Introduction

A well-designed data-driven culture is optimized for transforming data into new knowledge. Organizational leaders own responsibility for enabling that optimization. Organizational members own responsibility for engineering that optimization. Among the many valuable products any startup, or other organization, can offer, is new knowledge.

Knowledge, especially new knowledge, is the goal. Before the data-driven leader can build a data-driven culture, we have to know what a data-driven culture is. The leader, in collaboration with organizational members, must decide about what data-driven culture means. This guide offers definitions for the term “data-driven culture” and its constituent terms.

Data-driven leaders, their organizational, along…

TL;DR: This is a case study-based tutorial on how to see if a question has been asked and answered ~ fodder for the review of existing results. Sometimes also called a review of literature. Scroll below to the appendix showing selected findings on why college is ‘worth it.’

Introduction

The first step in the scientific process is often, ask a question. Or otherwise sometimes it is about defining a problem.

Usually, the second step in the scientific workflow is to look at whether anyone else has already asked and answered your question. …

Introduction

This article provides a cookbook on implementing measures of distance for quantifying similarity. The specific use case I propose for this cookbook is to look at a list of observations, then to pick one of those observations as a reference, and ultimately use distance measures to identify which observations are like the reference observation.

Using measures of distance to measure similarity is not novel. Under the hood, this math is an important component of clustering, factor analysis, component analysis, and other techniques.

Identifying observations that are like a reference observation is a useful exercise when your analytical goal is to…

Introduction

This article outlines four ideas that will help you add to or enhance your professional portfolio (optimized for programmers and data scientists). A well-planned portfolio that provides values to others and shows your skills will improve your career prospects.

Others have written about this topic. The advice out there before this article is good stuff. In preparing this article, I focused on thoughts, ideas, and suggestions that are less common.

The four specific tips are:

1. Make a Rosetta Stone,
2. Make a cheat sheet,
3. Write an article about software you dislike, and
4. Contribute to another project.

1) Make A Rosetta Stone

The original “Rosetta Stone” was…

TLDR: Over the years new and emerging independent consultants have asked, “can you look over this agreement I have with a potential client for me?” I say “no, but here are some questions to ask and some things to thinking about.” Below is a plan that can help you get the legal advice you might be seeking.

Introduction

For a small business owner, new consultant, and many startups, getting legal advice is usually easier and less costly than you might think. Below is a plan for how to make that happen.

I’ve followed this plan for legal advice in business, family…

TLDR: A look at four open-source software projects that support data scientists including the popular Pandas, an emerging Datasist, an R Package known as People Analytics Data (technically open data), and Pandas-Profiling. Also discusses reasons in favor of contributing to open-source software projects, including the opportunity to make the world a better place and to enhance or build knowledge and skills.

Introduction & Motivation

The best way to become a data scientist is to be a data scientist. The best way to get better at data science is to practice, practice, practice.

There are at least three smart reasons to make contributions to…

TLDR: This guide puts together a three-year data panel (also known as longitudinal data) using public data available from the US Department of Education. Use the techniques in this article to prepare data that you can analyze on your own. Tell me in the comments what other federal data sources I should build similar guides for next.

Introduction

Are you any of the following?

• Working for a client that needs to understand the landscape of higher education institutions throughout the United States?
• A researcher (graduate student, university faculty) looking to study higher education institutions?
• A current or aspiring data professional looking…