Eurostat methodological skills staff survey lesson learned final
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Transcript of Eurostat methodological skills staff survey lesson learned final
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Eurostat methodological skills staff survey:
Lessons learned after the 1st wave
Q&A session 9 September 2015 room A2/45 11:00https://ec.europa.eu/eusurvey/runner/MethNet2015
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Eurostat methodological skills staff survey• Why?
To map existing methodological skills within Eurostat
• What for? Understanding better our competencies and how they are grouped
• Who? Open to all staff
• How? Linked with new flexible working conditions in Eurostat
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Background
• Initiative proposed at the end of 2014 by the Eurostat 2020 discussion group B on “Permanent Methodological Development”, chaired by Director of A
• Adopted by the DM seminar in April 2015• Launched in June 2015 by Unit B1 in cooperation with
A2 under new sponsorship of Emanuele Baldacci, Director of B
• First wave during July 2015, open to all ESTAT staff• Still up and running
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Aim of this presentation
• Snapshot after the "first wave", network visualisation tools (igraph for R)
• - Respondents• - Dimensions
• methodological areas, statistical domains, tools• training activities, active role
• - Lesson learned • - Next steps
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When
Q&A session
2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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1010 10
1 12
1
7
3
7
1
3
1 12
1 12
1
Number of respondent per day
Launching: Cybernews email to DM & MethNet
Cybernews on Q&A
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Dat
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ased
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ata
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Expertise Ranking of Methodological Areas
0
20
40
60
80
10091
60
43
30 29 27 2623 23 22 21 19 18 17 16 16
1310 10 10 10 9 7 6 5 3 1 0
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7Agr
icul
tura
l and
Fish
erie
s S
tatis
tics
Bus
ines
s S
tatis
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Dem
ogra
phy
Env
ironm
enta
lS
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tics
Inte
rnat
iona
l tra
de
Nat
iona
l Acc
ount
s
Pric
es
Sci
ence
and
tech
nolo
gy
Soc
ial S
tatis
tics
Tran
spor
t
Ene
rgy
Sta
tistic
Oth
er
Expertise breakdown per statistical domain
0
5
10
15
2
7
5
2
4
8
10
4
9
2
4 4
3
8
4
5
1
5 5
2
7
13
6
7
2
6
3
1
3
2 2 2
11
14
12 12
1 1
0 0 0 0
1 1
5
8
3 3
During studiesWorking in EurostatWorking elsewhereWorking on articles
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R
SA
S
Evi
ews
Sta
ta
Mat
lab
Had
oop
Pyt
hon
SP
SS
Java
Oth
er
Expertise breakdown per statistical tool
0
5
10
15
20
10
13
8
17
10
14
12
4
8
0
4
1
8
0
5
2
7
0
3
4
1 1
3
2
1
2
4 4
16
1
10
5
6
4
5
6
7
4
7
3
During studiesWorking in EurostatWorking elsewherePersonal interest
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Big
Dat
a A
naly
tics,
Dat
aM
inin
g an
d D
ata
Sci
ence
Dat
a A
naly
sis
Dat
a D
isse
min
atio
n
Dat
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tegr
atio
n
Dat
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roce
ssin
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Dat
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atio
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sta
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roce
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Eco
nom
etric
s
Ent
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Arc
hite
ctur
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Indi
ces
Indi
cato
rs, c
ompo
site
and
synt
hetic
Infe
rent
ial S
tatis
tics
Info
rmat
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mod
els
and
stan
dard
sLi
near
Alg
ebra
Met
adat
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odel
san
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Mic
ro-d
ata
acce
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Mod
el-b
ased
Est
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Qua
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Sea
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ent
Sta
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entia
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Sta
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Sur
vey
Met
hodo
logy
Sam
plin
g
Adm
inis
trativ
e D
ata
Tim
e S
erie
s
Oth
er
Competency in R and SAS by methodological areas
0
5
10
15
8
2
15
12
1
0
2
3
8
1
2
3
0
2 2
1
2
3
8
6
1 1
2 2
3
4
5
4
1
2
3
1 1
3
0 0
6
3
0
3
4 4
0 0
5
4 4
8
2
5
2
5
12
10
2
5
RSAS
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Other
Energy Statistic
Transport
Social Statistics
Science and technology
Prices
National Accounts
International trade
Environmental Statistics
Demography
Business Statistics
Agricultural and Fisheries Statistics
Competency in R and SAS by statistical domains
number of people0 5 10 15
5 1
1 1
2 1
1314
3 0
5 4
7 5
1 3
3 5
3 4
5 8
3 4
RSAS
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Other skillsDynamic Factor Analysis
Questionnaire Design
Monte Carlo, sensitivity analysis;
Fuzzy logic
Population size; analytical
hypotheses testing
Regional Statistics/
Geographic information
Land cover and
use
Classifications and metadata standards
Temporal disaggregation
Nowcasting
ArcGIS
Business cycle analysis, S-VARS, G-
VARS
Neural network
Remuneration and Pensions
Cluster analysis
Labour Market
Asylum migratio
n
Fortran, Cobol,Pascal, C
, C++,PHP, Octave
OX-metrics 11
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Active Role
Informed58%
Active40%
No2%
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Training delivering experience
No
Yes
Training delivering experience
0 5 10 15 20 25 30 35
24
31
As a trainer elsewhere
As a trainer for in-house courses
As a workshop facilitator
As a ESTP course trainer
As a ESTP course leader
At the university
Training delivering experience
0 5 10 15 20
16
15
10
12
4
20
13
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Network Visualisation (Areas)
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Network Visualisation (Tools)
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Active role: full networkarea & tools
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Classification of statistical skillsMICROECONOMETRICS MACROECONOMETRICS MULTIDIMENSIONAL
DATA ANALYSISPROCESSES CONCEPTUALISATION
Model based estimation Time series Inferential statistic Statistical confidentiality
Design and optimisation of statistical processes
Econometrics Indicators, composite and synthetic indices
Descriptive statistics Micro-data access
Metadata models and standards
Linear algebra Indices Data analysis Data validation Enterprise architecture
Sampling Seasonal adjustment Big data analytics, Data mining and data science
Data warehousing
Data integration
Survey methodology Administrative data Data processing Information models and standards
Statistical softwareQuality
Data dissemination
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Network of classes
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Class 1MICROECONOMETRICS
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Class 2MACROECONOMETRICS
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Class 3MULTIDIMENSIONAL DATA ANALYSIS
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Class 4PRACTICAL PROCESSES
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Class 5 CONCEPTUALISATION
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Lesson learned
• About half of participants ready to take up an active role
• Strong expertise in Data analysis, Time Series and Econometrics• Most common tools SPSS, SAS and R• More than half have training delivery experience
• Identification of Centre of Competence might be based on a classification
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Next steps
September 2015• Survey link will be kept open, please participate if
you have not done so yet• Eurostat Methodological Network (Yammer Group)
October 2015• New flexible way of working, network layer• DM paper with structured proposal
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…to kick-off the discussion
• Any views and/or interpretation of what you have seen today?
• Do you see gaps in competencies emerging from the survey?