=Paper=
{{Paper
|id=Vol-2104/paper_266
|storemode=property
|title=Architecting Data Science Education
|pdfUrl=https://ceur-ws.org/Vol-2104/paper_266.pdf
|volume=Vol-2104
|authors=Vadim Ermolayev,Mari Carmen Suarez-Figueroa,Oleksii Molchanovskyi
|dblpUrl=https://dblp.org/rec/conf/icteri/ErmolayevSM18
}}
==Architecting Data Science Education==
https://ceur-ws.org/Vol-2104/paper_266.pdf
Architecting Data Science Education
Vadim Ermolayev1[0000-0002-5159-254X], Mari Carmen Suárez-Figueroa2,
and Oleksii Molchanovskyi3
1
Department of Computer Science, Zaporizhzhia National University, Ukraine
vadim@ermolayev.com
2
Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain
mcsuarez@fi.upm.es
3
Ukrainian Catholic University, Lviv, Ukraine
olexiim@ucu.edu.ua
Abstract. Data scientists are currently among the most demanded professionals
in many spheres, including industries, governments, public sector, among oth-
ers. This is due to several good reasons. Probably an important one of those rea-
sons is the growing demand to find proper ways to face the challenges of estab-
lishing data-driven economies and societies. As academics and educationalists,
but also Data Science professionals, we look at how to bring up this kind of
specialists such that to meet the current shortages but also mid-term demands.
In this position paper we deliberate about how to architect thematically, didacti-
cally, and organizationally a university program under the thematic umbrella of
Data Science. We focus on the selection of learning units or disciplines to be
covered in order to produce the M.Sci. and Ph.D. graduates who will be ready
to face the future challenges in the mid-term perspective. We outline our rec-
ommendation on using learning tools and materials. We also concisely present
the approach for stimulating competitive and cooperative atmosphere in the
class that stimulates intensive collective and individual learning. We recom-
mend to reinforce an academic program by involving industrial partners inten-
sively in the process. We ground our deliberation on our experience in imple-
menting relevant M.Sci. and Ph.D. programs in Data Science and Semantic
Technologies.
Keywords: Data Science education, topical scope, program structure, learning
tools, didactics, collaboration with industries.
1 Introduction
The boost in the abundance, complexity, and variety of data in all spheres of human
activity is a phenomenon that leaves a rare information professional negligent these
days. Industries are entering into data driven economy, which demands having and
using data as a primary asset. On the other hand, the shift to more intensive use of
data results in the increase of data generation and storage at unprecedented scales in
terms of volumes and rates. A few topical examples are as follows (c.f. [1]):
“Exponential growth of data volumes is accelerated by the dramatic increase of
social networking applications that allow non-specialist users create a huge
amount of content easily and freely. Equipped with rapidly evolving mobile
devices, a user is becoming a nomadic gateway boosting the generation of ad-
ditional real-time sensor data. The emerging Internet of Things makes every-
thing a data or content, adding billions of additional artificial and autonomic
sources of data to the overall picture. Smart spaces, where people, devices, and
their infrastructure are all loosely connected, also generate data of unprece-
dented volumes and with velocities rarely observed before.”
Hence, data generation is a phenomenon that fuels itself and so far we do not ob-
serve any signs of saturation for this process. Straightforwardly, the societal demand
for the professionals capable of efficient and effective processing of these data also
increases at unprecedented rate. These gave rise to Data Science as a discipline and
community. As denoted by Hoehndorf and Queralt-Rosinach [2]:
“Data Science has as its subject matter the extraction of knowledge from data.
While data has been analyzed and knowledge extracted for millennia, the rise
of “Big” data has led to the emergence of Data Science as its own discipline
that studies how to translate data through analytical algorithms typically taken
from statistics, machine learning or data mining, and turning it into knowledge.
Data Science also encompasses the study of principles and methods to store,
process and communicate with data throughout its life cycle, and starts just af-
ter data has been acquired”.
A data scientist is currently one of the most requested and highly paid jobs. The
reason for it is the lack of such professionals in industries, but also in academia.
It is widely acknowledged that Big Data, which is the area of our interest, begins
when the traditional methods for processing data do not work due to the excess in
volume, variety, velocity, or complexity. The phenomenon of Big Data causes also a
conceptual divide in the Data Science community in broad. Enthusiasts propagate
that, faced with real big data, a scientific approach “… hypothesize, model, test – is
becoming obsolete. … Petabytes allow us to say: "Correlation is enough." We can
stop looking for models. We can analyze the data without hypotheses about what it
might show. We can throw the numbers into the biggest computing clusters the world
has ever seen and let statistical algorithms find patterns …” (c.f. [3]). Pessimists how-
ever point out that Big Data is often not healthy, as it provides “… destabilizing
amounts of knowledge and information that lack the regulating force of philosophy”
(c.f. [4]).
Academia has to respond to this challenge by providing professionals capable of
dealing with this phenomenon following a balanced path that equally accounts to the
highlights and lowlights of both optimistic and pessimistic approaches. A pursuit to
such a balanced path gives us a hint about what is the shortage in the required skills
for a Data Scientist.
This is exactly the way we follow to architect and deploy the related programs, at
M.Sci and Ph.D. levels. There are some results on this way which we want to share in
this paper. To help a reader better understand our approach we structure this presenta-
tion along the three important facets that form, so to say, the environmental grid.
These are thematic, didactic, and organizational.
The remainder of the paper is structured as follows. Section 2 focuses on the re-
lated work and therefore discusses the most prominent relevant academic programs to
date. Section 3 deals with our approach to form the topical scope of the academic
programs at M.Sci. and Ph.D. levels. It is also about choosing the teaching and learn-
ing tools and also didactics that help make, in our opinion, teaching and learning Data
Science more efficient and effective. Section 4 is about our approach to propose a
proper organizational environment for our students that enables seamless bi-
directional interaction with the data generating and processing stakeholders in indus-
tries. Here we also report about our experience in having different kinds of coopera-
tion, between universities and also with industrial partners, that help us achieve a
surplus in providing quality education to our students. Finally, in Section 5 we draw
some conclusions and outline our plans for future work.
2 The Most Prominent Related Work
There are an increasing number of institutions from around the World now offering
M.Sci. courses and also Ph.D. programs in Data Science. Perhaps one of the most
prevalent international efforts in Europe is EIT Digital Master Program in Data Sci-
ence 1 . This Master offers to the students the possibility of studying data science, in-
novation, and entrepreneurship at leading European universities. In this program,
students will learn about scalable data collection techniques, data analysis methods,
and a suite of tools and technologies that address data capture, processing, storage,
transfer, analysis, and visualization, and related concepts (e.g., data access, pricing
data, and data privacy). This two-year Master promotes the geographical mobility by
means of studying in universities in two different European cities.
One more relevant European initiative was the European Data Science Academy
(EDSA) 2 an H2020 EU project that has been effective in 2015 - 2018. The objective
of the EDSA project was to deliver the learning tools that are crucially needed to
close the skill gap in Data Science in the EU. The project spinned off an Online Insti-
tute, based on the project foreground, to leverage the outcomes of the EDSA project.
The Institute will continue to be operated by the EDSA project partners beyond the
lifetime of the project: Open University (UK), University of Southampton (UK), Insti-
tut Josef Stefan (Slovenia), Fraunhofer Institut (Germany), KTH Royal Institute of
Technology (Sweden), ideXlab (France), Persontyle Limited (UK), Technische Uni-
versitaet Eindhoven (TU/e) (the Netherlands), Open Data Institute LBG (UK).
At National scale in Europe, the Italian Data Science PhD program 3 needs to be
mentioned. Data Science PhD is a joint initiative by Scuola Normale Superiore, Uni-
versity of Pisa, Sant'Anna School of Advanced Studies, IMT School for Advanced
1
https://www.tue.nl/universiteit/faculteiten/wiskunde-en-informatica/studeren/graduate-
programs/masteropleidingen/eit-data-science/
2
http://edsa-project.eu/
3
http://datasciencephd.eu/
Studies Lucca, and National Research Council. This program develops a mix of
knowledge and skills on the methods and technologies for: the management of large,
heterogeneous, and complex data; data sensing; data analysis and mining; data visu-
alization and storytelling; understanding the ethical issues and social impact of Data
Science. Ph.D. students admitted to the program have an opportunity to develop data
science projects in different domains.
While Data Science is a relatively new term, the academic programs (M.Sci. and
Ph.D. levels) in the neighboring areas, such as Statistics, Business Analytics, Artifi-
cial Intelligence, and Machine Learning, existed for a while. The appearance of the
new term, Data Science, allowed to introduce “an umbrella” for many programs. That
led to their (programs) broader comparison and analysis. A lot of web resources ap-
peared for that purpose. For instance, “23 Great Schools with Master’s Programs in
Data Science” 4 that lists M. Sci. degree programs in Data Science in the U.S. univer-
sities. One of the most completed (and continuously updated) list of the academic
programs in the field is provided by the Data Science Community list 5 . The list
counts almost 600 colleges.
The structure and the curricula of the majority of the academic programs in Data
Science are relatively the same worldwide - all admitting its interdisciplinary nature
and synergetic character (e.g. [5]). There are courses focused on ramping-up the stu-
dents regarding the necessary mathematical background (theory of probability, statis-
tics, time series, linear algebra, etc.). Another group of courses teaches data and soft-
ware engineering (mostly databases, database management, and related software in-
frastructures). The core group of courses, including machine learning, data mining,
deep learning with various modifications provide the competencies in relevant ena-
bling technologies for data scientists. Finally, there is a business or application ori-
ented group of courses. This group teaches how the technologies could be effectively
applied in specific business tasks and for solving specific business problems. Applica-
tion domains are seen quite broadly and span across marketing analytics, natural lan-
guage processing, computer vision, bioinformatics, etc. In the recent years Data Sci-
ence curricula started to integrate the courses that take into account ethical problems
in the context of data processing, analytics and interpretation. Some programs see the
landscape for ethical issues even broader and include Data Science and Artificial
Intelligence applications.
In terms of building Data Science curricula, the Data Science Model Curriculum
[6], created in the context of the EDISON project 6 , is of particular relevance. This
Model Curriculum was built as a part of the EDISON Data Science Framework
(EDSF) that provided a foundation for the Data Science profession definition. The
EDSF includes the following core components: Data Science Competence Framework
(CF-DS), Data Science Body of Knowledge (DS-BoK), Data Science Model Curricu-
lum (MC-DS), and Data Science Professional Profiles definition (DS-PP).
4
http://www.mastersindatascience.org/schools/23-great-schools-with-masters-programs-in-
data-science/
5
http://datascience.community/colleges
6
http://edison-project.eu/
3 Topical Scope and Didactics
In this section we present our positions by putting together: a structural and topical
organization of a Data Science program, mainly for its M.Sci. level; our views on
appropriate tools and media for teaching and learning; and some tips on didactics that
helps make the learning process more effective and efficient.
3.1 Topical Scope
For architecting Data Science Education the approach elaborated in the EDISON
project looks like very relevant. The EDISON approach to defining the Data Science
Model Curriculum followed a competence-based education model and has been
summarized in the following steps (c.f.[6]):
1. For each enumerated competence from CF-DS, the Learning Outcome is defined
according to knowledge or mastery level (Familiarity, Usage, Assessment for cur-
rent MC-DS version)
2. A DS-BoK includes Knowledge Area Groups (KAG) from the available BoK ele-
ments and also those defined based on the 2012 ACM Computing Classification
System 7 (CCS 2012)
3. Each Knowledge Area Group (KAG) is mapped to existing academic subject clas-
sification groups that are based on ACM CCS 2012 complemented with the do-
main or technology specific classifications to be defined by subject experts.
4. For each KAG or Knowledge Unit, related Learning Units are specified according
to academic subject classification or current university practices
5. For each Learning Unit, its category as core/mandatory (Tier 1 or Tier 2), elective,
or prerequisite is assigned
6. For Core and Elective Learning Units, the list of Learning Outcomes is defined
In addition, as suggested by the Government of the UK in their Guidance docu-
ment 8 , a data scientist has to have, among others, the following skills:
Good knowledge about applied mathematics, statistics and scientific practices
Good knowledge about data engineering and manipulation
To best account for an interdisciplinary and synergetic character of Data Science
and also its focus on practice, [7] suggests the following guiding principles to form-
ing the curricula:
Organize the course around a set of diverse case studies
Integrate computing into every aspect of the course
Teach abstraction, but minimize reliance on mathematical notation
Structure course activities to realistically mimic a data scientist’s experience
7
https://www.acm.org/publications/class-2012
8
https://www.gov.uk/government/publications/data-scientist-skills-they-need/data-scientist-
skills-they-need
Demonstrate the importance of critical thinking / skepticism through example
Taking into account the aforementioned guidance and desired skills, we suggest
that a Data Science program needs to cover:
Foundations, such as mathematical apparatus (e.g. statistics, algebra), machine
learning, and artificial intelligence,
Technologies, such as text mining, semantic web, open data, data storage and proc-
essing,
And also specific domain applications, such as in healthcare, humanities, public
governance, social studies, finance, management, media, journalism, etc.
Therefore, we propose, following the EDISON framework [6], that a Data Science
program is structured as follows and the parts further comprise the following
units/disciplines:
Core subjects/courses. The examples of the courses can be: Elementary statistics;
Computational thinking; Advanced algorithms and data structures; Web technolo-
gies; Digital entrepreneurship; Qualitative research methods; Interdisciplinary
thinking; Data-centric decision making
Subjects/courses for the DS Analytics Itinerary. This set includes subjects for
using appropriate statistical and data analytics techniques on available data to de-
liver insights and discover information, providing recommendations, and support-
ing decision-making. The examples of the courses can be: Data mining; Supervised
and unsupervised machine learning; Statistical modelling; Predictive analytics.
Subjects/courses for the DS Engineering Itinerary. This set includes disciplines
subjects for using engineering principles to research, design, develop and imple-
ment new instruments and applications for data collection, analysis and manage-
ment. The examples of the courses can be: Software and infrastructure engineering;
Manipulating and analyzing complex, high-volume, high-dimensional data, struc-
tured and unstructured data; Cloud based data storage and data management. In
this part it is also important to inform student About: Symbolic Artificial Intelli-
gence; Semantic technologies and knowledge graphs; Open Data and respective
initiatives; Cognitive Computing - an initiative by IBM Watson Research Center;
Automated Machine Learning 9 ; Computer Vision; Natural Language Processing;
Reinforcement Learning; Network Analysis.
Subjects/courses for specific applications. This set includes different scenarios
for disciplines as management, healthcare, social sciences and humanities, media
and communication, and astronomy, among others.
3.2 Tools and Learning Materials
A number of MOOCs offer data science courses to large, global audiences, offering
the opportunity for data science training to occur online and remote from the host
9
http://www.ml4aad.org/automl/
organization. For example, the University of Warwick offers a MOOC in Big Data:
Measuring and Predicting Human Behaviour, while in the US, Stanford offers a dedi-
cated MOOC on Machine Learning. The University of Southampton 10 has held a
highly successful Web Science MOOC 11 hosted on FutureLearn and a MOOC on the
use and implementation of open data and innovation in organizations. UPM offers
also MOOCs related to semantic technologies and ontologies in MiriadaX platform,
which is focused on Spanish.
A good approach for learning resources is to create them as reconfigurable course
components that can be adapted and customized for different learning contexts. Such
learning materials should be created by means of a participatory approach (using for
example the SlideWiki platform). It is also important that such resources include also
real world data sets, including publicly available data 12 and well as data assets and
benchmarks used in different scientific disciplines.
3.3 Tips on Didactics
Active competitive learning model is suggested for the courses in order to make the
learning process more effective and efficient. Among didactical patterns we already
used or plan to use in different related courses are:
The use of peer review for course assignments
Organizing final competitions to rate the results of course works or practical as-
signments
Real-life project work
A peer review model assumes that the students in a class take an active part in
evaluating the results of the other students. This model has been practiced in the
classes on Advanced Algorithms and Data Structures, and Linear Algebra, two of the
core courses in the Computer Science with Data Science (CSDS) program at Ukrain-
ian Catholic University (UCU) for evaluating coursework reports. The approach was
based on the former practices reported in [8] and resembled a conference peer review
process. The students played the role of a “Program Committee Member”. They got
their assignments from the Chair played by their instructor. The chair also did the
reviews. In order to make student reviews more unbiased and structured, a detailed
review form has been developed, which included all the required aspects to be evalu-
ated. The individual grades given by the students to the others work have been com-
pared to the average grades, also counting for the instructor’s grades. The students
with smaller deviations from the averages received more additional points for their
review work. Overall, a student had a chance to receive up to 80 points for his or her
10
The University of Southampton, UK, launched the first data science related MOOC on the
platform on web science, which attracted around 15,000 students worldwide.
11
https://www.futurelearn.com/courses/web-science
12
E.g. http://open-data.europa.eu/ and http://publicdata.eu/
work and up to 20 points for the reviews. EasyChair conference management sys-
tem 13 was used to manage this peer review process.
A competition of the software developed by students as a practical component of a
course is planned to be introduced in the course on Automated Term Extraction
(ATE) and Ontology Learning from Texts. The course will be given in Spring 2018 at
the Ukrainian Catholic University. The course will be organized in the form of several
short tutorials, each dealing with a particular aspect in the ATE pipeline. Each tutorial
comprises a hands-on component taking circa 50 percent of teaching time. After each
lecture, except the introductory one, the students are offered to:
Use the instrumental software and the document collection(s) / dataset(s) provided
by the tutor
Refine the software in some advised way, e.g. by introducing a more sophisticated
metric or an improvement in an algorithm
Perform a cross-evaluation experiment to compare the initial revision of the soft-
ware and their refined revision
These practical tasks are organized in a way to finally assemble a simple instru-
mental tool suite that helps performing a basic ATE workflow.
The final slot of the course is organized as a cross-evaluation contest for the solu-
tions by students. They are offered to apply their tool suites to the same document
collection and measure the quality of ATE results using objective metrics. The ranked
list of the solutions is built based on the comparison of these results. So, the students
are rated according to their achievements in the cross-evaluation.
Another important and highly useful approach to didactics includes integration of
the real-life project-based syllabus for various classes. One of the relevant examples
was implemented as a part of the course Introduction to Data Science (CSDS program
at UCU). During the project work (which was the only one practical part of the
course) students had to participate in the Queen's International Innovation Challenge14
organized by Smith School of Business from Queens University, Canada. As the par-
ticipants of the contest, the student teams worked on the challenges provided by vari-
ous Canadian companies (in 2016) and United Nations (in 2017). Several topmost
teams have been qualified for the final in Toronto, Canada, where they presented their
projects to a jury. The whole aim was to not only show a technical solution but pro-
vide its business background and value. The latter is crucial for the data scientists.
4 Partnerships and Cooperation
Several activities related to data science training should be performed as part of
M.Sci. programs. These activities could be included in short-term certified programs
(e.g. summer and winter) schools and exchange programs. These training activities
should be also aligned with institutional practices in human resources and profes-
13
http://easychair.org/
14
https://smith.queensu.ca/centres/scotiabank/competition/index.php
sional development, as well as with current company creation courses and activities;
as for example ActuaUPM at UPM, which has created more than 100 technology
companies in the past 10 years. Universidad Politécnica de Madrid (UPM) has been
among the first IT labs in Europe to establish long-term collaborations via research
projects, researcher exchange programs, and training in life sciences, healthcare,
management, or Earth and Space science. UPM.is active in organizing summer
schools and also involves students, at M.Sci. and Ph.D. levels, in academic and re-
search exchange programs. Several good examples are:
Organized summer schools:
UPM has been involved in the ISSGC summer school series (International Summer
School on Grid Computing), until its end in 2009, in the SSSW summer school series
(Summer School on Ontology Engineering and Semantic Web), and in the Marie
Curie ITNs SCALUS and BigStorage.
Exchange programs / projects:
SemData 15 was the project coordinated by UPM and funded under the International
Research Staff Exchange Scheme (IRSES) of the EU Marie Curie Actions. It was
focused on facilitating exchanges of the research group members, including Ph.D.
students, among the participating institutions. SemData brought together research
leaders and young researchers across the globe from the relevant communities:
Linked Data, Semantic Web, and Database Systems. Research cooperation between
the members of project partner organization continues beyond the lifespan of Sem-
Data, for example between the Ontology Engineering Group (OEG, UPM) and Intel-
ligent Systems Research Group (ISRG, Zaporizhzhia National University, ZNU).
UPM also was partner in the PlanetData Network of Excellence 16 , in which one of
the main highlights was the founding of the ESWC Summer School. This school was
created to provide an opportunity for Master’s and Ph.D. students in the area of the
Semantic Web to learn the key topics in the field from the leading researchers in the
area. This summer school was designed using experiences from previous summer
schools run within the OntoWeb, KnowledgeWeb and S-Cube networks of excel-
lence.
UPM also participated in the UNIVERSAL (Universal Exchange for Pan-European
Higher Education) project, which offered a solid basis for curriculum alignment.
Ukrainian Catholic University (UCU) also runs their summer school in Data Sci-
ence 17 in Summer semesters. The school aims to provide a broad and rapid introduc-
tion to the field of Data Science. The audience mainly consists of the senior-year
bachelor, master, and Ph.D. students, and young professionals. The curricula include
introductory courses (e.g., Statistics, Machine Learning), domain-specific courses (in
healthcare, finance, marketing analysis, urban analytics, among others), and project
work. The latter is based on the topics provided by the third-party organizations and
15
http://www.semdata-project.eu/
16
https://www.planet-data.eu/
17
http://cs.ucu.edu.ua/en/summerschool/
commercial companies. The activities framing out short-to-long-term stays in differ-
ent organizations should include industrial partners in addition to the involved aca-
demic institutions in different countries around the world. Long-term collaborations
via research projects, researchers exchange programs, training in several disciplines
of Science, and involvements in public administration work are topical sorts of coop-
erative relationships that help framing, broadening and deepening the professional
horizons of future data scientists. So, these need to be established, evolved, and re-
fined for every Data Science program, especially at a Ph.D. level.
ISRG at ZNU is active in establishing and rationally exploiting cooperative part-
nerships with industrial entities regarding their M.Sci. and Ph.D. level students en-
rolled on the Data Science and Semantics program (the part of Computer Science
program). In their cooperation with BWT Group 18 , the M.Sci. students are enrolled
for short professional internships in their second year. On these internships, the stu-
dents are fully involved as junior software engineers and data scientists in the com-
mercial projects performed by the company. The Ph.D. students in this cooperation
may apply for a part-time work at BWT and hence combine the benefits of academic
and industrial professional environments put together for their Ph.D. projects. For
example, a Ph.D. student may borrow a use case for his research from his industrial
work. From the other hand, a new approach or technique developed in a Ph.D. project
may find its validation and swift transfer to industry in this cooperative setting. last
but not least, Ph.D. students are paid for their part-time work at an industrial scale.
So, they also earn enough money during their Ph.D. term and also learn what is indus-
trial research consulting.
One more good example of the cooperation of ISRG with industry is their activity
with the LOD project by Springer Nature 19 . In this case, Springer provides the use
case for one of our Ph.D. projects (c.f. [9]) which develops an approach to detect ter-
minological saturation in high-quality document collections. The use case by Springer
is on journal papers in Knowledge Management. The company provides the docu-
ments and looks forward to evaluate the outcome of this research in their industrial
setting. One more partner in this research project is UPM.
The CSDS program at UCU which was designed tightly with the local IT industry
includes the same internship approach as well. During the last semester of the pro-
gram, students visit the companies for the internship work. The diploma thesis could
be grounded on the projects that students made during that visits.
Yet one more line of cooperation includes the invitation of lecturers for various
MSc and Ph.D. courses. To our experience, that could be done at least in two scenar-
ios: (i) when calling for external academic expertise may bring a super-additive effect
on the quality of teaching and learning; and (ii) to attract industrial professionals for
more specific and state-of-the-art educational content. One of the interesting examples
for the first scenario is a course on Computer Vision at UCU. For that relatively large
course (5 ECTS), the university didn't possess a top-qualified instructor. Thus the idea
to split the course into several interlinked modules and invite different instructors for
18
https://www.groupbwt.com/
19
http://www.springer.de/
teaching these units has been implemented. This approach also allows decreasing the
overall load on one particular professor and makes the whole course more flexible and
diversified in terms of been based on several approaches, opinions and experiences.
The involvement of industrial leaders was piloted at UCU as a part of the season
certified programs (Machine Learning Winter School 20 and Machine Learning Sum-
mer Workshops 21 ). This allows introducing most relevant knowledge and practical
techniques to the students and making them familiar with the real-life industry cases
and projects.
5 Recommendations
In fact, a Data Science program, like any other academic program, becomes success-
ful if a proper balance between efficiency and effectiveness is achieved. Efficiency is
traditionally regarded as a function of spent resource per achieved result, which has to
be minimized. Effectiveness is related to impacts on the students, and also on the
society in broad. Contrarily to efficiency, effectiveness needs to be maximized. Nota-
bly, improving efficiency – i.e. decreasing resource and effort spent – brings a risk of
reducing effectiveness. Luckily for Data Science education and due to a high demand
for data science professionals, efficiency can be improved by not reducing but sharing
and balancing resource and effort with interested partners, such as industries.
In this section we summarize our positions presented in Sections 3-5 in the form of
recommendations for architecting an efficient and effective academic Data Science
program.
Recommendation 1. Make your curriculum competence-based, modular, flexible,
and adaptable to feedbacks. In our case, as explained in Section 3, these are achieved
by following the EDISON framework for competencies, forming the topical scope,
and using reconfigurable teaching units for flexibility and reacting to feedbacks.
Recommendation 2. Reduce efforts and increase impacts by using appropriate
teaching and learning tools. For achieving that, we suggested using a MOOC ap-
proach and infrastructures like SlideWiki for up-scaling impacts through the re-use of
teaching materials. This approach also facilitates to making course units reconfigur-
able and more flexible.
Recommendation 3. Improve effectiveness by incorporating proper motivations in
didactics. To implement this recommendation, we use peer-review and competitions
in the teaching process. Competitiveness is balanced with teamwork. Hence, as men-
tioned above, the motivation of students and the quality of teaching and learning are
improved. Yet one more way to increase these impacts is the introduction of the real-
life project-based syllabus for various courses.
Recommendation 4. Balance resources and increase impacts by intensively in-
volving industrial partners and international programs in the process. In our experi-
ence, this is achieved by actively involving different stakeholders as academic pro-
gram partners, such as companies, international associations, public funding bodies.
20
http://cs.ucu.edu.ua/en/winterschool/winter-school-2017/
21
http://cs.ucu.edu.ua/en/mlworkshop/machine-learning-summer-workshops-2017/
As presented in Section 4, this works very well and proves to be effective both at
M.Sci and Ph.D. levels.
It looks like the recommendations we gave may work well not only for Data Sci-
ence Education, but also broader – for other educational domains. We did not check it
though, so far. The results of our checks show that, for Data Science, these architec-
tural tips are (i) modular – so can be exploited individually to improve programs; and
(ii) super-additive – so the more of these are used, the better the balance between the
efficiency and effectiveness of a program becomes.
6 Conclusions and Future Work
In this position paper we presented our views on how an effective and efficient aca-
demic Data Science program may be architectured. These views are of course biased
as we take active parts in developing such programs at our universities (UCU, UPM,
and ZNU). We also believe that we had the right to share these views as the programs
we contribute to are deemed successful. Despite the bias and some indicators of indi-
vidual success, we think that the paper presented some considerations and choices that
are broadly applicable, because these are parts of the best practices in Europe and
overseas. We referred to these practices, and also some of their resources, in our con-
cise review of the related work in Section 2.
Section 3, focusing on the proposed topical scope and outlining some tips on tools,
learning modes, and didactics, is deemed as our contribution in this paper. based on
the relevant results, we outlined how a Data Science program should be structured
and look topically. Further we presented our views on the effective use of learning
tools and materials, like MOOC. Yet further, we shared our experience in using an
active and competitive learning model in the courses. These didactics use a peer re-
view approach, students’ software competitions, and real-life project work.
In Section 4, we scoped out our views and shared relevant experience on how a
productive environment for “breeding” data scientists may be organized via several
sorts of partnerships, including cooperation with industries. By pointing out to our
experience, we articulated that a collaborative environment involving industrial par-
ties proves to be useful and effective for making a Data Science program successful.
Finally, we summarize our positions on architecting a successful academic Data
Science program in the form of four recommendations in Section 5.
Regarding the future work, our plan is to analyze and review our proposal with re-
spect to well-known teaching practices and novel teaching methods. In addition, we
will update the proposed program organization taking into account the developed
educational content of the courses within Data Science programs in EU (as for exam-
ple the one proposed by the EIT Digital School). We also think about ways to include
the relevant ethical and legal issues in the curricula in a harmonized way. Ethical
issues could be taught based on the existing frameworks like the Guidance document
by the Government of the UK on ethical issues 22 . The discussion of legal issues in
22
https://www.gov.uk/government/publications/data-science-ethical-framework
(Big) data analytics is on the hype today. So the lessons could be learnt and taught to
students, e.g. based on the case of Cambridge Analytica 23 .
Acknowledgements
This work has been done in cooperation between the authors which has been kicked
off in the EU FP7 SemData project funded by Marie Curie International Research
Staff Exchange Scheme, grant agreement No PIRSES-GA-2013-612551. The authors
would also like to thank the anonymous reviewers for their valuable suggestions on
improving the paper.
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23
https://amp.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-
election?CMP=share_btn_tw&__twitter_impression=true