in list columns
play

IN LIST COLUMNS Cases from an Online Recruitment Platform Omayma - PowerPoint PPT Presentation

Dec 17, 2022 •163 likes •1.04k views

FITTING HUMANS STORIES IN LIST COLUMNS Cases from an Online Recruitment Platform Omayma Said @OmaymaS The Leading Job Site in EGYPT 19 th Century Adolphe Quetelet 19 th Century THE AVERAGE MAN (Lhomme Moyen) Adolphe Quetelet THE

  1. FITTING HUMANS STORIES IN LIST COLUMNS Cases from an Online Recruitment Platform Omayma Said @OmaymaS

  2. The Leading Job Site in EGYPT

  3. 19 th Century Adolphe Quetelet

  4. 19 th Century THE AVERAGE MAN (L’homme Moyen) Adolphe Quetelet

  5. THE AVERAGE MAN Physical Weight, Height (Body Mass Index)

  6. THE AVERAGE MAN Social Marriage

  7. The AVERAGE MAN Moral Crimes

  8. For Quetelet THE AVERAGE MAN = PERFECTION

  9. “ If an individual at any given epoch of society possessed all the qualities of the AVERAGE MAN , he would represent all ” that is great, good, or beautiful. Adolphe Quetelet

  10. Who Is The “AVERAGE MAN” in Your Society?

  11. Are You Just a Deviant from The “AVERAGE MAN” ?

  12. Many Disagree !

  13. Now...

  14. Now... Tremendous Growth of Data

  15. Misuse of SUMMARY STATISTICS

  16. Misuse of SUMMARY STATISTICS

  18. Misuse of SUMMARY STATISTICS

  22. The Leading Job Site in EGYPT

  24. What Do We Optimize For? Quality Quantity Relevance Matching Jobs & Job Seekers

  25. Let’s talk about DATA KPIs METRICS

  26. “The average job seeker applies for N jobs per month” Me:

  27. “The average number of applications per job this month is GREAT” Me:

  28. What AVERAGE Do You Measure?

  29. Who is The AVERAGE Job Seeker?

  30. Can We Tell Better STORIES About Our Users?

  32. We can tell better stories with…. Contextual Effective Understanding + Data Analysis

  33. Contextual + Effective Understanding Data Analysis Culture Socioeconomic Status Market Dynamics

  34. Effective Contextual + Understanding Data Analysis Mindset Workflow Framework/Tools

  35. Contextual Effective + Understanding Data Analysis Culture Mindset Socioeconomic Status Workflow Market Dynamics Framework/Tools

  36. Contextual Understanding Contextual Understanding + Effective Data Analysis Effective Data Analysis = Better Stories

  37. Contextual Understanding Contextual Understanding + Effective Data Analysis Effective Data Analysis = Actionable Insights

  38. Framework/Tools + Compatible Packages https://speakerdeck.com/hadley/tidyverse

  39. The Tidyverse Let’s focus on Main Concepts

  40. Three Main Concepts Tidy Data by: @_inundata & @jcheng

  41. Three Main Concepts Tidy Data A variable in a column An observation in a row Tidy your data And here you go! [ tibble, tidyr , dplyr, and friends ]

  42. Data comes from different SOURCES And more...

  43. Data comes in different FORMATS And more...

  44. Data comes in different FORMATS DATAFRAME Read Tidy (TIBBLE)

  45. Tidy Data user job_id job_title company application_date Sara A1234 Software Developer Company A 2017-01-02 Sara A1568 Senior Software Company B 2017-03-02 Engineer Sara A1590 Software Engineer Company C 2017-03-03 …... ….. …. …. …. Omar A1234 Software Developer Company A 2017-01-03 Omar A1580 Android Developer Company C 2017-01-20 ….. …. …. …. …..

  46. Three Main Concepts Nested Data

  47. Three Main Concepts Nested Data One row per group Instead of One row per observation [ tidyr ]

  48. Nested Data user job_id job_title company application_date Sara A1234 Software Developer Company A 2017-01-02 Sara A1568 Senior Software Company B 2017-03-02 Engineer user applications Sara A1590 Software Engineer Company C 2017-03-03 Sara <Tibble [3 x 4]> …... ….. …. …. …. user_data %>% group_by(user) %>% Omar A1234 Software Developer Company A 2017-01-03 nest(.key = “applications”) Omar <Tibble [2 x 4]> Omar A1580 Android Developer Company C 2017-01-20 …. …... ….. …. …. …. …..

  49. Nested Data user job_id job_title company application_date Sara A1234 Software Developer Company A 2017-01-02 Sara A1568 Senior Software Company B 2017-03-02 Engineer Sara A1590 Software Engineer Company C 2017-03-03 job_id applications A1234 <Tibble [2 x 4]> …... ….. …. …. …. job_data %>% group_by(job_id) %>% Omar A1234 Software Developer Company A 2017-01-03 A1568 <Tibble [30 x 4]> nest(.key = “applications”) A1590 <Tibble [100 x 4]> Omar A1580 Android Developer Company C 2017-01-20 A1580 <Tibble [120 x 4]> ….. …. …. …. …..

  50. Three Main Concepts Functional Programming

  51. Three Main Concepts Functional Programming Handle iteration problems powerfully and emphasize the actions rather than the objects [ purrr ]

  52. Let’s store models in columns job_id applications app_count A5638 <tibble [362 x 27]> 362 A8957 <tibble [110 x 27]> 110 ….. ….. ….. job_app_data<- job_app_data %>% mutate(glm_model = map(app_data, ~ glm(viewed ~ app_day, data = .x, family = binomial)))

  53. Let’s store models in columns job_id applications app_count glm_model A5638 <tibble [362 x 27]> 362 <S3: glm> A8957 <tibble [110 x 27]> 110 <S3: glm> ….. ….. ….. …. job_app_data<- job_app_data %>% mutate(glm_model = map(app_data, ~ glm(viewed ~ app_day, data = .x, family = binomial)))

  54. Iterate and answer more questions user applications preferences Sara <tibble [2 x 10]> <tibble [4 x 10]> Omar <tibble [2 x 15]> <tibble [2 x 10]> ….. ….. …. user_data <- user_data %>% mutate(common_jobs = map2(applications, preferences, ~intersect(.x[[“job_title”],.y[[“job_title”]])

  55. Iterate and answer more questions user applications preferences common_jobs Sara <tibble [2 x 10]> <tibble [4 x 10]> <chr [2]> Omar <tibble [2 x 15]> <tibble [2 x 10]> <chr [0]> ….. ….. …. user_data <- user_data %>% mutate(common_jobs = map2(applications, preferences, ~intersect(.x[[“job_title”],.y[[“job_title”]])

  56. Let’s Look Closer !

  57. Problem Overall growth and good KPIs Shortage in applications for certain Software Development jobs

  58. Problem Shortage in applications for certain Software Development jobs Dissatisfied Employers

  59. Problem Shortage in applications for certain Software Development jobs Flagged by different sources

  60. Problem Shortage in applications for certain Software Development jobs Masked by high-level metrics

  62. Hypotheses Talent Shortage What if we just have a small pool of job seekers who are interested in the affected jobs?

  63. Hypotheses Irrelevant Jobs Maybe employers are not catching up with the global trends or job seekers aspirations!

  64. Hypotheses Hidden Jobs What if some jobs do not get enough exposure in the search/recommendation pages?

  65. Investigation The Job’s Side st

  66. The Job’s Side What about applications details per job?

  67. The Job’s Side Job applications details

  68. The Job’s Side What about iOS job applications?

  69. Job Applications Growth over time iOS Developers Jobs

  70. What happens to job posts on day X? iOS Developers Jobs Day 7

  71. What is special about these jobs? iOS Developers Jobs Mobile Developer ( iOS , Android )

  72. iOS Developers Jobs What about the rest?

  73. More with Shiny... *Sample of Wuzzuf Job Posts

  74. Investigation The Job Seeker’s Side nd

  75. The Job Seeker’s Side How do job seekers fill their profiles? tidytext

  76. The Job Seeker’s Side How do job seekers fill their profiles? Details of job s eeker’s keywords

  77. The Job Seeker’s Side What about the repetition in the extracted keywords?

  78. The Job Seeker’s Side What about the repetition in the extracted keywords? Summaries from Job Seeker's Keywords

  79. The Job Seeker’s Side Which jobs match each user’s profile? solrium

  80. The Job Seeker’s Side Which jobs match each user’s profile?

  81. The Job Seeker’s Side Which jobs match each user’s profile? Recommended Jobs Details

  82. What ACTIONS Did This Analysis Trigger?

  83. Recommended Actions Talent Shortage - Acquire more senior developers - Activate the existing developers - Support the community

  84. Recommended Actions Irrelevant Jobs - Advise employers about the market - Revisit preference-based matching

  85. Recommended Actions Hidden Jobs - Revisit text fields indexing - Tune field weights for scoring - Improve mail recommendation

  86. Main Concepts Tidy Data Nested Data Functional Programming Understanding + = Contextual Actionable Effective Data Analysis Insights @OmaymaS

  87. FITTING HUMANS STORIES IN LIST COLUMNS Cases from an Online Recruitment Platform Omayma Said @OmaymaS

Recommend

ITERATION AND LIST COLUMNS Jeff Goldsmith, PhD Department of Biostatistics 1 Why

ITERATION AND LIST COLUMNS Jeff Goldsmith, PhD Department of Biostatistics 1 Why

ITERATION AND LIST COLUMNS Jeff Goldsmith, PhD Department of Biostatistics 1 Why iterate You will frequently encounter problems where you need to the same basic thing a lot The dont write the same code more than twice

374 views • 15 slides
1 Agenda Agenda Agenda 1 Classical Columns 2 2 Digital Columns Digital Columns 3

1 Agenda Agenda Agenda 1 Classical Columns 2 2 Digital Columns Digital Columns 3

1 1 Agenda Agenda Agenda 1 Classical Columns 2 2 Digital Columns Digital Columns 3 Cylindrical Screens 4 Interactive Columns 5 Discussion 2 2 1. CLASSICAL COLUMNS 3 3 4 4 5 5 2. DIGITAL COLUMNS 6 6 DIGITAL COLUMNS

871 views • 48 slides
Monitoring Rheology as a Tool for Measuring the Effective Shear Rate in the Bubble Columns

Monitoring Rheology as a Tool for Measuring the Effective Shear Rate in the Bubble Columns

Monitoring Rheology as a Tool for Measuring the Effective Shear Rate in the Bubble Columns . Presenter: Veena Bobade V.Bobade 1 , J.C. Baudez 2 , Geoffrey Evans 3 , N. Eshtiaghi 1 1 RMIT University, Australia 2 LIST, Luxemburg. 3 The

562 views • 20 slides
The Periodic Table 1 Arranged into Columns called GROUPS or FAMILIES (the columns go up and

The Periodic Table 1 Arranged into Columns called GROUPS or FAMILIES (the columns go up and

The Periodic Table 1 Arranged into Columns called GROUPS or FAMILIES (the columns go up and down) and Rows called PERIODS (rows go right and left) 2 Groups numbered left to right 1 2 H He Hydrogen Helium in roman numerals 3 4 5

441 views • 17 slides
Example: List filter -&gt; (val ns (List withAll: (1 2 3 4 5))) List( 1 2 3 4 5 ) -&gt; (ns

Example: List filter -&gt; (val ns (List withAll: (1 2 3 4 5))) List( 1 2 3 4 5 ) -&gt; (ns

Example: List filter -&gt; (val ns (List withAll: (1 2 3 4 5))) List( 1 2 3 4 5 ) -&gt; (ns filter: [block (n) ((n mod: 2) = 0)]) List( 2 4 ) Classes codify different forms of data No interrogation about form! Design process still works 1.

412 views • 20 slides
BUILDING PAD FOOTINGS 1ST FLOOR COLUMNS SLAB ON GRADE 2ND FLOOR SLAB 2ND FLOOR COLUMNS ROOF

BUILDING PAD FOOTINGS 1ST FLOOR COLUMNS SLAB ON GRADE 2ND FLOOR SLAB 2ND FLOOR COLUMNS ROOF

BUILDING PAD FOOTINGS 1ST FLOOR COLUMNS SLAB ON GRADE 2ND FLOOR SLAB 2ND FLOOR COLUMNS ROOF SLAB EXTERIOR WALLS CEILING LININGS COLUMN LININGS INTERIOR WALLS FIRE PAVER FLOORS EXTERIOR OVERHAND SOFFIT PROTECTION RAILINGS, DOORS AND

302 views • 14 slides
12-1 Using an Array to Implement a List Space Management: Size vs. Capacity Idea: allocate

12-1 Using an Array to Implement a List Space Management: Size vs. Capacity Idea: allocate

Implementing a List in Java Two implementation approaches are most commonly used for simple lists: CSC 143 Java List via Arrays Linked list Java Interface List&lt;E&gt; concrete classes ArrayList, LinkedList List

510 views • 9 slides
twenty six concrete construction: columns &amp; frames http://www.building.co.uk Concrete

twenty six concrete construction: columns &amp; frames http://www.building.co.uk Concrete

A RCHITECTURAL S TRUCTURES : F ORM, B EHAVIOR, AND D ESIGN A RCH 331 D R. A NNE N ICHOLS S PRING 2018 lecture twenty six concrete construction: columns &amp; frames http://www.building.co.uk Concrete Columns 1 Architectural Structures

371 views • 12 slides
Self-Affirmation As A Stress BUffer Denice Higareda Context In two or three columns Stress

Self-Affirmation As A Stress BUffer Denice Higareda Context In two or three columns Stress

Self-Affirmation As A Stress BUffer Denice Higareda Context In two or three columns Stress Impairs Cognitive Function Carmen Sandi Things to Know In two or three columns Self-affirmation The act of acknowledging one's values

416 views • 40 slides
BEST Design of Steel Columns for Standard Cross-Sections Verification according to DIN

BEST Design of Steel Columns for Standard Cross-Sections Verification according to DIN

BEST Design of Steel Columns for Standard Cross-Sections Verification according to DIN and EN with national annexes for DE, AT, SK/CZ &amp; UK Modelling of multi-storey columns with different stepped cross-sections Use of

210 views • 6 slides
SOLID- -METAL THERMAL COLUMNS METAL THERMAL COLUMNS SOLID IN CONVENTIONAL PCBs IN CONVENTIONAL

SOLID- -METAL THERMAL COLUMNS METAL THERMAL COLUMNS SOLID IN CONVENTIONAL PCBs IN CONVENTIONAL

SOLID- -METAL THERMAL COLUMNS METAL THERMAL COLUMNS SOLID IN CONVENTIONAL PCBs IN CONVENTIONAL PCBs SMTA Jim Fraivillig Thermal Issues session Fraivillig Technologies Boston, MA Rosemount, IL 9/24/03 PowerVia technology PowerVia

520 views • 18 slides
How to use Table and Data Graph Advanced Skill 1. Select 3D table on the Object list, and add it.

How to use Table and Data Graph Advanced Skill 1. Select 3D table on the Object list, and add it.

How to use Table and Data Graph Advanced Skill 1. Select 3D table on the Object list, and add it. 2. Get a Table Node Setup Dialog. We can change Title, Navigation, Columns and Data on the dialog. 3. Click OK. We can get a default table

65 views • 4 slides
Silica Monolith Columns for Silica Monolith Columns for Ultra High Speed Separations Ultra High

Silica Monolith Columns for Silica Monolith Columns for Ultra High Speed Separations Ultra High

Silica Monolith Columns for Silica Monolith Columns for Ultra High Speed Separations Ultra High Speed Separations Outline Outline Do more in less time Onyxtechnology &amp; advantages Product offerings Applications

447 views • 24 slides
Introduction to Beamer Ana Hadavi and Eliana Alvarez February 16, 2015 Ana Hadavi and Eliana

Introduction to Beamer Ana Hadavi and Eliana Alvarez February 16, 2015 Ana Hadavi and Eliana

Introduction Columns Inserting Pictures Math Mode Blocks Pictures List Overlays Introduction to Beamer Ana Hadavi and Eliana Alvarez February 16, 2015 Ana Hadavi and Eliana Alvarez Introduction to Beamer Introduction Columns Inserting

546 views • 26 slides
seventeen pipe built-up steel construction: columns &amp; tension members Steel Columns

seventeen pipe built-up steel construction: columns &amp; tension members Steel Columns

A RCHITECTURAL S TRUCTURES : Structural Steel F ORM, B EHAVIOR, AND D ESIGN A RCH 331 standard rolled shapes Cor-Ten Steel Sculpture By Richard Serra Museum of Modern Art Fort Worth, TX D R. A NNE N ICHOLS (AISC - Steel Structures of the

950 views • 6 slides
twenty pipe built-up steel construction: columns &amp; tension members Steel Columns

twenty pipe built-up steel construction: columns &amp; tension members Steel Columns

A RCHITECTURAL S TRUCTURES : Structural Steel F ORM, B EHAVIOR, AND D ESIGN ARCH 331 standard rolled shapes Cor-Ten Steel Sculpture By Richard Serra Museum of Modern Art Fort Worth, TX D R. A NNE N ICHOLS (AISC - Steel Structures of the

448 views • 6 slides
twenty one transfer load subjected to tension or compression steel construction:

twenty one transfer load subjected to tension or compression steel construction:

A RCHITECTURAL S TRUCTURES : Connections F ORM, B EHAVIOR, AND D ESIGN ARCH 331 needed to: D R. A NNE N ICHOLS support beams by columns S PRING 2018 connect truss members lecture splice beams or columns twenty one transfer

470 views • 8 slides
Aggregation An aggregate function in the [columns] clause computes a value from a group of rows

Aggregation An aggregate function in the [columns] clause computes a value from a group of rows

Aggregate Functions So far, all SQL expressions have referred to the values in a single row at a time [expression] as [name], [expression] as [name], ... select [columns] from [table] where [expression] order by [expression]; Aggregation An

452 views • 4 slides
WHAT IF? Columns run vertically &amp; are identified by letters A, B, etc. Rows run

WHAT IF? Columns run vertically &amp; are identified by letters A, B, etc. Rows run

An application program that can quickly handle calculations A spreadsheet uses numbers like a word processor uses words. WHAT IF? Columns run vertically &amp; are identified by letters A, B, etc. Rows run horizontally &amp; are

548 views • 24 slides
Seismic Performance of Seismic Performance of Reinforced Concrete Columns Reinforced Concrete

Seismic Performance of Seismic Performance of Reinforced Concrete Columns Reinforced Concrete

Seismic Performance of Seismic Performance of Reinforced Concrete Columns Reinforced Concrete Columns Under Biaxial Excitation Under Biaxial Excitation Tiffany McCarthy Dept. of Civil &amp; Environmental Engineering University of Houston

390 views • 23 slides
Zipping Lists with Repetition -- a puzzle Koen Lindstrm Claessen type Nat = Int data List a =

Zipping Lists with Repetition -- a puzzle Koen Lindstrm Claessen type Nat = Int data List a =

Zipping Lists with Repetition -- a puzzle Koen Lindstrm Claessen type Nat = Int data List a = Unit a | List a :++: List a | Rep Nat (List a) same length zip :: List a -&gt; List a -&gt; List (a,b) minimal size len :: List a -&gt; Integer

332 views • 5 slides
set list tuple set set() Sets methods .intersection() .union() .difference() set sets

set list tuple set set() Sets methods .intersection() .union() .difference() set sets

set list tuple set set() Sets methods .intersection() .union() .difference() set sets methods .add() .update() .remove() .discard() list comprehension lists generators generator list next() list list list list 1 - 1 -

985 views • 61 slides
OZONE columns and profiles from ground based FTIR observations ( ESA-NIVR-KNMI project 2907 OMI

OZONE columns and profiles from ground based FTIR observations ( ESA-NIVR-KNMI project 2907 OMI

OZONE columns and profiles from ground based FTIR observations ( ESA-NIVR-KNMI project 2907 OMI validation by ground based remote sensing: ozone columns and atmospheric profiles, 2005-2008 ) A.V. Shavrina, Veles A.A., Pavlenko Ya. V.,

575 views • 43 slides
twenty steel construction: columns &amp; tension members Steel Columns &amp; Tension 1

twenty steel construction: columns &amp; tension members Steel Columns &amp; Tension 1

A RCHITECTURAL S TRUCTURES : F ORM, B EHAVIOR, AND D ESIGN A RCH 331 Cor-Ten Steel Sculpture By Richard Serra Museum of Modern Art Fort Worth, TX (AISC - Steel Structures of the Everyday) D R. A NNE N ICHOLS S PRING 2018 lecture twenty steel

713 views • 23 slides

More recommend