Durant cette présentation, nous introduirons des concepts de bases de la science de la donnée et discuterons d’un projet réalisé chez un de nos client. Nous découvrirons, comment on peut facilement réaliser des projets de science de la donnée à l’aide du langage de programmation statistique R, ainsi que de son intégration dans la nouvelle suite de Microsoft SQL Server 2016.

1. BÂLE BERNE BRUGG DUSSELDORF FRANCFORT S.M. FRIBOURG E.BR. GENÈVE
HAMBOURG COPENHAGUE LAUSANNE MUNICH STUTTGART VIENNE ZURICH
Data Science
… a comprehensible customer case
by Lev Kiwi

2. The menu
1. What is Data Science, and how one does it?
2. Our client‘s context
3. Demo Time with R
Exploring data
Modeling
Evaluating

3. Data Science

4. Why Data Science?
Raw Data
Operational
Reporting
Descriptive
Analytics
Predictive
Analytics
Prescriptive
Analytics
Analytic maturity
Value

5. What is Data Science?
Domain
Expertise
Math &
Stats
Computer
Science

6. CRISP-DM (CRoss Industry Standard Process - Data Mining)
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining.
Journal of Data Warehousing, 5(4), 13–22.
Business
Understanding
Data
Understanding
Data
Preparation
Modeling
Evaluation
Deployment
DATA

7. Client‘s Context
University of Fribourg CH

8. CRISP-DM (CRoss Industry Standard Process - Data Mining)
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining.
Journal of Data Warehousing, 5(4), 13–22.
Business
Understanding
Data
Understanding
Data
Preparation
Modeling
Evaluation
Deployment
DATA

9. What are ECTS
(European Credit Transfer and Accumulation System)?
Obtain
ECTS
Credits
Pass
exams
Take
exams
Follow
courses

10. How does the studies at the university works?
Mathematics
(120 ECTS)
Computer Science
(60 ECTS)
Bachelor in Mathematics
Philosophy
(60 ECTS)
Biology
(60 ECTS)
Mathematics
(90 ECTS)
Master in Mathematics

11. How does the studies at the university works?
3 years
180 ECTS
1.5 years
90 ECTS
30 ECTS are equivalent to a full-
time study load for one semester

12. What is the study intensity?
The study intensity of a student for a given semester is the number of ECTS
credits this students gets evaluated in the semester.
-------------------------------------------------------------------------------------------------------
Example. Dominique Duay follows the courses:
1. Introduction to Machine Learning (4 ECTS)
2. Macroeconomics (6 ECTS)
3. Data Analysis and Statistics with R (8 ECTS)
Dominique takes the exams for the first two only, his study intensity for the first
semester is 10 ECTS.
The next semester he decides to take the exam of the third course, this will add 8 ECTS
to its study intensity of the next semester.

13. What is the big deal about the study intensity?
 The average study intensity across study paths, programs, faculties, levels
is not the same and varies significantly. It is not clear why…
 It is felt that the study intensity is somehow linked to the reputation of the
studies.
 The number of ECTS evaluated per year is somehow correlated to the
budget the university will receive from the confederation.
 The Swiss Confederation started few years ago to monitor more closely the
study intensity of Universities.

14. Strategy
Identify variables correlated to low/high study
intensity
Predict which students will have a low study
intensity
Increase study intensity with concrete actions

15. Demo

16. What is R?
 Programming language for statistical computing and graphics
 Interpreted language (access it through the console)
 Open source and used by researchers, statisticians and data miners all
around the world
 Features > 9000 libraries on the CRAN repository
 Runs in memory (mostly…)

17. CRISP-DM (CRoss Industry Standard Process - Data Mining)
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining.
Journal of Data Warehousing, 5(4), 13–22.
Business
Understanding
Data
Understanding
Data
Preparation
Modeling
Evaluation
Deployment
DATA

18. The Data
 DWH fact table and related dimensions
 1 line = number of ECTS evaluated per student, per course, per professor…
 We aggregate this data to have one line per student, per semester
 We take data from 2012-2015

19. The Data

20. OFS Report on study intensity
Significant independent variables we already
know:
 Age
 Level (Bachelor, Master)
 Major (Economy, Law, Medicine, …)

21. Linear Regression Model

22. Regression Tree Model

23. Comparison between models
 Root Mean Square Error:
0 < RMSE < 𝜎
(the smaller, the better…)
 R Squared:
𝑅2
< 1
(the closer to 1, the better…)

24. Comparison between models
 Root Mean Square Error:
0 < RMSE < 𝜎
(the smaller, the better…)
 R Squared:
𝑅2
< 1
(the closer to 1, the better…)
Linear Regression.
RMSE = 12.2
𝑅2
= 0.37
Regression Tree.
RMSE = 11.6
𝑅2
= 0.43

25. CRISP-DM (CRoss Industry Standard Process - Data Mining)
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining.
Journal of Data Warehousing, 5(4), 13–22.
Business
Understanding
Data
Understanding
Data
Preparation
Modeling
Evaluation
Deployment
DATA

26. Variable importance of the advanced model
Overall
AVG_PRESTATION_POPULARITE 9,721355
PRC_EXAM_IN_SEMESTRE 6,282732
PRC_PERIODICITE0 6,004846
AVG_TIME_TO_EXAM 5,810226
AVG_PRESTATION_DIFFICULTE 5,762537
UNI_VP_FAC 5,646884
PRC_PERIODICITE1 5,473713
UNI_VP_VE_NIVEAU 5,182513
PRC_PERIODICITE4 4,442017
PRC_TYPE_PRESTATION2 4,342148
PRC_TYPE_PRESTATION13 3,675386
ETU_DOMAVET_CANTON 2,938208
ETU_AGE 2,775811
ETU_ETABLISSEMENT 1,585795
PRC_TYPE_PRESTATION1 1,556703
PRC_PERIODICITE23 1,413239
PRC_TYPE_PRESTATION3 0,286059
SEMESTRE 0,16307
ETU_SEXE 0,133126
PRC_TYPE_PRESTATION7 0,07456

27. Predictive capabilities of the advanced model
RMSE = 9.3
𝑅2
= 0.63

28. CRISP-DM (CRoss Industry Standard Process - Data Mining)
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining.
Journal of Data Warehousing, 5(4), 13–22.
Business
Understanding
Data
Understanding
Data
Preparation
Modeling
Evaluation
Deployment
DATA

29. Cumulative Response Curve
Baseline model Advanced model

30. Feedback from the client

31. Dr. Lev Kiwi
Consultant BI
Tel. +58 459 53 75
Lev.Kiwi@trivadis.com