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The last post dealt with using pushshift and handling requests to access posts and comments from Reddit. This post deals with using the Python Reddit API wrapper to accces posts and comments from Reddit and then using some NLP tools for some basic sentiment analysis.
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In light of recent internet trends about retail investors, I’m sure many of us have questions about the kinds of content that gets posted on reddit, and if there are home-grown, analytical ways of addressing these questions. I’ll be showing two ways of parsing submissions and comments to Reddit, this one focusing on using pushshift API endpoints using the requests library, some custom classes for processing these responses, and asyncio to handle asynchronous threading for multiple requests to pushshift.
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Some F@H data is freely accessible on AWS. This will be a relatively short post on accessing and navigating the data on AWS.
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What is poetry and where does this fit in the python software/DS ecosystem? And some beginner forays into docker.
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Digital privacy is everywhere, and recent laws are pushing companies to disclose whatever personal information they may have on you. In the spirit of science, I’m going to make myself my own study subject and observe what Facebook has stored from my messenger history. Along the way, I’ll do some recursion, a little parallelization, some generators for data procesing, and basic visualization to observe my messenger behavior. Notebooks can be found here, but this one you can’t reproduce because I won’t be providing my messenger data (try this notebook on your own messenger data if you’re curious).
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Does a team’s defensive scheme influence opponents’ shot portfolios? I’m going to be using a different NBA API to query all the games for the 2018-2019 regular season. In each game, I’m going to log each “make” against a team’s defense. For example, Houston makes a 22-ft 3-point shot against Boston, I will log the distance at which Houston scored against Boston.
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“Open Babel is a chemical toolbox designed to speak the many languages of chemical data. It’s an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas.”
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Foyer is an open-source Python package, part of the MoSDeF suite of tools for molecular modeling. In the description: “a package for atom-typing as well as applying and disseminating forcefields.”
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ParmEd is an open-source python package for molecular modeling applications. In the description: “Cross-program parameter and topology file editor and molecular mechanical simulator engine.”
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Technical debt is an ongoing, ever-pressing issue to any large, collaborative code base. If left unaddressed, technical debt can seriously cripple productivity.
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This is my first post. I’m Alex. I’m from the Northern Virginia area. I like chemical engineering, chemistry, computer science, and scientific computing/data science.
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Some F@H data is freely accessible on AWS. This will be a relatively short post on accessing and navigating the data on AWS.
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“Open Babel is a chemical toolbox designed to speak the many languages of chemical data. It’s an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas.”
Published:
Foyer is an open-source Python package, part of the MoSDeF suite of tools for molecular modeling. In the description: “a package for atom-typing as well as applying and disseminating forcefields.”
Published:
ParmEd is an open-source python package for molecular modeling applications. In the description: “Cross-program parameter and topology file editor and molecular mechanical simulator engine.”
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(Last updated: 2019-07-31). This is an ongoing post as I work through a tutorial I found.
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Running a simulation means taking a model and sampling sort of distribution with it. 
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Let’s say you’ve conducted a simulation. Everything up to that point (parametrization, initialization, actually running the simulation) will be assumed and probably discussed another day. What you have from a simulation is a trajectory (timeseries of coordinates), and now we have to derive some meaningful properties from this trajectory.
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Let’s talk about molecular modeling from both the chemistry and mathematic standpoints. When you want to model something, what do you need?
Published:
This is my first post. I’m Alex. I’m from the Northern Virginia area. I like chemical engineering, chemistry, computer science, and scientific computing/data science.
Published:
The last post dealt with using pushshift and handling requests to access posts and comments from Reddit. This post deals with using the Python Reddit API wrapper to accces posts and comments from Reddit and then using some NLP tools for some basic sentiment analysis.
Published:
In light of recent internet trends about retail investors, I’m sure many of us have questions about the kinds of content that gets posted on reddit, and if there are home-grown, analytical ways of addressing these questions. I’ll be showing two ways of parsing submissions and comments to Reddit, this one focusing on using pushshift API endpoints using the requests library, some custom classes for processing these responses, and asyncio to handle asynchronous threading for multiple requests to pushshift.
Published:
Some F@H data is freely accessible on AWS. This will be a relatively short post on accessing and navigating the data on AWS.
Published:
What is poetry and where does this fit in the python software/DS ecosystem? And some beginner forays into docker.
Published:
Published:
Digital privacy is everywhere, and recent laws are pushing companies to disclose whatever personal information they may have on you. In the spirit of science, I’m going to make myself my own study subject and observe what Facebook has stored from my messenger history. Along the way, I’ll do some recursion, a little parallelization, some generators for data procesing, and basic visualization to observe my messenger behavior. Notebooks can be found here, but this one you can’t reproduce because I won’t be providing my messenger data (try this notebook on your own messenger data if you’re curious).
Published:
Published:
Published:
Published:
Published:
Does a team’s defensive scheme influence opponents’ shot portfolios? I’m going to be using a different NBA API to query all the games for the 2018-2019 regular season. In each game, I’m going to log each “make” against a team’s defense. For example, Houston makes a 22-ft 3-point shot against Boston, I will log the distance at which Houston scored against Boston.
Published:
Part 2 of evaluating fantasy NBA draft picks - modeling and sampling for expected fantasy output.
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Part 1 of evaluating fantasy NBA draft picks - first gathering the relevant data.
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This is my first post. I’m Alex. I’m from the Northern Virginia area. I like chemical engineering, chemistry, computer science, and scientific computing/data science.
Published:
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“Open Babel is a chemical toolbox designed to speak the many languages of chemical data. It’s an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas.”
Published:
Foyer is an open-source Python package, part of the MoSDeF suite of tools for molecular modeling. In the description: “a package for atom-typing as well as applying and disseminating forcefields.”
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Applying some principles from earlier mcmc posts/notebooks to estimate the parameters of a linear model
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ParmEd is an open-source python package for molecular modeling applications. In the description: “Cross-program parameter and topology file editor and molecular mechanical simulator engine.”
Published:
(Last updated: 2019-07-31). This is an ongoing post as I work through a tutorial I found.
Published:
Running a simulation means taking a model and sampling sort of distribution with it.
Published:
Technical debt is an ongoing, ever-pressing issue to any large, collaborative code base. If left unaddressed, technical debt can seriously cripple productivity.
Published:
Let’s say you’ve conducted a simulation. Everything up to that point (parametrization, initialization, actually running the simulation) will be assumed and probably discussed another day. What you have from a simulation is a trajectory (timeseries of coordinates), and now we have to derive some meaningful properties from this trajectory.
Published:
Let’s talk about molecular modeling from both the chemistry and mathematic standpoints. When you want to model something, what do you need?
Published:
This is my first post. I’m Alex. I’m from the Northern Virginia area. I like chemical engineering, chemistry, computer science, and scientific computing/data science.