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Code School Outcomes, Explained

Wednesday, April 21, 2021

How to evaluate software development training stats to make the right decision for your career

If you’re looking to train at one of the best code schools, you’ve probably encountered stats.

And you probably have some big questions.

After all, schools make big claims that they back up with numbers and percentages. And numbers always tell the truth, right?

Well… not exactly.

Understanding how stats work and how schools report on their results is crucial for calculating the potential return on your educational investment.

Transparency is key and good code schools want to share their graduate successes.

Where to Start

Here’s where to start when it comes to stats:

  • Assume that schools avoiding meaningful data are hiding bad outcomes.
  • Outcome data should be evergreen and fresh. Look for a track record of consistently providing outcome data. Avoid spotty outcomes based on one six-month period from two years ago. 
  • Especially for 2020, schools should be clear about how the pandemic has impacted outcomes. 
  • Look out for industry research masquerading as school outcomes. Reporting on the average salaries for working developers in a given city is not the same thing as the salaries people who graduated from that program in that market.
  • Be wary if a school only offers anecdotal outcomes based on a few individual stories. Feel good stories are great; they help us to picture ourselves in a similar way. But outcomes need to be statistically driven based on high reporting rates in order to support good decision making for your personal career development.

Stats Should Answer These Questions

Good outcome reporting should provide you with clear answers to the following questions:

  • Will I be able to obtain an in-field job after completing the training?
  • What are the details of the job such as the role, employment structure, and how much it pays?
  • How much time will I spend job seeking before I am hired and making money?

Starting Salaries

Let’s start with the most eye-catching statistic: starting salaries. Salary is important beyond a "starting" value because it indicates the type of role and likely career trajectory your salary will take over the next few years.

Median is better than average when it comes to salary data. Averages are prone to being skewed by a small number of very high salaries. The median value is the middle at which half the people are above and half below.

Don't accept an alternate statistic that uses average salary increase. It lacks context to understand how that would apply to you as an individual. From the median starting salary, everyone can calculate their relative increase. Why hide it?

Geography matters. Salaries are higher on the coasts than elsewhere in America, highest in the Bay Area and New York City, and rising in metropolitan areas with a strong tech scene. Even with the rise in remote work, it's important to know to which geographic market the outcome data refers.

Salary data is collected by surveying graduates. Check for participation rates that indicate broad collection of data. The lower the rate, the more likely the outcome is skewed to participants that self-select based on success.

Code schools differ on how and when they track earnings data as it pertains to interim work. Most people who go to code school are seeking the promise of full-time, salaried employment. Alchemy tracks and reports information about short-term contracts or apprenticeships prior to employment, but we find it more relevant to report on their salaried outcome (and to help them to get there).

Other code schools only report on the first work opportunity, even if it's only a temporary internship, and the amount of continued job seeking support may drop off accordingly.

Job Role and Title

Make sure you pick a training program that aligns with the type of role you want. If your goal is to be a developer who uses code to make software, avoid programs that place large numbers of graduates in tech-adjacent positions like technical support, QA/QC, or web producers (WordPress). Unless the employer specifically offers it, there is little opportunity to move into development from these roles.

Data on top job titles can be helpful, but one of the best indicators is the job salary data. Companies pay more for developers than for tech-adjacent roles.

Be cognizant of the type of employment and how interim work is reported. Doing short-term contracts, apprenticeships, being a teaching-assistant, or other temporary roles can be a great way to gain experience, but these are not as important as the full-time, salaried role you land as a result.

Job Placement Success

Knowing the placement rate of graduates into in-field jobs is essential in evaluating your likelihood of finding similar success. No code school is 100% effective for every person, but the percentage of graduates working in-field is indicative of your probability of finding success.

Find out if any criteria has been applied to exclude people from placement numbers. For example, many schools report 95%+ placement rates, but exclude a third or more of graduates who don't meet career services requirements. Non-responsive and non-seeking graduates are another category that will often be excluded. Look for low participation rates that may be skewing the numbers.

Make sure you understand the data and avoid erroneous conclusions about your likelihood of success.

Job Seeking

The average amount of time spent job seeking is important in planning your career transition and personal finances.

Many schools measure job seeking in the absolute time from graduation to working. Other schools, including Alchemy, report job seeking in time spent while unemployed. This differentiates between time spent doing interim work from unemployed time spent purely job seeking.

Ask the school about indicators for those who find jobs relatively quickly versus long job searches.

Have More Questions About Stats?

Or just more questions in general? Schedule a chat with our Admissions team!