: The Exploding Pie Chart
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Bonus Blog Post: My Personal Thoughts on Nobel-Prize Winning Economist Ben Bernanke
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What Does “Machine Learning” Mean? Random Forest Walk, Linear Regression, and Other Common Algorithms
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Python for Finance: Variance, Volatility, and Matrix Multiplication
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PROLOGUE: Introspective Growth - What is “Invisible Learning”?
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