Acceptance Criteria

1. The example uuid generation in main.go has tests in main_test.go
2. Test runner runs all tests when pushed
3. Code linter lints all code when pushed

Acceptance Criteria

1. Get data from college scorecard data api
2. Data is from all universities with degrees of types 2 and 3
3. Data contains the specific fields listed below

Specific fields to get

• School name
• School city
• 2018 student size
• 2017 student size
• 2017 amount of students over poverty line 3 years after completion
• 2016 3 year repayment

Create placeholder gui that does nothing probably using go-astilectron because I assume gui testing on electron will be better documented and easier.

Using Selenium or cypress or something else to do automated gui tests, perform E2E tests

Source: https://www.bls.gov/oes/#data

for each state get every major employment category and record
the state
the occupation major title
the total employment in that field in that state
the 25th percentile salary (lets assume that most college grads earn in the lower 25%) for that field both hourly and annual
the total employment in that state for that field oops typo, It should be the Occupation code instead

Fields to add

school.state
2016.repayment.repayment_cohort.3_year_declining_balance

Create at least one more table in your database with state by state employment data per the following:

for each state get every major employment category and record
the state
the occupation major title
the total employment in that field in that state
the 25th percentile salary (lets assume that most college grads earn in the lower 25%) for that field both hourly and annual
the Occupation code instead

AC:

not sure how to test this properly, going to ask dr.santore on this one.

write tests to assure your method to read from the xlsx file works properly
eg, make sure you get data from all 50 states from the original data
maybe create a test xlsx with a limited number of major occupational groups along with some other stuff. make sure you get the right number of major occupational groups
write a test to make sure the new table is there
write a test to make sure the old table is still there if test is still not there from previous sprint
write a test to make sure that your new write to table works (unless you found a way to make the old write to table work and the old test covers it all.

provide the user the ability to do two forms of data analysis
The first analysis should display the data in a color coded text format as a list in ascending or descending order (let the user choose)
The second should render a map to visualize the data.

compare the number of college graduates in a state (for the most recent year) with number of jobs in that state that likely expect a college education. (lets remove those that usually require a specialized school like police academies or apprenticeships). So lets remove all those professions which have an occ_code that begins with 30-39 or 40-49. (yes this is a broad brush, but we need to do this in 2 weeks)
comparing total jobs isn't going to be perfect since it isn't entry level jobs, but that data required a second API key
Compare the 3 year graduate cohort declining balance percentage to the 25% salary in the state and visualize that data

Prerequisite Issue #2

Acceptance Criteria

1. All data is stored in data.txt
2. Data is written concurrently with api calls

Currently there is a known rate limit of 1000 requests per two hours, but there is also another rate limit that only allows so many requests per x amount of seconds. that x amount of seconds is unknown.

AC

• no data lost to rate limiting

Add additional tests to your test suite. You should include tests to make sure your data analysis functions work properly (both forms)

Using plotly.go implement data visualization feature into a map of the US

compare the number of college graduates in a state (for the most recent year) with number of jobs in that state that likely expect a college education. (lets remove those that usually require a specialized school like police academies or apprenticeships). So lets remove all those professions which have an occ_code that begins with 30-39 or 40-49. (yes this is a broad brush, but we need to do this in 2 weeks)
comparing total jobs isn't going to be perfect since it isn't entry level jobs, but that data required a second API key
Compare the 3 year graduate cohort declining balance percentage to the 25% salary in the state and visualize that data

I tried to just add this into the other issues, but because I am very new at go it turned out to be challenging so I am separating it to its own issue.

AC:

• requests happen all at once
• writes to file in order of page number