1. Lynn Cherny, Assoc Prof Data Science, emlyon business
school
& Students!
@arnicas
PyData Paris 2017

2. Why am I here?
• Starting up a program in data science/analytics at
a business school: emlyon business school
• My courses first year: Python bootcamp, Data
analysis with Pandas, Text analysis/NLP, Business
Analytics (Excel pivot tables, SQL, Tableau).
• Next year: an intro AI course, some web & db stuff,
plus above.

3. –faculty in the marketing department when I introduced myself
“What do our students really need to know?”

4. –faculty in the marketing department when I introduced myself
“What do our students really need to know?”
–me, who likes NLP problems
“Hey, let’s find out by looking at job ads in
France.”

5. Also, This Project Course
• “Business Data Science Projects” — combine students
from
• École Lyon Centrale (engineering school, so
presumably coders) +
• emlyon business students (presumably non-coders)
for product design/research/plan
In practice, coding skills in the teams were not distributed
as expected; but my project had strong skills on both
sides (we already taught a few Python courses by then)

6. The student team
• Mathilde TRÉARDE (superb
project manager)
• Thomas PUCCI (amazing
reactjs front-end dev)
• Yann VAGINAY (great python
data scientist)
• Imen FEHRI
• Mohamed Amine MEJRI
• Roxane MARCILHACY (great
python data scientist)
• Julien RAULT
• Eric DUPRAZ
• Sophie REISER (great market
research/analyst)
• Nicolas LOUVIGNE (top notch
visual designer/branding)
• Grégoire CANER-CHABRAN
• Sarah DAIEN

7. Data Sources
Indeed API: targeted searches, text collection
apec.fr: targeted searches (and sifoning from API)
“JT” (CSV data dump from an edu provider)
Data collection began in February 2017 in earnest.
I beefed it up in April/May.

8. Demo

14. Filter: A PDF resume uploaded… maybe a bit imperfect now:

15. Biz students:
95 student interviews of job searchers

16. Excellent creative work

17. UI mockup suggestions from
biz team

18. Architecture
Lynn said we should do these (Mongo, ES, Flask)
and set up (poorly managed and insecure) Mongo / Elastic / EC2 crawler host
herself on AWS.
Dev team did their own github/react & nodejs/Heroku plan.

19. Some discoveries in the
code after it was over.
• Databases didn’t have date the items were added
to them (date of scrape)
• Scraping was based on rather random sets of
words, and not consistent across site sources
• No automation of the indexing in Elastic - manual
job from Jupyter notebook (they knew this was an
issue too)
• Scraper code was never put on github.

20. My security issues
• Tried and failed to secure mongo by my own ssh key gen,
ended up using tunneling from scraping machine(that works
fine).
• Elastic is wide open and had been written to by a virus
(Amazon just sent me a warning), creating extra tables.
• We had a lot of issues with university firewalls and the cloud.
We all had to tether to phones to access the dbs from
school.
• AWS security stuff is really confusing. (One student team
didn’t succeed in using AWS at all— no one helped them.)

21. the data in more
detail…

22. Total Data Now by Source
• “JT,” an academic partner (given us as dump in
Jan, now “out of date”): 78K
• Apec: 25K
• Indeed: 10K

23. Apec - cadres
My student: “they would never hire someone like me”

24. Indeed - international feed (API)
with links - need to scrape text

25. more english:

26. Data in the db : the search
terms requested by API (!?)
apec.fr Indeed

27. Dates in the db (remember,
not the date scraped…)
Indeed’s date of
publication counts
Apec
student work ended March/Apr - I added new terms and increased scraping into May/June

28. JT provided data dates

29. JT provided data dates
No, this spike is real,
they are different ads and dated
this same day.

30. Job type labels
on JT data
Largest cats are
Marketing, Bizdev,
Communication
(Dev/IT not small tho)

31. “JT” : more “stages”

32. Revisit the word2vec
part

33. Or create your own list
and see the related
skills in the
“neighborhood”:
scikit-learn is not in the skills list? but is found in a job ad!

34. What is that graph?
a few “closely related skills” (by word2vec distance) in
a simple TSNE layout, computed and passed over API.
Awesome idea… but caveat: “Skills” were pre-filtered from the
word2vec model of the job ads, using the list of LinkedIn
skills.
link

35. A few related links
Radio’s tutorial on using word2vec in gensim:
https://rare-technologies.com/word2vec-tutorial/
My 8 million links on w2v papers/code etc:
https://pinboard.in/search/u:arnicas?query=word2vec
Interactive demo of w2v tsne layout of Yelp text reviews:
https://bl.ocks.org/arnicas/dd2ef348ad8854e40ef2
Useful warnings/info about making tsne layouts (we need
a grid search option):
http://distill.pub/2016/misread-tsne/

36. LinkedIn Skillz list: English,
Mysterious, —Garbage?

37. LI skills only
from the w2v model
in March

38. Zoom in…

39. Word2Vec updated (a week ago)
?!
Python also didn’t make the
“top 50 words
per search term,” which is
sad.

41. My shitty tsne layout that took
40 minutes on my laptop

42. Tensorboard projector view
convert your gensim model to tensorflow tsv files and upload
http://projector.tensorflow.org/
english

44. Tableau app
vis in
Tableau,
more UI
options

45. Most frequent
data-related
words, sized by
frequency
in search on
source.
Note: few JT ads
words (pink)

46. Sales,
logistics
supply chain -
lot of JT.

47. Let’s look at job ads
again…

50. “skills” are often soft or “previous
experience doing” in business job ads
link

51. Market research with
students:
Algorithm to determine “skill” “matches” is interesting but
worrying. It has to be really “good.”

52. –one of my students (who did better after tips on searching for skills I’d
taught on other job sites) :)
“I feel like we’re all looking at the same vague
job ads and competing with each other.”

53. Search by courses taken?
some of these descriptions are really short and vague; what’s
a good criterion for match?

54. sure, with 2 words, we get
some matches…

55. Teaching vs. Jobs, a Gap.
Les entrepreneurs sont appelés à résoudre
constamment des problèmes avec peu de temps
et de ressources pour prendre du recul dans un
environnement à forte incer7tude. En s'appuyant
sur des résultats en recherche sur le management
et la psychologie cogni7ve, ce cours vise à fournir
quelques apports simples pour développer et
accompagner l'ap7tude décisionnelle des
par7cipants.
“decision-making” course:
Job ad: “You can make decisions”?

56. So, Extension Ideas
• For student job search improvement:
• Return to skill extraction problem; use some training data. (Do some qualitative
analysis.)
• CV matching problem: revisit. Use different skills extraction (n-grams)
• Compare description of ALL courses taken (and liked) vs. jobs out there; is this
better?
• Curriculum development:
• Evaluate course descriptions by how well they match jobs
• Find “gaps” in teaching — what’s not being taught? (E.g., SQL.)
• Could course descriptions (and content) be better? Make this easier for
students?

57. My plan now
• Generally, starting up a Data Science Institute in
EM-Lyon. Money —> DS and data vis visitors/
confs/talks.
• Looking for help with teaching/workshops/tutorials
(Paris, Lyon, St. Etienne, Shanghai, Casablanca,
India)
• Contact me at cherny@em-lyon.com or @arnicas

58. Reminder: The student team
• Mathilde TRÉARDE (superb
project manager)
• Thomas PUCCI (amazing
reactjs front-end dev, multiply
employed)
• Yann VAGINAY (great python
data scientist doing NLP in
German stage now)
• Imen FEHRI
• Mohamed Amine MEJRI
• Roxane MARCILHACY (great python
data scientist) - now also web dev.
Looking for stage in Paris.
• Julien RAULT
• Eric DUPRAZ
• Sophie REISER (great market
research/analyst, not dev, but looking)
• Nicolas LOUVIGNE (top notch visual
designer/branding)
• Grégoire CANER-CHABRAN
• Sarah DAIEN