Some students have a calling towards a certain walk of life and questions of finance, opportunity costs, industry health, where to work, and so on, are simple distractions to an inexorable pursuit! This passion has likely been engrained early by some inspirational teacher, or family legacy, or some formative moment that predates by years interaction with Degreechoices or any other educational website.
A second less fortunate type of potential student, probably a much larger cohort, is not blessed with an obvious path. These students benefit from guidance, both in qualitative or descriptive terms – what is this career and what would I be doing in it? – and in quantitative terms.
One of our primary objectives at teamodeon is to provide quantitative guidance. This means curating data from numerous databases, generally from governmental or private data sources (with emphasis on the former), and outputting intuitive data sets. These data sets are meant to answer student questions, provide a basis for understanding career or degree prospects, and to provide comparative frameworks for approaching different career or degree choices.
We have recently released our first set of 5 widgets, meant to detail demand and salary performance. These widgets utilize data from:
Below I’ll give brief explanations of the purpose of each of these widgets and outline their data sources.
BLS and projectionscentral data is used to present the employment landscape for each career. Projected job growth is detailed and compared against the national average for context.
Further breakdowns are available by state, with key performance indicators like jobs per 1000, comparative per capita employment, and estimated future demand increases.
We use BLS, BEA, and payscale data to present salary info by state on nominal (the number on the paycheck) and real (adjusted for purchasing power) terms. Data is sourced from BLS and/or Payscale. BLS is the preferred source, as it pulls data directly from tax returns to determine salary information. Payscale is an interesting secondary source as it reports on salaries being offered in real time on job postings. Payscale is also more granular in terms of the positions covered, and so is often the only source of data on more specific or less common careers.
Payscale and BLS offer unique additional information, which is displayed depending on which source has been selected above.
We look at the historic salary and demand figures presented by BLS on a national and state level to show trends. As there is some overlap with the demand increase projections, this can be used to determine the initial accuracy of the demand increase estimates.
We offer two additional data sets from Payscale. The first measures salary by experience level, for each relevant degree type.
And the second shows which skills are desirable by potential employers, measured by average salary premiums.
Forgive the cliché, but the widgets above are just the tip of the iceberg, in two different ways. First, we have a lot of additional data projects set to launch in the coming months, most imminent being our scholarship database, which will offer students the ability to filter 1000s of scholarships by their preferred metrics – subject, demographic, GPA requirement, and the like. Second, these widgets are simply the first output from a large investment in developing a scalable and clean data architecture. We have already integrated massive quantities of data into our systems from numerous data sources, streamlined the data, defined the logic, and demonstrated the ease of calling and filtering varied data sets. We then process and structure the data in our central system before deploying to the website, either via widget blocks or API endpoints. This system has been built to easily update new data from our sources, which is generally released annually.
While we are up against entrenched competition, we know that many competitors are encumbered with outdated, legacy systems. Our ability to start our data project from scratch is a competitive advantage. Our investment now will pay operational dividends later, both in terms of resource usage, ease and quickness of data updates and improvements, and in supporting an innovative company culture, deployed to provide useful quantitative guidance to prospective students.