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* Qualification: For the purposes of brevity, administrative points have been removed
IPython.display.HTML('./gordon_source_df.html')
Directorate | Department | PositionName | CriteriaGroup | Row | AppraisalScoreWeight |
---|---|---|---|---|---|
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Discipline Specific Skills L3 | 25 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Impact and Influence L3 | 30 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Organisational Awareness L3 | 15 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Planning and Organising L3 | 30 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | ANALYTIC DRIVEN CULTURE | 20 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | DATA AUTOMATION | 20 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | DATA INSIGHT | 30 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | DATA REQUIREMENTS | 30 |
First, loading the model:
nlp = spacy.load('en_core_web_lg')
stop_words = {
"service", "delivery",
"function", "functions",
"orientation", "orientations",
"problem", "solving",
"cfadm", "cfpro", "cfuni", "cfsup", "cfart", "cfman", "cfart", "cftec",
"kpaa", "kpan",
"l1", "l2", "l3", "l4", "l5"
}
nlp.Defaults.stop_words |= stop_words
hr_df.RowVector = hr_df.Row.apply(
lambda row: nlp(row).vector
)
IPython.display.HTML('./gordon_source_wv_df.html')
Directorate | Department | PositionName | CriteriaGroup | Row | RowVector | AppraisalScoreWeight |
---|---|---|---|---|---|---|
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Discipline Specific Skills L3 | [-0.115235664, 0.094851844, -0.032811504, -0.1... | 25 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Impact and Influence L3 | [-0.185014, 0.27602965, -0.020265013, -0.01785... | 30 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Organisational Awareness L3 | [-0.0405184, 0.15518801, 0.110339, 0.008534556... | 15 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | CFPRO: Planning and Organising L3 | [-0.03400433, 0.02099867, 0.016796663, -0.0964... | 30 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | ANALYTIC DRIVEN CULTURE | [-0.01125025, 0.105551496, 0.2284475, -0.08509... | 20 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | DATA AUTOMATION | [-0.24759, 0.0056599975, 0.28850502, 0.09628, ... | 20 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | DATA INSIGHT | [-0.107594505, 0.18723, -0.019495003, 0.2254, ... | 30 |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | DATA REQUIREMENTS | [0.006064996, -0.272295, -0.1181675, 0.003385,... | 30 |
Using centre of mass formula:
$$C = \frac{\sum_i^N{W_i X_i}}{\sum_i^N{W_i}}$$a few weighted averages later...
IPython.display.HTML('./gordon_cg_df.html')
Directorate | Department | PositionName | CriteriaGroup | CriteriaGroupVector |
---|---|---|---|---|
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | Competencies | [-0.10059217, 0.13609965, 0.00730747, -0.06769... |
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | KPA's | [-0.0822269, -0.0032772017, 0.062091753, 0.070... |
and a few more...
IPython.display.HTML('./gordon_position_df.html')
Directorate | Department | PositionName | PositionVector |
---|---|---|---|
CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | [-0.08773648, 0.038535856, 0.045656465, 0.0293... |
IPython.display.HTML('./hr_translation_I.html')
IPython.display.HTML('./hr_translation_II_na.html')
data_words = [
"data",
"gathering",
"processing",
"analysis",
"dissemination"
]
data_word_vectors = {
word: nlp(word.lower()).vector
for word in data_words
}
for word, word_vector in data_word_vectors.items():
score_df[f"{word.title()}Score"] = sklearn.metrics.pairwise.cosine_similarity(
numpy.vstack(score_df.PositionVector.values),
numpy.array([word_vector])
)
IPython.display.HTML('./data_score_df.html')
Directorate | Department | PositionName | DataScore | GatheringScore | ProcessingScore | AnalysisScore | DisseminationScore | |
---|---|---|---|---|---|---|---|---|
4869 | CORPORATE SERVICES | Organisational Performance Management | Principal Professional Officer: Data Sci | 0.861595 | 0.411872 | 0.603572 | 0.698752 | 0.495789 |
IPython.display.HTML('./na_data_scoring.html')
IPython.display.HTML('./data_scoring_comparison.html')
IPython.display.HTML('./data_scoring_pca.html')