orientation to data science studies (1 cr) · •held on wednesdays at 9.15-11.00 in b120...

Faculty of Science

ORIENTATION TO DATA SCIENCE STUDIES

(1 CR)Pirjo Moen

6.9.2017

6.9.2017Orientation to Data Science Studies / Pirjo Moen 1

Faculty of Science

OUTLINE

• Introduction to the course

• Studying Data Science

o Degree structure

o Personal study plan (PSP)

• Learning Skills for (Data) Science

• Scientific writing in general


Faculty of Science 6.9.2017Orientation to Data Science Studies / Pirjo Moen 3

INTRODUCTION TO THE COURSE

Faculty of Science

POSITION IN THE DEGREE STRUCTURE

• A compulsory course for students in the Master’s Programmein Data Science

• Aimed only for the students in that programme

o Not for students in other programmes

o Not for exchange or visiting students

• A part of module Academic Skills for Data Skills

o Other courses in the module are Academic Writing 1 and 2


Faculty of Science

THEMES OF THE COURSE

• Studying in the Master’s Programme in Data Science

o Degree structure

o Data Science Fest

o Planning of studies

• Learning skills in (data) science

• Scientific reading and writing

• Ethics in writing


Faculty of Science

AIMS OF THIS COURSE

• Developing skills needed for studying Data Science

• Especially

o Learning the scientific writing practices used in data science

o Building own processes for following and finding information on

interesting areas in data science

o Developing own processes for producing good scientific text


Faculty of Science

LECTURES

• Held on Wednesdays at 9.15-11.00 in B120


Date Topic6.9.2017 Introduction, studying data science, material search

13.9.2017 Reading and reviewing articles, editing tools

20.9.2017 Writing process

27.9.2017 Ethics of writing

4.10.2017 Scientific writing after this course

11.10.2017 No lectures; advice on writing, if needed

18.10.2017 No lectures; advice on writing, if needed

Faculty of Science

EXERCISES

• MAIN TASK:o To write a 5-page scientific report (article or essay) based on

existing scientific journal and/or conference articles using your own voice

• Individual writing task

• Peer-review of texts of other students

• Feedback from teachers

• No language teaching


Faculty of Science

EXERCISES (2)

• You will practise and learn:

o How to find relevant material

o How to read peer-reviewed scientific articles

o How to review articles

o What is the process of writing and editing

o How to structure your own text

o How to write scientific text in a (possibly) new language

o How to give feedback on text written by someone else


Faculty of Science

YOUR SCIENTIFIC REPORT

• A short scientific report

o 5 pages in the given layout

o Scientific style, good written English

o Written in LaTeX

o Based on only journal, conference or workshop articles

• A data science related topic

o Research done at the Departments related the Programme

o Related research done elsewhere

=> Possibility to be acquainted with the research done at UH


Faculty of Science

STRICT SCHEDULE

• Week 1 (6. - 13.9.): Material search, bibliography

• Week 2 (13.9. - 20.9.): Report layout, table of contents, reading

and material search

• Week 3 (20.9. - 27.9.): Writing the text

• Week 4 (27.9. - 4.10.): Draft version of the report to be

submitted for peer-review by Wednesday 4th of October, 2017

• Week 5 (4.10. - 11.10.): Feedback from the draft, editing your

work

• Deadline for submitting the final report: Sunday 22nd of

October, 2017 (end of Period 1)


Faculty of Science

COMPLETING THE COURSE

• No exam!

• Writing a scientific essay / report

• Active participation in the lectures

• Completing tasks given in the lectures

• Grading on scale Pass / Fail



STUDYING DATA SCIENCE

Faculty of Science

MASTER’S DEGREE IN DATA SCIENCE

• New 2-year master’s degree programme

• Three departments involved

o Department of Computer Science

o Department of Mathematics and Statistics

o Department of Physics

• Strongly science and research oriented programme

6.9.2017 14Orientation to Data Science Studies / Pirjo Moen

Faculty of Science

DEGREE STRUCTURE


Master’s degree in Data Science (120 cr)

Advanced studies in Data Science (at least 85 cr)

Core studies (35 cr)

Specialization studies (20 cr)

Master’s thesis (30 cr) and Maturity test

Other studies (up to 35 cr)

Faculty of Science

CORE STUDIES

• The core studies consist of 35 credits worth of compulsory key courses:

o DATA11001 Introduction to Data Science (5 cr)

o DATA11002 Introduction to Machine Learning (5 cr)

o DATA11003 Distributed Data Infrastructures (5 cr)

o MAT22005 Bayesian Inference (5 cr)

o DATA11005 Data Science Seminar (5 cr)

o DATA11004 Data Science Project I or some other project (5 cr)

o DATA11100 Academic Skills for Data Science (5 cr)


Faculty of Science

SPECIALIZATION STUDIES

• At least 20 credits for data science specialization courses

• Divided thematically into five key areas of data science:

o Algorithmic data science

o Computer and cognition

o Data science infrastructures (core course: Distributed Data Infrastructures)

o Machine learning (core course: Introduction to Machine Learning)

o Statistical data science (core course: Bayesian Inference)


Faculty of Science

SPECIALIZATION STUDIES (2)

• Most of the specialization courses are worth 5 credits

• Specialization modules are worth 15 credits

• No specialization modules required

• Possible to include 1-4 specialization modules into the degree


Faculty of Science

ACADEMIC SKILLS FOR DATA SCIENCE

• A compulsory course module for 5 credits

• Consists of three courses

o DATA11101 Orientation to Data Science Studies (1 cr)

o 993734 Academic Writing for Students in English-Medium Master's Degree Programmes 1 (2 cr)

o 993735 Academic Writing for Students in English-Medium Master's Degree Programmes 2 (2 cr)


Faculty of Science

DATA SCIENCE SEMINARS

• One seminar is compulsory, two can be included to the degree

• Basically, all data science related seminars are suitable for this purpose

• Seminars are organised by

o Master’s Programme in Data Science, or

o Master’s Programme in Computer Science


Faculty of Science

DATA SCIENCE PROJECTS

• One project is compulsory, two can be included to the degree

• Solving practical data science challenges in a team

• Projects are organised by

o Master’s Programme in Data Science, or

o Master’s Programme in Linguistic Diversity in the Digital Age


Faculty of Science

MASTER’S THESIS + MATURITY TEST

• Master’s thesis is worth of 30 credits

• MSc thesis should demonstrate

o the ability for scientific thinking,

o command of scientific methods,

o conversance with the topic of the thesis, and

o aptitude for scientific communication.

• Thesis work takes usually half a year, and the result is a scientific study of 40-60 pages

o Can be literature survey, experimental evaluation, development of a software tool, new theoretical research, etc.

• Maturity test = Abstract of the thesis


Faculty of Science

DATA SCIENCE FEST

• Monthly meeting of the programme on Fridays at 14.15-16.00 in Exactum B120

o Autumn 2017: 8.9., 13.10., and 24.11.

o Spring 2018: 19.1., 9.2., 16.3., 13.4., and 4.5.

• Programme will include, for example,

o Master’s thesis presentations

o PhD student presentations

o Research talks

o Project presentations

o Invited talks


Faculty of Science

DEGREE STRUCTURE


Master’s degree in Data Science (120 cr)

Common Advanced Studies in Data Science (at least 50 cr)

Introduction to Data Science (5 cr)

Professional Skills in Data Science (at least 15 cr)

Specialization studies in Data Science (at least 35 cr)

Master’s thesis (30 cr) and Maturity test

Other studies (up to 35 cr)

Faculty of Science

TRANSFERRING RULES

• Already >= 15 credits studies in Data Science

o Introduction to Data Science

o Bayesian Inference

o Academic Skills for Data Science

• Distributed Data Infrastructures can be replace by Distributed Systems (completed 2016 or before that) or Big Data Frameworks

• Introduction to Big Data Management can be replaced by Algorithms and Systems for Big Data Management


NOT COMPULSORY}

Faculty of Science

TRANSFERRING RULES (2)

• Data Science Seminar I can be replaced by any computer science or statistics seminar

• Data Science Project I can be replaced by former CS project courses (Data Mining Project, Probabilistic Models Project or Distributed Systems Project)

• All CS algorithms and machine learning specialization courses, data science profile courses, and statistics courses can be used as optional courses

• Orientation to Data Science Studies can be replaced by course Scientific Writing for MSc Students in Computer Science



PERSONAL STUDY PLAN IN DATA SCIENCE

Faculty of Science

WHY PLANNING OF STUDIES IS NEEDED?

• Unique experts at graduation

o Different course selections

o Own, unique thesis

• Allows unexpected combinations

• Gives possibility to learn to manage own plans and schedules


Faculty of Science

PLANNING OF STUDIES

• The size of the MSc degree is 120 credits

• Only few compulsory courses (35 credits)

• Rest of the courses you need to select

o to fulfil the degree requirements

o to support your thesis

o to be interesting for yourself

• Advice in making a PSP in Data Science:

o Education Coordinator Reijo Sivèn, [email protected], D239


Faculty of Science

TEACHING PERIODS 2017-2018

• 4 periods (7 weeks teaching + one exam week)

o I Period: September 4 - October 22 (exams: 23.10.-29.10.)

o II Period: October 30 - December 17 (exams: 18.12.-22.12.)

o [Intensive period in January (2.1.-14.1.)]

o III Period: January 15 - March 4 (exams: 5.3.-11.3.)

o IV Period: March 12 - May 6 (exams: 7.5.-13.5.)

o Intensive period in May (7.5. – 31.5.)


Faculty of Science

REGISTRATION FOR COURSES AND EXAMS

• Registration is done in Weboodi

• Registration for lecture courses typically start 2-4 weeks before the beginning of the period

• Registration for separate exams must be done at latest 10 days before the exam

o If you want the exam questions in English, contact the examiner two weeks before the exam


Faculty of Science

IMPORTANT GUIDES AND LINKS

• Instructions for students: http://guide.student.helsinki.fi/en

• My studies: https://student.helsinki.fi/info/login

• Weboodi: https://weboodi.helsinki.fi

• Course pages: https://courses.helsinki.fi

• Web pages of the programme

o https://www.helsinki.fi/en/programmes/master/data-science



LEARNING SKILLS FOR SCIENCE

Faculty of Science

LEARNING SKILLS FOR (DATA) SCIENCE

• Information Retrieval

• Listening & Observing

• Scientific Reading

• Data representation

• Scientific Writing

• Knowledge Presentation


A. Hunt, Z. Scherz, O. Spektor-Levy, S. Johnson, and R. R'kaina

Faculty of Science

LEARNING SKILLS FOR (DATA) SCIENCE (2)


Source: The Learning Skills for Science programmehttp://www.sep.org.uk/lss.asp


SCIENTIFIC WRITING

Faculty of Science

WHAT IS SCIENTIFIC WRITING?

• Describing new scientific ideas and results for other scientists and the science community

o New ideas

o Combining old ideas in a new way

• Evaluation of the presented ideas/results

o Correctness

o Relationship to former ideas and results

• New results (new contribution) build on existing knowledge


Faculty of Science

WHAT IS SCIENTIFIC WRITING? (2)

• Scientific results are accepted when they have been

independently reviewed and published

• Different types of scientific publications with their own

characteristics

• Scientific writing is an important part of the process of science, i.e., the process of accumulating reliable knowledge

• Scientific writing itself is also a process


Faculty of Science

HOW DO YOU LEARN SCIENTIFIC WRITING?

• Following the topic area and reading relevant articles

• Writing yourself

• Searching for feedback from others

o Peer students

o Teachers

o ...

• Iterative process!


Faculty of Science

SCIENTIFIC INFORMATION RETRIEVAL

• Every research project is based on former known research

• Finding relevant source material is important, but can be very challenging

• Most data science articles are nowadays available in digital form


Faculty of Science

TYPES OF PUBLICATIONS

• Course books

• Other kinds of text books

• Standards

• Journal articles

• Conference articles

• Theses

• Technical reports

• Manuscripts

• Web documents/publications

• …


Faculty of Science

NATURE OF PUBLICATIONS

• Primary sources

o Original, reliable information

o Articles in scientific journals, conference and workshop books,

research reports, theses

o Patents, laws and directives, standards

• Secondary sources

o Useful material for restructuring and analysis of existing

information, not original and new results

o Course books, article collections, surveys, newsletters,

dictionaries, etc.


Faculty of Science

BIBLIOGRAPHIES AND DIGITAL LIBRARIES

• Bibliographies are collections of the most important bibliographical facts of articles

• Digital libraries are collections of digital versions of articles published by a certain publisher

• Important digital libraries for data scientists are, for example,

o The ACM Digital Library (http://portal.acm.org/dl.cfm)

o IEEE Xplore Digital Library (http://ieeexplore.ieee.org/Xplore/dynhome.jsp)

o SpringerLink (http://www.springerlink.com)

o arXiv.org (https://arxiv.org/)


Faculty of Science

BIBLIOGRAPHIES AND DIGITAL LIBRARIES (2)

• University of Helsinki have a license for these digital libraries

• List of available digital libraries and bibliographies can be found

from the Search Library Resources page of the Helsinki

University Library

• Use of the libraries and bibliographies is possible in the

network of the University

• They can also be used via Authenticating Proxy (ask help from

the IT Services, [email protected])


Faculty of Science

KUMPULA CAMPUS LIBRARY

• http://www.helsinki.fi/kirjasto/en/library-locations/kumpula/

• To borrow books from the library, you need a Helsinki University Library Helka card

• To access the e-resources (for example, digital libraries), you need a user account for the university IT systems


Faculty of Science

SEARCH ENGINES

• There are several search engines specialised in scientific information retrieval

• Examples of such engines are

o Google Scholar (http://scholar.google.com/)

o SiteSeer.IST (http://citeseer.ist.psu.edu/)

o Elsevier's ScienceDirect (http://www.sciencedirect.com)

o Elsevier’s Scopus (https://www.scopus.com/)

• Other relevant databases and search engines can be found in Wikipedia's article Academic databases and search engines


Faculty of Science

OTHER SOURCES OF MATERIAL

• Citation indexes

• Following reference chains

• Web pages of

o individual researchers,

o research groups,

o departments, and

o universities

• Scientific libraries

o books and journals,

o technical reports, ...


Faculty of Science

NOTES FOR RETRIEVED MATERIAL

• Only few of the retrieved articles are central, most of them are auxiliary

o Many references to central sources

o A few references to auxiliary sources

• When you find an interesting article, write directly down at least the bibliographical data of the article

• It is also good to write down a short summary of the article

• Start making the notes from the very beginning!


orientation to data science studies (1 cr) · •held on wednesdays at 9.15-11.00 in b120...

Documents