
WES WARRINER
Portfolio
WES WARRINER
Portfolio
Data Analytics, Visualization, and Modelling
INTRODUCTION
Seeing as I don't believe you can discern much at all from the traditional resume, I've decided to display some of my skill set and recent endeavors in a slightly more transparent format.
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The fact of the matter is, I'm no software engineer. I am merely an economics major with a great amount of curiosity and a passion for learning. That said, considering my lack of a computer science degree, I figured I should make some attempt to prove that I know what I'm talking about when I say I love working with data.
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My hope is that this site will amount to some of that evidence, and perhaps even provide a look into my character as an individual, as I will undoubtedly overlook filtering out some of my comments along the way.
About Me
As mentioned above, I majored in economics at the University of Washington, Seattle, taking courses on econometrics and international econ, in addition to general micro, macro, and statistics.
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I also enjoy fulfillment from doing my best to learn foreign languages and interacting with people born and raised overseas. To various extents I've studied a number of languages from French to Mandarin, but thanks to a series of happy coincidences, I've ended up focusing on Japanese these past few years. I currently find myself in Niigata Prefecture, following a semester of study at Tokyo's Hitotsubashi University.
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I am also incredibly interested in physiology, psychology, biology, physics, ...pretty much anything to do with the mysteries of the universe or the peculiarities life on Earth.
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Then, of course, we come to data. Data is merely the general term we use for the measurements of these so-called peculiarities, but it also happens to be (in my opinion, perhaps) the ultimate resource. In economics, in economic game theory, for instance, information is considered either complete or not quite so. But the truth is, in reality information is always incomplete. It is impossible to dissect the infinitely complex web of causes and effects from the beginning of time (or even earlier?) that led to any given outcome we wish to explain. As such, the best we can do is estimate as accurately as possible with the information we do have (as scarce as it is).
This, put excruciatingly briefly, is why I am fascinated by data and hope to make a career out of data exploration. It is a resource that will be scarce long after we have solved the problem of renewable energy, and honestly we are just getting started.
The purpose of this website
EDUCATION
DataCamp
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Data Scientist with R – 95hr series, 23 courses
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Data Scientist with Python – 67hr series, 20 courses
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DataCamp: SQL for Data Science, Joining Data in PostgreSQL
In addition to the general R and Python for data science career tracks, I’ve gone through individual courses and skill tracks that happened to catch my eye, especially for R.
So while most of my work will likely include the standard: lots of tidyr and ggplot2 for visualization, sticking to dataframes for the most part, and sometimes working with tibbles, I also have experience with other packages such as ggvis and lattice for visualization, and data.table.
For both R and Python I made a point of completing DataCamp’s machine learning skill tracks as well, since advanced topics in clustering and scikit-learn, for instance were not a part of my undergraduate studies.
*Again, I don’t believe that a list of course names proves anything, so feel free to take it with a grain of salt. The list is merely here for reference as to what kind of background I have, seeing as I don’t have an aesthetically pleasing way to demonstrate everything effectively all at once.
Also note that the above courses comprise a relatively recent sample (say, within the past year and a half), and are noncomprehensive.
Although I’ve probably gotten more mileage from searching out projects and datasets to practice with on my own time, some of the courses I’ve taken over the past year or so have been stimulating and informative.
The majority of the courses I’ve taken (both at UW and online) have had some sort of final project to go along with them, so I may upload a few at some point if I judge them relevant.
University of Washington
As coursework for my Economics BA
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Econometrics, Advanced Econometrics – statistical modelling, linear and logistic regression using EViews and R. I was first introduced to SAS through my Advanced Econometrics course as well.
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Labor Economics – A course I took while on exchange in Tokyo, but we had a number of assignments involving data analysis with R, so I might include something from this course at some point.
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Coursera
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University of Michigan: Applied Data Science with Python Specialization (5 courses)
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Johns Hopkins University: Data Science Specialization – Exploratory Data Analysis, Reproducible Research, Statistical Inference, Practical Machine Learning
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Yandex: Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames
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Duke: Excel to SQL Data Science Specialization – Excel, Tableau visualization, SQL
edx
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Microsoft:
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Principles of Machine Learning
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Algorithms and Data Structures
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Analyzing and Visualizing Data with Excel
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Developing NoSQL Solutions in Azure
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University of Michigan: Data Science Ethics