Given a candidate resume and a query of desired role, identify the skill gap between the candidate and the desired role, then generate a unique learning path for the candidate.
This project is a testing playground that I used to record all the progress and how I solve the problems accordingly.
In order to efficiently extract skills out from the resume, use free online Emsi Skills api to get the list of available skills.
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Emsi Skills API
Example: calling the API endpoint using Python. The detailed documentation is given [Here](Link text Here)
url = "https://auth.emsicloud.com/connect/token" payload = "client_id=<CLIENT_ID>&client_secret=<CLIENT_SECRET>&grant_type=client_credentials&scope=emsi_open" headers = {'Content-Type': 'application/x-www-form-urlencoded'} response = requests.request("POST", url, data=payload, headers=headers) -
Skills Extraction from Resume
Example: extract all the skills from a given resume. The output will be in a Python set.
extract_skills(resume_text=resume_string, skills_api=skills_api, clean=True) # Output: # { # 'ada', # 'business administration', # 'coaching', # 'conflict resolution', # 'environmental quality', # 'labor law', # 'management', # 'mediation', # 'organizational development', # 'policy development', # ... # }
The output shown above is implicitly indicating that all the skills have the same experience level. This is undesirable as there are much more information in the resume that can be used to weigh the skills differently, e.g. project period.
In order to weigh each skill differently based on the project period, I first assumed that:
- All the experience levels are using
year(s)as unit. - All the skills extracted from the resume have at least 1 year of experience.
- The project description shall come after the project period.
- The year of experience is computed by taking the ceiling of the year difference, i.e.
$\text{difference in time} // \text{365 days} + 1$ - When there are multiple projects of different periods having the same skill, the experience level will be computed by
$max(\text{years of project 1}, \text{years of project 2}...)$ .
Example: extract all the sections out using date range as guidance from a resume.
experience_tagging(date_list)
# Output:
# key: year of experience
# value: list of tuples, where each tuple is (section start index, section end index)
# {10: [(981, 1066)], 4: [(1996, 3608)], 3: [(3635, -1)]}
I looped through each section and extract all the skills out, then label all these skills with the corresponding years of experience. For all the other skills that are found not within any section, we just tag the year of experience to be 1.
Example: extract all the skills with corresponding years from a resume. The output is in a Python dictionary.
skills_experience_level_identification(resume_string)
# Output:
# {
# 'reduction': 4,
# 'environmental quality': 4,
# 'teamwork': 4,
# 'management': 4,
# 'poultry': 4,
# 'strong work ethic': 4,
# 'business administration': 3,
# 'coaching': 3,
# 'conflict resolution': 3,
# 'workplace safety': 3,
# 'mediation': 3,
# 'organizational development': 3,
# 'session': 3,
# 'ada': 3,
# 'labor law': 1,
# 'policy development': 1
# }
We first assumed that all the candidates that are applying for a specific role must be qualified, i.e. they have all the skills required for the particular role. With this assumption, we can do aggregation for all the resumes that belong to the same category and find out the percentage of the resumes having certain skills. High percentage indicates that they are the common skills required for the particular role.
We then set the significance level
Example: We use the percentile method to set the threshold for classifying the skills that appear at least once, and decide if they are significant or not. Then, we scale the skills' frequency to the range of [0, 1]. This is to help us to decide whether if we need high level of skill competency to standout from peers. We save all the remaining skill with the corresponding competency level required into a dictionary
# Output: where the value is the competency level based on my client's business settings
# {'ADVOCATE':
# {'active listening': 3,
# 'administrative support': 3,
# 'advocacy': 3,
# 'agenda': 3,
# 'auditing': 3,
# 'authorization': 3,
# 'banking': 3,
# 'billing': 3,
# 'budgeting': 3,
# 'business administration': 3,
# 'business development': 3,
# ...
# },
# ...
# }
With the significant skills identified for each industry, we can now make comparison between your skillset and the skillset required for the particular industry. We can do that by first mapping the years of experience into pre-defined competency levels: 3 for entrant level, 4 for specialist level and 5 for expert level.
Example: The output will be a dictionary with skills as the keys and the skill gaps as the values
skill_gap_identification_peers(skills[31605080], skills_required['AVIATION'])
# Output:
# {'aircraft maintenance': [3],
# 'aviation': [4, 5],
# 'b': [3],
# 'business administration': [3],
# 'c': [3],
# 'construction': [3],
# 'critical thinking': [3],
# 'drawing': [3],
# 'filing': [3],
# ...
# 'supervision': [3],
# 'test equipment': [3],
# 'time management': [3],
# 'track': [3, 4],
# 'tracking': [3]
# }
Once we successfully found out the skill gap between an applicant with the job and peer standard, we can suggest the applicant to job and continuous learning programmes which help the applicant to fulfill the skill gap, and even outperforming the peers in the specific industry.
Example: We infer skills required in a vector form using Doc2Vec and find the top 5 unique most similar courses
# Output:
# {'job':
# {
# 3: [(7203,'Innovation and Entrepreneurship Capstone','3 - Entrant Level'), ...],
# 4: [(4602, 'Express Data Base Administrator', '4 - Specialist Level'), ...],
# 5: [(12502, 'Cyber Security Management Capstone Project','5 - Expert Level'), ...]
# },
# 'continuous learning':
# {
# 3: [(7203,'Innovation and Entrepreneurship Capstone','3 - Entrant Level'), ...],
# 4: [(4602, 'Express Data Base Administrator', '4 - Specialist Level'), ...],
# 5: [(12502, 'Cyber Security Management Capstone Project','5 - Expert Level'), ...]
# }
# }