1) Building A Professional Portfolio Getting A Portfolio Started
Adding & Enhancing A Portfolio
Contribute To Other Projects2) Data Science & Machine Learning Making Fictional Data
(For testing, training, demonstration)
Automating Data Collection3) Career Advice Big Career Mistakes (Big Ones)
LinkedIn4) Data Culture Data Driven Culture?
What Is A Data Set?
Columns, Variables, Dimensions . . .5) Professional Writing Resources Common Writing Mistakes
Research Questions6) Personal Meets Professional Coming Out
Building a professional portfolio takes time and dedication. These articles provide advice on how to get started. Once started…
As I started writing this article I originally intended to share a few common method chains I use, myself. I don’t see others use them. And I had a hunch that they would maybe be useful to others.
Towards the end of drafting, I realized this has turned into a bit of an ode to Pandas. Do you ever find yourself spotting the poetry in code, too?
Below are three (and a half) method chains I couldn’t live without. I hope you’ll find them useful, too.
The default output on Pandas
describe() puts the data frame columns across the top…
Getting started with Pandas and Python can be an angsty experience. In my experience, I had grown to be an intermediate-advanced programmer in Stata. It was in the weeks after I submitted my dissertation to the PhD committee, but before my defense. I had a ton of time on my hands. Data analysis was over and writing also, done.
How did I spend the time after submitting but before my defense? For me this was a period of about six weeks. I started in on Python.
Years later I find myself hacking out solutions to things I once thought were…
Has a course platform ever asked you to contribute a course to their platform? Since producing tutorials, cookbooks, YouTube videos, LinkedIn content, and Udemy courses, a handful of emerging platforms have reached out my way asking if I will contribute courses to their platform.
At first, I was not sure how to respond. I had fears and worries.
What if I inadvertently share code that destroys a corporate network via one of my courses? I’ll be ruined.
This is an example of catastrophizing.
Of course, what are the chances of a catastrophe like that? They’re low. But, that doesn’t change…
Often, in my data science work I often point clients and partners to a family of software that bridges the world between qualitative and quantitative analysis.
We sometimes know these packages as qualitative data analysis software (QDAS). They’re numerous. And in my opinion the qualitative moniker is a misnomer. Under the hood, for many of these packages, is a robust set of quantitative algorithms and capabilities.
I am also a fan of the word plays many of these packages used to name themselves!
Without this family of software, it would be difficult for the data science or the analytical community…
Last Updated: March 27, 2021.
My goal is to provide readers with pleasant opportunity to learn about data science, data-related careers, and other professional or personal topics. In providing that visually and aesthetically pleasing experience I sometimes use a variety of images. I aim to provide attribution and credit. The information below provides additional information related to the images I use online.
“Via Design Pickle” — The Design Pickle service provides visual design services on a monthly subscription basis. The artists at Design Pickle provide subscribers with images, illustrations, graphics, and other related productions. Design Pickle subscribers own the productions…
This article uses fictional data, previously generated using the code in an earlier article to illustrate the k-nearest neighbors classification algorithm. Readers can use this article as a cookbook for executing classification algorithms with the k-nearest neighbors algorithms.
Both a Jupyter notebook and a YouTube instructional video accompany this article. I placed links to these additional resources at the end of the article.
This algorithm, k-nearest neighbors, is one of the simplest supervised machine learning algorithms available for classification. Frequently, the k-nearest neighbors algorithm performs as well as many other more sophisticated machine learning algorithm options.
The underlying principles at…
There are many ways to pursue a career in data science. This article covers six possibilities. Of all the possibilities two things to keep in mind. One, there are more than seven paths. Two, there is no right or wrong way to go about it.
From my frequent and ongoing discussions with other data professionals, I wanted to share a summary of common paths towards data science.
For example, I spent most of my career working in education. In my career, I have worked in the classroom. I taught abroad. Also, I moved into education administration before I finally transitioned…
After a recent article on the topic of sourcing federal data, in which I show how to use Python to automate the process of getting data from the US Department of Education (US DOE) and then assembling that data into a panel data set, I started getting questions.
Why would you go through the trouble of writing code for this? Wouldn’t it be faster to just download the files and then use point and click to assemble the data?
The answer is, it depends. I did an experiment. In executing this experiment I recorded myself as I used point and…