In [1]:
import my_util as my_util
import cluster_skill_helpers as cluster_skill_helpers
from cluster_skill_helpers import *
In [2]:
HOME_DIR = 'd:/larc_projects/job_analytics/'; DATA_DIR = HOME_DIR + 'data/clean/'
RES_DIR = HOME_DIR + 'results/';
SKILL_DAT = DATA_DIR + 'skill_cluster/'; SKILL_RES = RES_DIR + 'skill_cluster/new/'
In [ ]:
df = pd.read_csv(SKILL_DAT + 'doc_index.csv')
In [ ]:
df = df.sort_values(['employer_name', 'doc'])
print('# posts bf filtering dups: %d' %df.shape[0])
In [ ]:
df.head(10)
In [ ]:
df = df.drop_duplicates(['employer_name', 'doc'])
print('# posts after filtering dups: %d' %df.shape[0])
In [ ]:
df.head(10)
In [ ]:
df = df.reset_index()
df.head()
In [ ]:
df.to_csv(SKILL_DAT + 'uniq_doc_index.csv', index=False)
A careful check also reveals a renaming of job title (from Marine Superintendent to Fleet Manager, employer: "K" LINE LOGISTICS SINGAPORE), though the posts are the same. This is also interesting and may need further analysis later. However, we first need to re-plot cluster distributions again to see if we really get rid of dups.
In [3]:
df = pd.read_csv(SKILL_DAT + 'uniq_doc_index.csv')
df = df.set_index('index')
In [8]:
with(open(LDA_DIR + 'doc_topic_distr.mtx', 'r')) as f:
doc_topic_distr = mmread(f)
In [4]:
# Global settings for all cluster plots
abbv_clusters = ['FIN', 'HWR', 'SCI', 'PROD', 'CON', 'LEG', 'CUS', 'LOG', 'MKT', 'DEV', 'MAN', 'HOS', 'AUD', 'COM', 'HR']
x = range(1, 16); labels = abbv_clusters
In [5]:
def getPosts2Check(job_title, industry, n_pair=10):
sub_df = df[(df['title'] == job_title) & (df['industry'] == industry)]
# Drop duplicated posts due to reposting
scores = calScore(sub_df, doc_topic_distr)
scores = scores.query('sim_score < 1').sort_values('sim_score', ascending=False)
k_pairs = scores.head(n_pair)
ids2check = np.unique(list(k_pairs['job_id1']) + list(k_pairs['job_id2']))
print('# posts to check: %d' %len(ids2check))
posts2check = sub_df[sub_df['job_id'].isin(ids2check)]
return posts2check.sort_values('employer_name')
# return k_pairs
In [6]:
def plotClusterDists(posts, figsize):
n_post = posts.shape[0]
fig, axarr = plt.subplots(n_post, sharex=True, figsize=figsize)
for r in range(n_post):
plt.subplot(n_post, 1, r+1)
plotClusterDistAtRow(r, posts, doc_topic_distr)
# Show ylabel at the middle
if r==(n_post/2 - 1):
plt.ylabel('Probability', fontsize=24)
## Fine tune the fig
fig.subplots_adjust(hspace=.5)
# Hide xticks on all subplots except the last one
hide_xticks(fig)
# Provide xtick labels only for the last subplot
plt.xticks(x, labels, rotation=45)
# Show xlabel only at the last subplot
plt.xlabel('Skill Clusters', fontsize=20)
return fig
In [10]:
posts2check = getPosts2Check(job_title="Software Engineer", industry="Financial and Insurance Activities", n_pair=5)
fig = plotClusterDists(posts2check, figsize=(6, 10))
# plt.savefig(LDA_DIR + 'fig/se_in_fin2.pdf')
plt.show(); plt.close()
Computing pair similarity scores among 31 job posts,
each post is compared with subseq posts...
0 posts and counting...
10 posts and counting...
20 posts and counting...
Done after 0.6s
# posts to check: 8
Duplications seem still exist! Let us examine further.
In [ ]:
sample_employers = ["Capital Match Holdings Pte. Ltd.", "Comfortdelgro Corporation Limited", "Fujitsu Asia Pte Ltd",
"Millennium Capital Management (Singapore) Pte. Ltd."]
sample_employers = map(str.upper, sample_employers)
sample_se_posts = df[(df.employer_name.isin(sample_employers)) & (df.title == "Software Engineer")]
se_fig = plotClusterDists(sample_se_posts)
plt.show(); plt.close()
In [ ]:
dups = df[(df.employer_name.isin(sample_employers)) & (df.title == "Software Engineer")]
dups = dups.sort_values('employer_name')
In [ ]:
dups.to_csv(RES_DIR + 'tmp/se_dups.csv', index=False)
dups.drop_duplicates(['doc']).to_csv(RES_DIR + 'tmp/se_no_dups2.csv', index=False)
By reading the job posts of Capital Match Holdings, we found that they are actually different. However, the differences in their contents are subtle. Cluster distribution of the last post is a bit different from the first two because the last one has no salary detail while the first two provide salary and equity. A closer look at the skill sets in these posts reveals:
In [ ]:
se_in_infocom = getPosts2Check(job_title='Software Engineer', industry='Information and Communications')
In [ ]:
fig = plotClusterDists(se_in_infocom)
plt.savefig(LDA_DIR + 'fig/se_in_infocom2.pdf')
plt.show(); plt.close()
In [ ]:
posts = getPosts2Check(job_title="Administrative Assistant", industry="Financial and Insurance Activities", n_pair=5)
In [ ]:
fig = plotClusterDists(posts, figsize=(6, 12))
# plt.savefig(LDA_DIR + 'fig/admin_in_fin.jpg')
plt.show(); plt.close()
In [ ]:
# Manager
posts = getPosts2Check(job_title="Manager", industry="Financial and Insurance Activities", n_pair=5)
In [ ]:
fig = plotClusterDists(posts, figsize=(6, 12))
plt.savefig(LDA_DIR + 'fig/man_in_fin.jpg')
plt.savefig(LDA_DIR + 'fig/man_in_fin.pdf')
plt.show(); plt.close()
These example tell us that we should also filter out the posts with are almost identical. Such posts can be detected based on the set of common skills (Jaccard similarity) as in the next section.
In [ ]:
cmh = df[df.employer_name == str.upper("Capital Match Holdings Pte. Ltd.")]
pairwiseJacSim(df=cmh)
This example confirms that posts under the same company can be very similar, the percentage of overlapping skills can be more than 90%. We want to remove such pairs of posts as they can be the reason consistency score are over-estimated.
The filtering process for a given DF of posts is follows.
In [38]:
def filterSimPosts(i, employer='Capital Match Holdings Pte. Ltd.', sim_thres=.9):
def doFilter(df):
keep, remain = [], df
t0 = time()
# While there are at least 2 posts then we need to check
while (remain.shape[0] >= 2):
res = jacSim2Others(0, remain)
job_id = remain.iloc[0]['job_id']
keep.append(job_id)
# In the remain, keep only posts that are sig. diff from post[0]
sig_diff_posts = list(res.query('jacSim < {}'.format(sim_thres))['job_id2'])
remain = remain[remain['job_id'].isin(sig_diff_posts)]
# If only 1 post remains then we can just keep it as there are no more posts too similar to it
if (remain.shape[0] == 1):
keep.append(remain.iloc[0]['job_id'])
print('\t done after {}s'.format(round(time()-t0, 1)))
return df[df['job_id'].isin(keep)]
sub_df = df[df.employer_name == employer.upper()]; n_post = sub_df.shape[0]
# if (i % 100 == 0):
print('\t{}, {}, {} posts'.format(i, employer, n_post))
return doFilter(sub_df)
In [12]:
tmp = filterSimPosts(0, employer='Capital Match Holdings Pte. Ltd.', sim_thres=.9)
pairwiseJacSim(tmp)
0, Capital Match Holdings Pte. Ltd., 5 posts, 0.0s elapsed
Computing pairwise Jaccard similarities among 3 job posts,
each post is compared against its subseq posts...
Done after 0.0s
Out[12]:
employer
intersection_size
jacSim
job_id1
job_id2
skills1
skills2
union_size
0
CAPITAL MATCH HOLDINGS PTE. LTD.
7
0.44
JOB-2015-0198405
JOB-2015-0198229
{referrals, investors, credit, institutional i...
{web, investors, institutional investors, capi...
16
1
CAPITAL MATCH HOLDINGS PTE. LTD.
5
0.29
JOB-2015-0198405
JOB-2015-0256490
{referrals, investors, credit, institutional i...
{investors, processing, competitive, banking, ...
17
2
CAPITAL MATCH HOLDINGS PTE. LTD.
5
0.28
JOB-2015-0198229
JOB-2015-0256490
{web, investors, institutional investors, capi...
{investors, processing, competitive, banking, ...
18
In [ ]:
tmp = filterSimPosts(1, employer='Apar Technologies Pte. Ltd.', sim_thres=.9)
pairwiseJacSim(tmp)
In [13]:
by_employer = df.groupby('employer_name').agg({'job_id': 'nunique'})
by_employer = by_employer.add_prefix('n_').reset_index()
# by_employer.n_job_id.describe().round(1)
by_employer = by_employer.sort_values('n_job_id')
employers = list(by_employer.query('n_job_id >= 2')['employer_name'])
len(employers)
Out[13]:
6838
In [14]:
print('Filtering too similar posts in {} companies...'.format(len(employers)))
Filtering too similar posts in 6838 companies...
In [15]:
def filterPosts4Employers(start=0, end=100):
t0 = time()
frames = [filterSimPosts(i+start, emp, sim_thres=.9) for i, emp in enumerate(employers[start:end])]
filtered_df = pd.concat(frames)
print('Done after %.1fs' %(time() - t0))
return filtered_df
In [16]:
filtered_df_1 = filterPosts4Employers(start=0, end=3000)
0, KIARATEX EXPORTS PTE LTD, 2 posts, 0.0s elapsed
100, KPS CONSTRUCTION & ENGINEERING PTE. LTD., 2 posts, 0.0s elapsed
200, TANESEI TRADING PTE. LTD., 2 posts, 0.0s elapsed
300, IT-GRID TECHNOLOGIES PTE. LTD., 2 posts, 0.0s elapsed
400, KALTIMEX ENERGY (SINGAPORE) PTE LTD, 2 posts, 0.0s elapsed
500, PRESTIGE BIOSYSTEMS PTE LTD, 2 posts, 0.0s elapsed
600, ST. ANDREW'S DENTAL SURGEONS GROUP PTE. LTD., 2 posts, 0.0s elapsed
700, MEGAWAY ENGINEERING & TRADING PTE LTD, 2 posts, 0.0s elapsed
800, NELCO PRODUCTS PTE. LTD., 2 posts, 0.0s elapsed
900, LYS ENERGY SOLUTIONS PTE. LTD., 2 posts, 0.0s elapsed
1000, SINOHYDRO-SEMBCORP JOINT VENTURE, 2 posts, 0.0s elapsed
1100, ELTEK POWER PTE. LTD., 2 posts, 0.0s elapsed
1200, HTE (S) PTE. LTD., 2 posts, 0.0s elapsed
1300, BANFF CYBER TECHNOLOGIES PTE. LTD., 2 posts, 0.0s elapsed
1400, AYA FUJI INTERNATIONAL (M & E) PTE LTD, 2 posts, 0.0s elapsed
1500, ESPZEN PTE. LTD., 2 posts, 0.0s elapsed
1600, BRADY ENGINEERING PTE. LTD., 2 posts, 0.0s elapsed
1700, ALLIANCE HEALTHCARE GROUP PTE LTD, 2 posts, 0.0s elapsed
1800, C P WORLD PTE. LTD., 2 posts, 0.0s elapsed
1900, SINGAPORE INSTITUTE OF MANAGEMENT, 3 posts, 0.0s elapsed
2000, SOUTHERN GLOBE CORPORATION PTE. LTD., 3 posts, 0.0s elapsed
2100, NET SOFTWARE TECHNOLOGY PTE. LTD., 3 posts, 0.0s elapsed
2200, DELUGE FIRE PROTECTION (S.E.A.) PTE LTD, 3 posts, 0.0s elapsed
2300, DAVID WOOD & ASSOCIATES PTE. LTD., 3 posts, 0.0s elapsed
2400, SHEVS IFT CONSULTANTS PTE. LTD., 3 posts, 0.0s elapsed
2500, COLUMBIA SHIPMANAGEMENT (SINGAPORE) PTE LTD, 3 posts, 0.0s elapsed
2600, HRNET ONE PTE LTD, 3 posts, 0.0s elapsed
2700, AVA INSURANCE AGENCY PTE. LTD., 3 posts, 0.0s elapsed
2800, THE BANK OF NOVA SCOTIA, 3 posts, 0.0s elapsed
2900, J. F. HILLEBRAND SINGAPORE PTE. LTD., 3 posts, 0.0s elapsed
Done after 27.0s
In [17]:
filtered_df_2 = filterPosts4Employers(start=3000, end=5000)
3000, LIAN SIONG ALUMINIUM & TRADING PTE. LTD., 4 posts, 0.0s elapsed
3100, ANIMAL RECOVERY VETERINARY CENTRE PTE. LTD., 4 posts, 0.0s elapsed
3200, UHP TECHNOLOGY PTE. LTD., 4 posts, 0.0s elapsed
3300, SWEE HUP ENGINEERING RESOURCES PTE LTD, 4 posts, 0.0s elapsed
3400, SOCIETY FOR WINGS, 4 posts, 0.0s elapsed
3500, FAGERDALA SINGAPORE PTE LTD, 4 posts, 0.0s elapsed
3600, SINGAPORE SAIZERIYA PTE. LTD., 4 posts, 0.0s elapsed
3700, NATIONAL HEALTHCARE GROUP PHARMACY, 4 posts, 0.0s elapsed
3800, D.PBU PTE. LTD., 5 posts, 0.0s elapsed
3900, GENESYS TELECOMMUNICATIONS LABORATORIES ASIA PTE LTD, 5 posts, 0.0s elapsed
4000, PULSESYNC PTE. LTD., 5 posts, 0.0s elapsed
4100, LOTUS ASIA TOURS PTE. LTD., 5 posts, 0.0s elapsed
4200, SHOPPY PTE. LTD., 5 posts, 0.0s elapsed
4300, SHIN KHAI CONSTRUCTION PTE. LTD., 6 posts, 0.0s elapsed
4400, DNATA SINGAPORE PTE. LTD., 6 posts, 0.0s elapsed
4500, APP SYSTEMS SERVICES PTE. LTD., 6 posts, 0.0s elapsed
4600, BLUM & CO PTE LTD, 6 posts, 0.0s elapsed
4700, GATEWAY TECHNOLOGIES SERVICES PTE LTD, 7 posts, 0.0s elapsed
4800, SINGAPORE ASSOCIATION OF THE VISUALLY HANDICAPPED, 7 posts, 0.0s elapsed
4900, GRAPHENE SERVICES PTE. LTD., 7 posts, 0.0s elapsed
Done after 57.3s
In [18]:
filtered_df_3 = filterPosts4Employers(start=5000, end=6000)
5000, NOOR HOLDINGS PTE. LTD., 8 posts, 0.1s elapsed
5100, YOUNG WOMEN'S CHRISTIAN ASSOCIATION OF SINGAPORE, 8 posts, 0.1s elapsed
5200, TAYLOR & FRANCIS ASIA PACIFIC, 9 posts, 0.1s elapsed
5300, VINTEDGE PTE LTD, 9 posts, 0.1s elapsed
5400, KGI FRASER SECURITIES PTE. LTD., 10 posts, 0.1s elapsed
5500, PROCTER & GAMBLE EUROPE SA SINGAPORE BRANCH, 10 posts, 0.1s elapsed
5600, SPARKY ANIMATION PTE. LTD., 11 posts, 0.1s elapsed
5700, SULZER CHEMTECH PTE. LTD., 12 posts, 0.1s elapsed
5800, KEPPEL SEA SCAN PTE LTD, 13 posts, 0.2s elapsed
5900, FIRMENICH ASIA PTE LTD, 15 posts, 0.2s elapsed
Done after 99.0s
In [23]:
# Defensive save
filtered_df.to_csv(SKILL_DAT + 'filter_doc_index.csv', index=False)
In [27]:
filtered_df_4 = filterPosts4Employers(start=6000, end=len(employers)-5)
6000, SWISS REINSURANCE COMPANY LTD, 16 posts, 0.2s elapsed
6100, LEAP NETWORKS PTE. LTD., 18 posts, 0.2s elapsed
6200, JOHNSON CONTROLS HOLDING (S) PTE. LTD., 20 posts, 0.3s elapsed
6300, SG REHAB PTE LTD, 24 posts, 0.2s elapsed
6400, ALEXANDER MANN BPO SOLUTIONS (SINGAPORE) PTE. LTD., 29 posts, 0.6s elapsed
6500, LUCASFILM ANIMATION COMPANY SINGAPORE B.V., 37 posts, 0.9s elapsed
6600, PARKROYAL KITCHENER HOTEL PTE. LTD., 48 posts, 1.3s elapsed
6700, ON DEMAND AGILITY SOLUTIONS PTE. LTD., 72 posts, 3.2s elapsed
6800, ADECCO PERSONNEL PTE LTD, 210 posts, 27.2s elapsed
Done after 3510.4s
In [ ]:
filtered_df_6 = filterPosts4Employers(start=len(employers)-5, end=len(employers)-4)
6833, DBS BANK LTD., 644 posts
In [39]:
filtered_df_5 = filterPosts4Employers(start=len(employers)-4, end=len(employers))
6834, MACHSPEED HUMAN RESOURCES PTE. LTD., 664 posts
done after 171.1s
6835, STANDARD CHARTERED BANK, 668 posts
done after 263.7s
6836, NATIONAL UNIVERSITY OF SINGAPORE, 1255 posts
done after 946.2s
6837, NANYANG TECHNOLOGICAL UNIVERSITY, 1292 posts
done after 928.0s
Done after 2309.1s
In [40]:
filtered_df = pd.concat([filtered_df_1, filtered_df_2, filtered_df_3, filtered_df_4, filtered_df_5, filtered_df_6])
print filtered_df.shape
filtered_df = filtered_df.reset_index()
filtered_df.to_csv(SKILL_DAT + 'filter_doc_index.csv', index=False)
(70723, 8)
In [58]:
df = df.dropna()
df.to_csv(SKILL_DAT + 'uniq_doc_index.csv', index=False)
In [61]:
dbs = df[df.employer_name == 'DBS BANK LTD.']
In [55]:
dbs.iloc[541]
# dbs = dbs.drop(dbs.index[541])
Out[55]:
doc support the roll out and monitoring of best pr...
job_id JOB-2015-0147783
title Information Technology Specialist
n_skill 0
occur_skills NaN
employer_id 196800306E
employer_name DBS BANK LTD.
industry Financial and Insurance Activities
Name: 99655, dtype: object
In [62]:
for r, skill_str in enumerate(dbs.occur_skills):
print((r, skill_str.split()))
(0, ['change,documentations,processing,trends,troubleshooting,vulnerability,change', 'management,team', 'work,information', 'security', 'standards'])
(1, ['awareness,benefits,cluster,coaching,crisis,deals,deposits,engagement,enquiries,equity,expert,feedback,investment,investments,ipo,legal,offshore,onshore,performing,probate,regulatory,revenue,risk,rules,targets,treasury,trust,wealth,asset', 'allocation,fiduciary', 'services,investment', 'products,investment', 'sales,process', 'enhancement,process', 'improvement,product', 'development,sales', 'management,structured', 'notes,unit', 'trusts,sales', 'and', 'marketing'])
(2, ['consultant,mlc,murex,vendor,back', 'office,best', 'practices,corrective', 'actions,front', 'office,production', 'support,release', 'management,system', 'maintenance,technical', 'support,user', 'acceptance', 'testing'])
(3, ['analytics,change,communicator,competitive,css,dashboards,deliverables,developers,driving,efficiency,flexibility,internet,optimizations,targets,trends,web,websites,business', 'units,customer', 'engagement,delivering', 'results,landing', 'pages,product', 'launches,sales', 'management,search', 'engines,online', 'user', 'experience'])
(4, ['advisory,assurance,awareness,committees,courses,expert,levels,policy,protection,reputation,risk,sanction,sanctions,trends,compliance', 'training,senior', 'management'])
(5, ['deliverables,performing,productivity,rollout,strength,customer', 'experience,technical', 'support'])
(6, ['credit,director,negotiations,security,supervision,client', 'coverage,financial', 'statements,operational', 'risk', 'management,strong', 'analytical', 'skills'])
(7, ['architectural,forecasting,infrastructure,conceptual', 'design,design', 'specifications,technical', 'architecture,technical', 'design'])
(8, ['audit,efficiency,feedback,health,investigation,levels,performing,productivity,rates,turnaround,cash', 'handling,customer', 'satisfaction,customer', 'service,regulatory', 'compliance,security', 'audit'])
(9, ['completion,wealth,customer', 'satisfaction,customer', 'service,investment', 'products,regulatory', 'compliance,sales', 'process,security', 'audit'])
(10, ['accountability,culture,performing,productivity,profitability,regulatory,security,selling,internal', 'audit,service', 'delivery'])
(11, ['accountability,culture,performing,productivity,regulatory,security,selling,targets,internal', 'audit,regulatory', 'requirements,sales', 'performance,service', 'delivery'])
(12, ['bau,deliverables,hpqc,impact,provisioning,rollout,scheduling,service', 'delivery,testing', 'tools'])
(13, ['accountability,acting,culture,motivation,profitability,revenue,security,selling,customer', 'satisfaction,internal', 'audit,regulatory', 'requirements,training', 'development'])
(14, ['referrals,validation,corporate', 'insurance,value', 'proposition'])
(15, ['aml,audit,cft,change,controls,efficiency,financing,governance,kyc,regulatory,sanctions,tax,terrorism,uat,customer', 'service,fraud', 'management,front', 'office,retail', 'banking,anti', 'money', 'laundering,operational', 'risk', 'management,user', 'acceptance', 'testing'])
(16, ['asset,custody,efficiency,impact,levels,metrics,presentation,questionnaires,regulatory,reviews,securities,sla,transitions,trends,business', 'launch,customer', 'experience,due', 'diligence,fiduciary', 'services,global', 'markets'])
(17, ['credit,database,reviews,sampling,tracking,administrative', 'support,credit', 'review,daily', 'operations,financial', 'data,management', 'reporting,trend', 'analysis'])
(18, ['boost,collaboration,culture,dynamic,editorial,exercises,feedback,impact,profitability,reviews,rewards,ups,wealth,best', 'practices,brand', 'building,business', 'analytics,client', 'communication,client', 'engagement,market', 'data,private', 'banking,team', 'work'])
(19, ['bonds,cash,custodians,entitlements,event,liaison,processing,reconciliation,rms,securities,stock,corporate', 'actions,tax', 'reporting'])
(20, ['cash,custodians,escalation,event,liaison,processing,reconciliation,rms,securities,stock,treatment,verification,tax', 'reporting,team', 'work'])
(21, ['audit,documentations,risk,syndicate,trends,metrics', 'reporting,risk', 'mitigation'])
(22, ['deliverables,deployment,infrastructure,optimisation,pmo,tracking,requirements', 'gathering'])
(23, ['automation,deployment,designing,hardware,infrastructure,java,security,application', 'development,application', 'servers,application', 'support,continuous', 'integration,data', 'protection'])
(24, ['collateral,competitive,controlling,controls,credit,derivatives,financing,integrity,legal,margin,regulations,regulatory,risk,stress,trends,underwriting,valuation,approval', 'process,credit', 'risk,portfolio', 'risk,senior', 'management,stress', 'testing,board', 'of', 'directors'])
(25, ['credit,proactive,reviews,scorecard,sound,targets,turnaround,uat,underwriting,credit', 'risk,risk', 'management,sme', 'banking'])
(26, ['automation,pricing,rates,reserves,trading,upgrades,yield,project', 'implementation'])
(27, ['automation,pricing,rates,reserves,trading,upgrades,valuation,yield,approval', 'process,project', 'implementation'])
(28, ['administrative,policy,statistics,client', 'service,power', 'point,service', 'delivery,service', 'providers,transaction', 'processing,process', 'improvement', 'projects'])
(29, ['asset,contract,contracts,expenses,gps,minutes,pmc,pmo,presentation,procurement,sharepoint,vendor,workshops,project', 'managers'])
(30, ['communications,messaging,pitching,scorecard,selection,strategy,business', 'planning,digital', 'banking,operational', 'excellence,strategic', 'initiatives'])
(31, ['administrative,collateral,consolidation,database,filing,logging,administrative', 'functions,data', 'sheets,user', 'acceptance', 'testing'])
(32, ['administrative,collateral,consolidation,database,filing,logging,administrative', 'functions,data', 'sheets,user', 'acceptance', 'testing'])
(33, ['bancassurance,campaigns,sound,product', 'development'])
(34, ['credit,database,workstream,coordinate', 'meetings,data', 'governance'])
(35, ['commercial,digital,fit,negotiations,sme,business', 'strategy,sme', 'banking'])
(36, ['audit,collections,infrastructure,risk,best', 'practices,continuous', 'improvement,gap', 'analysis,operation', 'management,process', 'improvement'])
(37, ['administration,emergency,engagement,fax,hardware,inventory,liaison,property,security'])
(38, ['cash,liquidity,traders,trading,interest', 'rates'])
(39, ['efficiency,tracking,business', 'units,effective', 'communication'])
(40, ['architecture,dashboards,deliverables,legal,presentations,financial', 'reporting,senior', 'management,team', 'lead'])
(41, ['commodity,initiative,pricing,risk,structuring,risk', 'management'])
(42, ['bau,change,controlling,mitigation,risk,business', 'case,functional', 'specifications,project', 'communications,project', 'delivery,project', 'manager,requirements', 'gathering'])
(43, ['change,conducting,deliverables,developers,engagement,governance,interpreting,retail,wholesale,business', 'requirements,process', 'improvement,senior', 'management,stakeholder', 'engagement,team', 'lead,workshop', 'facilitation,user', 'acceptance', 'testing'])
(44, ['analyses,enquiries,feedback,interventions,learning,partnerships,proactive,human', 'resources,programme', 'design,training', 'facilitation,learning', 'and', 'development'])
(45, ['efficiency,enquiries,financials,mis,processing,reviews,business', 'units,functional', 'specifications,ms', 'access,operating', 'systems,risk', 'control,technical', 'support'])
(46, ['analytics,budgeting,cadence,conducting,contracts,deliverables,indonesia,leadership,parties,partnerships,strategy,tracking,big', 'data,operations', 'management,project', 'management,strategic', 'alliances,strategy', 'development,strategy', 'execution,operational', 'due', 'diligence'])
(47, ['cash,channel,feedback,processing,trends,client', 'service,customer', 'satisfaction,trade', 'services'])
(48, ['assessment,budgeting,capital,funding,liquidity,mis,presentation,pricing,risk,sourcing,business', 'analytics,decision', 'making,institutional', 'banking,portfolio', 'management,quantitative', 'analysis'])
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(634, ['asset,campaigns,credit,innovation,revenue,sme,workflow'])
(635, ['abs,competitive,positioning,reviews,strategy,tracking,trends,balanced', 'scorecard,senior', 'management'])
(636, ['awareness,competitive,feedback,gts,health,regulatory,revenue,risk,structures,turnaround,business', 'analytics,cross', 'border,customer', 'experience,increase', 'productivity,risk', 'management,trade', 'finance,trade', 'operations,trade', 'sales,transaction', 'services'])
(637, ['bau,budgets,contract,eps,outsourcing,payroll,recruitment,roadmap,vendor'])
(638, ['css,designs,html,illustrator,interaction,javascript,omnigraffle,photoshop,trends,wireframes,agile', 'scrum,interaction', 'design,rapid', 'prototyping,ui', 'design,visual', 'design,wire', 'framing,human', 'computer', 'interaction'])
(639, ['currency,funding,structural,structuring,swaps,treasury,accounting', 'policy,approval', 'process,cross', 'border,fx', 'hedging,hedge', 'accounting,transfer', 'pricing'])
(640, ['advisory,commentary,competitive,credit,feedback,hybrids,interpretation,investment,legal,pricing,rates,trends,writing,corporate', 'actions,due', 'diligence,financial', 'goals,product', 'launches'])
(641, ['advisory,assurance,awareness,controls,enquiries,expert,groups,oversight,policy,regulations,statutory,tax,international', 'tax,senior', 'management,tax', 'audits,tax', 'law,transfer', 'pricing'])
(642, ['accruals,audits,capital,coordination,deliverables,deposits,financials,forecast,landlord,landlords,metrics,reserves,reviews,scm,tax,wip,accounts', 'payable,balance', 'sheet,decision', 'making,financial', 'modelling,fixed', 'assets,real', 'estate,transfer', 'pricing'])
(643, ['analytical,audits,capital,dashboard,deliverables,forecast,forecasting,formulation,integrity,liaison,methodology,metrics,noc,rates,statistics,tax,balance', 'sheet,cash', 'flow,financial', 'advisory,space', 'management,transfer', 'pricing'])
In [24]:
sample_employers = ["Capital Match Holdings Pte. Ltd.", "Comfortdelgro Corporation Limited", "Fujitsu Asia Pte Ltd",
"Millennium Capital Management (Singapore) Pte. Ltd."]
In [25]:
def plotSamplePosts(employers, df, figsize):
sample_employers = map(str.upper, employers)
sample_posts = df[(df.employer_name.isin(sample_employers)) & (df.title == "Software Engineer")]
return plotClusterDists(sample_posts, figsize)
In [26]:
fig = plotSamplePosts(sample_employers, df=filtered_df, figsize=(6, 8))
plt.savefig(LDA_DIR + 'fig/se_in_fin3.jpg')
plt.savefig(LDA_DIR + 'fig/se_in_fin3.pdf')
plt.show(); plt.close()
In [ ]:
filtered_df.shape[0]
In [ ]:
# Statistics check
In [ ]:
posts = pd.read_csv(DATA_DIR + 'full_job_posts.csv')
print posts.shape
posts.head()
In [ ]:
jd_df = pd.read_csv('d:/larc_projects/job_analytics/data/raw/jd.csv')
print jd_df.shape
jd_df.head()
In [ ]:
tmp = pd.merge(posts, jd_df)
tmp = tmp.drop_duplicates()
tmp.shape
In [ ]:
tmp = tmp.drop_duplicates(['employer_name', 'job_description_clob'])
tmp.shape
Content source: musketeer191/job_analytics
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