Women in CS @ TUM Welcome Event 2019/2020 - Dr. Lydia Nemec Data Scientist 2019-11-18
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Women in CS @ TUM Welcome Event 2019/2020 Dr. Lydia Nemec Data Scientist 2019-11-18
but I come in a
wrapping similar to
Penny
Dr. Lydia Nemec
Theoretical Physicist by training I am a bit like
Data Scientist @ Zeiss Dr. Sheldon Cooper,
A day with Zeiss?
A “user journey”
ZEISS binoculars and
80% of all high-end Every second, 2 people decide Conche chocolate camera lenses deliver
computer chips produced to purchase eyeglass lenses machine adjusted for the best outdoor
with ZEISS optics from ZEISS final texture and flavor experience
6:30 am 7:00 am 4:00 pm 6:00 pm
6:45 am 7:30 am 5:00 pm 9:00 pm
ZEISS metrology 15 million cataract
> 40 Nobel prize winners Cine lenses enable Oscar-winning
technology can be operations performed with
use ZEISS microscopes to movies like Titanic, Lord of the
found throughout the surgical systems from ZEISS
drive progress research Rings and Skyfall
automotive industry annually
~30,000 500 patent applications 25 global
~ EUR 6b in revenue
employees 11% R&D investments R&D sites
3
Your Zeiss Team for tonight
Simone Hanisch Michaela Haug Ellena Brenner Alexander Sayer
Lydia Nemec Annika Müller Michelle Knüchel Alejandra Armendáriz
4
Login to Mentimeter
The menti code:
123456
5
Mentimeter Moment
6
Mentimeter Moment
7
T.H. Davenport and D.J. Patil; “Data Scientist: The Sexiest Job of the 21st Century” Havard Business Review (10/12)
8
From Physics to Data Scientist
Florist, Erlangen Diplom Physics Research/ Thesis Physics R&D Expert Since Feb. 2019
Data Scientist
Data Scientist
Matura (Abitur) Family Nemec PhD MPG Berlin Post-Doc
Vienna, Austria
My Scientific background
Theoretical
Condensed
Matter Physics
High
Material Performance
Science / Computing /
Chemistry Computer
Science
10Mentimeter Moment
11A typical Data Scientist in 2019 [1]
Data Scientists apply numerical methods like Machine Learning to extract insights from data.
Predominantly male (69%) 8 years work experience
Bilingual Computer Science (22%)
Python / R (73%) Master (46%)
2.3 years as a Data Scientist PhD (28%)
In Germany, 7.8% of academics between
the ages of 25 and 65 have a PhD.[2]
[1] The Data Scientist Profile 2019 [2] Bildungsstand der Bevölkerung - Ergebnisse des Mikrozensus 2017
Skills, Experience, Education of 1,001 Data Scientists DEStatis, Statistisches Bundesamt p. 122 (2017)
12The Skillset of a Data Scientist
Data Scientists apply numerical methods like Machine Learning to extract insights from data.
Math, Numeric & Statistic Computer Science & Programming
❏ Machine Learning (AI) ❏ Software development
❏ Statistical modelling ❏ Programming Language (e.g. python)
❏ Linear Algebra & ❏ Databases (SQL/ No-SQL)
Optimization ❏ Cloud Computing
Communication, Soft
The Scientific Mind Skills & Visualisation
❏ Logical & independent mind ❏ Collaborative, strategic, proactive,
❏ Planning, conducting & creative and innovative
evaluate experiments ❏ Influence without authority
❏ Excellent analytical skills ❏ Translate data-driven insights into
❏ Meticulous attention to quality impactful decisions and actions
and accuracy ❏ Data Visualisation
13Data Science: The Challenge of Handling Complexity and Dynamics
Data Science
combines the complexity of
Software Development,
the challenges of applied numerical analysis
with the additional dynamic introduced by data!
14Machine Learning A field of Science that aims to create
refers to a set of machines that can perform tasks that are
algorithms that allow characteristic of human intelligence
computers to learn (John McCarthy 1956)
from data without
being explicitly
programmed. [1]
Deep Learning
is part of a broader family
of machine learning
methods based on
artificial neural networks. It
belongs to the class of
Rein-
hierarchical learning forcement
algorithm. Learning
[1] Samuel, Arthur L. „Some Studies in Machine Learning Using the Game
of Checkers,“ IBM Journal of Research and Development 44:1.2 (1959): 210–229.Market-Hypothesis are challenged through new technology
Computer Vision Computer Audition Natural language
processing
Reasoning & prediction Optimization & creation Motion & control
For the first time in history, machines can perform typically human tasks.
16Machine Learning: A different way of software development
Traditional Software
Write program Machine
DATA Result
based on rules
programming
Machine Learning Software
Prepared Program, fit
Write program Result Machine
DATA parameters
learning
I am
AI!
NEW DATA Use
ML Program
Program Result Machine
inference
Prediction
17Machine Learning cost asymmetry in training and inference
Machine Learning Software Higher computational cost during training
Prepared Program, fit
Write program Result
DATA parameters
NEW DATA Use
ML Program
Program Result
Lower computational cost during inference
Summit Oak Ridge National Lab since November 2018 fastest supercomputer in the world
Dr. Lydia Nemec 1852%
of
leave women in
t
[2] Th heir techn STEM
e ic
highe y leave at al role.
r rate a 45%
than
men.
31 31
[3]
27
27
22
22
Munich
[1] The Data Scientist Profile 2019 – Skills, Experience, Education of 1,001 Data Scientists [2] The Athena Factor: Reversing the Brain Drain in Science, Engineering, and
Technology [3] Why Women Leave the Tech Industry at a 45% Higher Rate Than Men (Forbes 02/2017)
19Mentimeter Moment
20Six things successful women in STEM have in common
Women filled 47% of all German jobs in 2016 but held only 22% of Science, Technology,
Engineering, and Mathematics (STEM) jobs. [1]
Women in STEM is tough and challenging – but in a exhilarating
and rewarding way
How to reap these rewards in a male-dominated environment
is not arbitrary clear
Systemic changes to improve the work experience for women in
STEM might be slow
[1] Statista; Erwerbstätigenquote der 20-64-Jährigen in Deutschland nach Geschlecht von 2002 bis 2018 Anteil MINT Akademikerinnen
21Six things successful women in STEM have in common
Women filled 47% of all German jobs in 2016 but held only 22% of Science, Technology,
Engineering, and Mathematics (STEM) jobs. [1]
2018
Women in STEM is1tough Repo challenging – but in a exhilarating
9% of and rt by t
he CT
and rewarding wayo wome I foun
Satisf n in S
o Re ied with t T EM a d that
spect heir c re [2]:
How to reap these o Irewards e urren
dinfoar tmale-dominated
n sen
i heir ex t jobs environment
I n sho or - le p
is not arbitrary clear rt à T vel positio ertise
hey a n
re suc s
cessf
ul
Systemic changes to improve the work experience for women in
STEM might be slow
[1] Statista; Erwerbstätigenquote der 20-64-Jährigen in Deutschland nach Geschlecht von 2002 bis 2018 Anteil MINT Akademikerinnen [2] Report: Center for Talent Innovation
(CTI), „Wonder Women in STEM and the Companies that Champion Them “ (09/2018)
22Six things successful women in STEM have in common
2018 Report by the CTI found that
19% of women in STEM are [2]:
o Satisfied with their current jobs
o Respected for their expertise
o In senior-level positions
In short à They are successful
Laura Sherbin; “6 Things Successful Women in STEM Have in Common” Havard Business Review (04/18)
Dr. Lydia Nemec 23Six things successful women in STEM have in common
Let’s figure out how:
ü We stay satisfied with our
current job
ü We feel respected for our
expertise
ü Reach (or stay) in senior-
level positions
Laura Sherbin; “6 Things Successful Women in STEM Have in Common” Havard Business Review (04/18)
Dr. Lydia Nemec 24Confidence in yourself and your capabilities
Report: Center for Talent Innovation (CTI), „Wonder Women in STEM and the Companies that Champion Them “ (09/2018)
25Claim credit for your ideas
Report: Center for Talent Innovation (CTI), „Wonder Women in STEM and the Companies that Champion Them “ (09/2018)
26Invest in peer network
Report: Center for Talent Innovation (CTI), „Wonder Women in STEM and the Companies that Champion Them “ (09/2018)
27Build up protege
Report: Center for Talent Innovation (CTI), „Wonder Women in STEM and the Companies that Champion Them “ (09/2018)
28Be authentic
Report: Center for Talent Innovation (CTI), „Wonder Women in STEM and the Companies that Champion Them “ (09/2018)
29Hone your brand
Report: Center for Talent Innovation (CTI), „Wonder Women in STEM and the Companies that Champion Them “ (09/2018)
30World-Café Discussion Hosts
(A) Confidence (B) Credit (C) Peers
(D) Protege (E) Authentic (F) BrandWorld-Café etiquette for participants
World-Café etiquette for participants
10+2 Min.
Check your plan
6 times
Change
with gongWorld-Café Discussion Hosts
1.Select your host:
• Check your plan
2.Discuss & record
(A) Confidence (B) Credit (C) Peers • 10 + 2 Minutes
3.Change:
• Check your plan
4.Repeat
• 6 times
(D) Protege (E) Authentic (F) BrandMentimeter Moment
35Thank you for your attention Simone Hanisch Michaela Haug Ellena Brenner Alexander Sayer Lydia Nemec Annika Müller Michelle Knüchel Alejandra Armendáriz
37
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