Author Archive: ryandata

Statistical Software and Data Workshops, Fall 2018

New Brunswick Libraries Data Workshop Series

Fall 2018

This Fall, Ryan Womack, Data Librarian, will offer a series of workshops on statistical software and data visualization as part of New Brunswick Libraries Data Management Services.   A detailed calendar and descriptions of each workshop are below.  The workshop on reproducible research is moving online to YouTube – stay tuned for an upcoming blog post and announcement on its availability.   We also anticipate offering additional workshops through the Graduate Specialist program.  That announcement will be coming in September.

This semester each workshop topic will be repeated twice in person, once at the Library of Science and Medicine on Busch Campus, and once at Alexander Library on College Ave.  These sessions will be identical except for location. Sessions will run approximately 3 hours.  Workshops in parts will divide the time in thirds.  For example, the first SPSS, Stata, and SAS workshop (running from 1:10-4:10 pm) would start with SPSS at 1:10 pm, Stata at 2:10 pm, and SAS at 3:10 pm.  You are free to come only to those segments that interest you.  There is no need to register, just come!   A

Logistics

Location: The Library of Science and Medicine (LSM on Busch) workshops will be held in the Conference Room on the 1st floor of LSM on Mondays from 12 to 3 pm.  The Alexander Library (College Ave) workshops will be held in room 413 of the Scholarly Communication Center (4th floor of Alexander Library) from on Tuesdays from 1:10 to 4:10 pm.

For both locations, you are encouraged to bring your own laptop to work in your native environment.  Alternatively, at Alexander Library, you can use a library desktop computer instead of your own laptop.  At LSM, we will have laptops available to borrow for the session if you don’t bring your own.  Room capacity is 25 in both locations, first come, first served.

If you can’t make the workshops, or would like a preview or refresher, screencast versions of many of the presentations are already available at https://libguides.rutgers.edu/data and https://youtube.com/librarianwomack. Additional screencasts are continually being added to this series.  Note that the “special topics” [Time Series, Survival Analysis, and Big Data] are no longer offered in person, but are available via screencast.

Calendar of workshops

Wednesday (LSM)

12 noon – 3 pm

  Thursday (Alexander)

1:10 pm -4:10 pm 

October 3 Introduction to SPSS, Stata, and SAS September 13
October 10 Introduction to R September 20
October 17 Data Visualization in R September 27

Description of Workshops:

§ Introduction to SPSS, Stata, and SAS (September 13 or October 3) provides overviews of these three popular commercial statistical software programs, covering the basics of navigation, loading data, graphics, and elementary descriptive statistics and regression using a sample dataset.  If you are already using these packages with some degree of success, you may find these sessions too basic for you.

  • SPSS is widely used statistical software with strengths in survey analysis and other social science disciplines.  Copies of the workshop materials, a screencast, and additional SPSS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208425. SPSS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SPSS is also available in campus computer labs and via the Apps server (see below).
  • Stata is flexible and allows relatively easy access to programming features.  It is popular in economics among other areas.  Copies of the workshop materials, a screencast, and additional Stata resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208427. Stata is made available by OIRT via campus license with no additional charge to install for Rutgers users.  Find it at software.rutgers.edu.
  • SAS is a powerful and long-standing system that handles large data sets well, and is popular in the pharmaceutical industry and health sciences, among other applications. Copies of the workshop materials, a screencast, and additional SAS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208423. SAS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SAS is also available in campus computer labs, online via the SAS University Edition cloud service, and via the Apps server (see below).

Note: Accessing software via apps.rutgers.edu

SPSS, SAS, Stata, and R are available for remote access on apps.rutgers.eduapps.rutgers.edu does not require any software installation, but you must activate the service first at netid.rutgers.edu.

§ Introduction to R (September 20 or October 10) – This session provides a three-part orientation to the R programming environment.  R is freely available, open source statistical software that has been widely adopted in the research community.  Due to its open nature, thousands of additional packages have been created by contributors to implement the latest statistical techniques, making R a very powerful tool.  No prior knowledge is assumed. The three parts cover:

  • Statistical Techniques: getting around in R, descriptive statistics, regression, significance tests, working with packages
  • Graphics:  comparison of graphing techniques in base R, lattice, and ggplot2 packages
  • Data Manipulation:  data import and transformation, additional methods for working with large data sets, also dplyr and other packages from the tidyverse useful for manipulation.

Additional R resources, including handouts, scripts, and screencast versions of the workshops, can be found here: http://libguides.rutgers.edu/data_R

R is freely downloadable from http://r-project.org

§ Data Visualization in R  (September 27 or October 17) discusses principles for effective data visualization, and demonstrates techniques for implementing these using R.  Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.  The three parts are:

  • Principles & Use in lattice and ggplot2: discusses classic principles of data visualization (Tufte, Cleveland) and illustrates them with the use of the lattice and ggplot2 packages.  Some of the material here overlaps with Intro to R, pt 2, but at a higher level.
  • Miscellany of Methods: illustrates a wide range of specific graphics for different contexts
  • 3-D, Interactive, and Big Data: presentation of 3-D data, interactive exploration data, and techniques for large datasets. Relevant packages such as shiny and tessera are explored.

Additional R resources can be found here: http://libguides.rutgers.edu/data_R

R is freely downloadable from http://r-project.org

pyramid

 § Special Topics

Note that the following special topics are no longer covered by in-person workshops, but are available via screencast.

Advertisements

Mongolian Multivariate Statistics (at the Mongolian University of Life Sciences)

I was delighted to be invited to return to Mongolia again for a more in-depth visit to the Mongolian University of Life Sciences [Хөдөө Аж Ахуйн Их Сургууль].  I was honored by the invitation from the Dean of the School of Economics and Business [Эдийн засаг бизнесийн сургууль], B. Baasansukh, and P. Munkhtuya, Chair of the Department of Economic Statistics and Mathematical Modeling, to come and teach a one week Short Course on Applied Multivariate Statistical Methods with R [Олон хэмжээст статистикийн богино хэмжээний сургалт R программ дээр].  This post is a brief summary of my trip.

P1020579P1020580

I got to Ulaanbaatar on my first ever flight on MIAT Mongolian Airlines, which was quite comfortable.

On Monday, February 26, we jumped right into work.  The material for the short course was adapted from the book and associated R code by Brian Everitt and Torsten Hothorn, An Introduction to Applied Multivariate Statistical Analysis with R, Springer, 2011.  The eight chapters of the book were covered in four days.  Topics covered were the following:

  • R environment, setup, basics
  • Multivariate Analysis – what is it?
  • Data Exploration and Visualization
  • Principal Components
  • Multidimensional Scaling
  • Exploratory Factor Analysis
  • Confirmatory Factor Analysis
    • Structural Equation Modeling
  • Cluster Analysis
  • Repeated Measures

The Everitt and Hothorn text was particularly useful for its compact treatment of complex topics, and the self-contained nature of the included R code demo modules.

 

 

This slideshow requires JavaScript.

There were a total of 31 participants attending, although course and meeting conflicts prevented some from coming every day.  The MULS School of Economics and Business had 20 participants, with the remaining attendees coming from the Division of Science and Research, the School of Agroecology, the School of Veterinary Medicine, and the School of Engineering and Technology.  Outside of MULS, participants also came from the National Statistics Office of Mongolia and the Cabinet Secretariat of the Government of Mongolia.

 

 

This slideshow requires JavaScript.

All participants followed along by executing sample code on their own laptops.  All presentation materials and code are available from https://github.com/ryandata/multivariate.  Supplemental texts and files were made available using a portable PirateBox to distribute materials via local wireless network.  See this post on how to set up a PirateBox.

 

 

The interaction was lively and particularly aided by assistance in translation from G. Bilguun, O. Amartuvshin, and T. Suvdmaa.  I hope the participants enjoyed it as much as I did!

 

 

Putting our knowledge of R to immediate use, we used R to select a random sample of winners of swag provided by Rutgers’ Master of Business and Science program.

 

 

During the week, I also had the opportunity for several meetings to discuss future collaborations on projects to improve the academic and data infrastructure of MULS, which I believe will be the subject of future collaborations.  The week also included some delicious food, including a hot pot dinner and khorkhog [хopxoг] and khuushuur [хуушууp].

 

 

On Thursday afternoon, I had the opportunity to address a group of undergraduate statistics majors on trends in data science and its intersection with statistics.  I argued that the expansion in the power and availability of open source software and data has made it possible for anyone, from Mongolia or even the United States (where there are arguably more distractions) to study and master the tools and skills that underpin the most dynamic growth sectors for future jobs.

 

 

This slideshow requires JavaScript.

Finally on Friday, we wrapped up the course with some final discussion and the presentation of certificates to participants.

 

 

This slideshow requires JavaScript.

Mongolia is always a land of warm welcome and surprises.  Thanks to my hosts at SEB-MULS for a fantastic trip, filled with learning.  Thanks to D Music for inspiration as always.  And thanks especially to P. Munkhtuya for leading all of the organizational efforts for my visit. I am looking forward to working together with MULS colleagues in the future!  Маш их баярлалаа!

 

 

Statistical Software and Data Workshops, Spring 2018

New Brunswick Libraries Data Workshop Series

Spring 2018

This Spring, Ryan Womack, Data Librarian, will repeat the series of workshops on statistical software and data visualization as part of New Brunswick Libraries Data Management Services.   A detailed calendar and descriptions of each workshop are below.  The workshop on reproducible research is moving online to YouTube – stay tuned for an upcoming blog post and announcement on its availability.

This semester each workshop topic will be repeated twice, once at the Library of Science and Medicine on Busch Campus, and once at Alexander Library on College Ave.  These sessions will be identical except for location. Sessions will run approximately 3 hours.  Workshops in parts will divide the time in thirds.  For example, the first SPSS, Stata, and SAS workshop (running from 12-3 pm) would start with SPSS at 12 pm, Stata at 1 pm, and SAS at 2 pm.  You are free to come only to those segments that interest you.  There is no need to register, just come!

Logistics

Location: The Library of Science and Medicine (LSM on Busch) workshops will be held in the Conference Room on the 1st floor of LSM on Mondays from 12 to 3 pm.  The Alexander Library (College Ave) workshops will be held in room 413 of the Scholarly Communication Center (4th floor of Alexander Library) from on Tuesdays from 1:10 to 4:10 pm.

For both locations, you are encouraged to bring your own laptop to work in your native environment.  Alternatively, at Alexander Library, you can use a library desktop computer instead of your own laptop.  At LSM, we will have laptops available to borrow for the session if you don’t bring your own.  Room capacity is 25 in both locations, first come, first served.

If you can’t make the workshops, or would like a preview or refresher, screencast versions of many of the presentations are already available at http://libguides.rutgers.edu/data and https://youtube.com/librarianwomack. Additional screencasts are continually being added to this series.  Note that the “special topics” [Time Series, Survival Analysis, and Big Data] are no longer offered in person, but are available via screencast.

Calendar of workshops

Monday (LSM)

12 noon – 3 pm

  Tuesday (Alexander)

1:10 pm -4:10 pm 

January 29 Introduction to SPSS, Stata, and SAS January 30
February 5 Introduction to R February 6
February 12 Data Visualization in R February 13

Description of Workshops:

§ Introduction to SPSS, Stata, and SAS (January 29 or January 30) provides overviews of these three popular commercial statistical software programs, covering the basics of navigation, loading data, graphics, and elementary descriptive statistics and regression using a sample dataset.  If you are already using these packages with some degree of success, you may find these sessions too basic for you.

  • SPSS is widely used statistical software with strengths in survey analysis and other social science disciplines.  Copies of the workshop materials, a screencast, and additional SPSS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208425. SPSS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SPSS is also available in campus computer labs and via the Apps server (see below).
  • Stata is flexible and allows relatively easy access to programming features.  It is popular in economics among other areas.  Copies of the workshop materials, a screencast, and additional Stata resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208427. Stata is made available by OIRT via campus license with no additional charge to install for Rutgers users.  Find it at software.rutgers.edu.
  • SAS is a powerful and long-standing system that handles large data sets well, and is popular in the pharmaceutical industry and health sciences, among other applications. Copies of the workshop materials, a screencast, and additional SAS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208423. SAS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SAS is also available in campus computer labs, online via the SAS University Edition cloud service, and via the Apps server (see below).

Note: Accessing software via apps.rutgers.edu

SPSS, SAS, Stata, and R are available for remote access on apps.rutgers.eduapps.rutgers.edu does not require any software installation, but you must activate the service first at netid.rutgers.edu.

§ Introduction to R (February 5 or February 6) – This session provides a three-part orientation to the R programming environment.  R is freely available, open source statistical software that has been widely adopted in the research community.  Due to its open nature, thousands of additional packages have been created by contributors to implement the latest statistical techniques, making R a very powerful tool.  No prior knowledge is assumed. The three parts cover:

  • Statistical Techniques: getting around in R, descriptive statistics, regression, significance tests, working with packages
  • Graphics:  comparison of graphing techniques in base R, lattice, and ggplot2 packages
  • Data Manipulation:  data import and transformation, additional methods for working with large data sets, also dplyr and other packages from the tidyverse useful for manipulation.

Additional R resources, including handouts, scripts, and screencast versions of the workshops, can be found here: http://libguides.rutgers.edu/data_R

R is freely downloadable from http://r-project.org

§ Data Visualization in R  (February 12 or February 13) discusses principles for effective data visualization, and demonstrates techniques for implementing these using R.  Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.  The three parts are:

  • Principles & Use in lattice and ggplot2: discusses classic principles of data visualization (Tufte, Cleveland) and illustrates them with the use of the lattice and ggplot2 packages.  Some of the material here overlaps with Intro to R, pt 2, but at a higher level.
  • Miscellany of Methods: illustrates a wide range of specific graphics for different contexts
  • 3-D, Interactive, and Big Data: presentation of 3-D data, interactive exploration data, and techniques for large datasets. Relevant packages such as shiny and tessera are explored.

Additional R resources can be found here: http://libguides.rutgers.edu/data_R

R is freely downloadable from http://r-project.org

pyramid

 § Special Topics

Note that the following special topics are no longer covered by in-person workshops, but are available via screencast.

Statistical Software and Data Workshops, Fall 2017

New Brunswick Libraries Data Workshop Series

Fall 2017

This Fall, Ryan Womack, Data Librarian, will offer a series of workshops on statistical software, data visualization, and reproducible research as part of New Brunswick Libraries Data Management Services.   A detailed calendar and descriptions of each workshop are below.  This semester each workshop topic will be repeated twice, once at the Library of Science and Medicine on Busch Campus, and once at Alexander Library on College Ave.  These sessions will be identical except for location. Sessions will run approximately 3 hours.  Workshops in parts will divide the time in thirds.  For example, the first SPSS, Stata, and SAS workshop (running from 12-3 pm) would start with SPSS at 12 pm, Stata at 1 pm, and SAS at 2 pm.  You are free to come only to those segments that interest you.  There is no need to register, just come!

Logistics

Location: The Library of Science and Medicine (LSM on Busch) workshops will be held in the Conference Room on the 1st floor of LSM on Wednesdays from 12 to 3 pm.  The Alexander Library (College Ave) workshops will be held in room 413 of the Scholarly Communication Center (4th floor of Alexander Library) from on Tuesdays from 1:10 to 4:10 pm.

For both locations, you are encouraged to bring your own laptop to work in your native environment.  Alternatively, at Alexander Library, you can use a library desktop computer instead of your own laptop.  At LSM, we will have laptops available to borrow for the session if you don’t bring your own.  Room capacity is 25 in both locations, first come, first served.

If you can’t make the workshops, or would like a preview or refresher, screencast versions of many of the presentations are already available at http://libguides.rutgers.edu/data and https://youtube.com/librarianwomack. Additional screencasts are continually being added to this series.  Note that the “special topics” [Time Series, Survival Analysis, and Big Data] are no longer offered in person, but are available via screencast.

Calendar of workshops

Tuesday (Alexander)

1:10 pm -4:10 pm

   Wednesday (LSM)

12 noon – 3 pm

September 12 Introduction to SPSS, Stata, and SAS September 13
September 19 Introduction to R September 20
September 26 Data Visualization in R September 27
October 3 Reproducible Research October 18

Description of Workshops:

§ Introduction to SPSS, Stata, and SAS (September 12 or September 13) provides overviews of these three popular commercial statistical software programs, covering the basics of navigation, loading data, graphics, and elementary descriptive statistics and regression using a sample dataset.  If you are already using these packages with some degree of success, you may find these sessions too basic for you.

  • SPSS is widely used statistical software with strengths in survey analysis and other social science disciplines.  Copies of the workshop materials, a screencast, and additional SPSS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208425. SPSS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SPSS is also available in campus computer labs and via the Apps server (see below).
  • Stata is flexible and allows relatively easy access to programming features.  It is popular in economics among other areas.  Copies of the workshop materials, a screencast, and additional Stata resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208427. Stata is made available by OIRT via campus license with no additional charge to install for Rutgers users.  Find it at software.rutgers.edu.
  • SAS is a powerful and long-standing system that handles large data sets well, and is popular in the pharmaceutical industry and health sciences, among other applications. Copies of the workshop materials, a screencast, and additional SAS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208423. SAS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SAS is also available in campus computer labs, online via the SAS University Edition cloud service, and via the Apps server (see below).

Note: Accessing software via apps.rutgers.edu

SPSS, SAS, Stata, and R are available for remote access on apps.rutgers.eduapps.rutgers.edu does not require any software installation, but you must activate the service first at netid.rutgers.edu.

 

§ Introduction to R (September 19 or September 20) – This session provides a three-part orientation to the R programming environment.  R is freely available, open source statistical software that has been widely adopted in the research community.  Due to its open nature, thousands of additional packages have been created by contributors to implement the latest statistical techniques, making R a very powerful tool.  No prior knowledge is assumed. The three parts cover:

  • Statistical Techniques: getting around in R, descriptive statistics, regression, significance tests, working with packages
  • Graphics:  comparison of graphing techniques in base R, lattice, and ggplot2 packages
  • Data Manipulation:  data import and transformation, additional methods for working with large data sets, also dplyr and other packages from the tidyverse useful for manipulation.

Additional R resources, including handouts, scripts, and screencast versions of the workshops, can be found here: http://libguides.rutgers.edu/data_R

R is freely downloadable from http://r-project.org

 

§ Data Visualization in R  (September 26 or September 27) discusses principles for effective data visualization, and demonstrates techniques for implementing these using R.  Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.  The three parts are:

  • Principles & Use in lattice and ggplot2: discusses classic principles of data visualization (Tufte, Cleveland) and illustrates them with the use of the lattice and ggplot2 packages.  Some of the material here overlaps with Intro to R, pt 2, but at a higher level.
  • Miscellany of Methods: illustrates a wide range of specific graphics for different contexts
  • 3-D, Interactive, and Big Data: presentation of 3-D data, interactive exploration data, and techniques for large datasets. Relevant packages such as shiny and tessera are explored.

Additional R resources can be found here: http://libguides.rutgers.edu/data_R

R is freely downloadable from http://r-project.org

pyramid

 

§ Reproducible Research (October 3 or October 18) covers

  • Reproducible research describes the growing movement to make the products of research accessible and usable by others in order to verify, replicate, and extend research findings.  This session reviews how to plan research, to create publications, code, and data in open, reusable formats, and maximize the impact of shared research findings.  Examples in LaTeX and Rmarkdown are discussed, along with platforms for reusability such as the Open Science Foundation.

Additional resources on reproducible research and data management, including presentation slides, can be found here: http://libguides.rutgers.edu/datamanagement

 

§ Special Topics

Note that the following special topics are no longer covered by in-person workshops, but are available via screencast.

PirateBox for Data Literacy

pb

Distributing files via PirateBox

A PirateBox is a wireless router that has been reconfigured to serve as a local fileserver.  The PirateBox project develops software to do this.  PirateBox makes it easy to share files with anyone within range of the router, and also supports a local anonymous discussion board for those within range.

I did this for the TP-Link TL-MR3040, a commonly used piece of hardware for PirateBoxes.  The MR3040 is small and battery powered, so you can easily carry it in your pocket to places that have no electricity or internet.  The file system goes on a removable USB, so it is easy to set up by just copying stuff from your computer to the USB.

Configuration is not really that difficult if you follow the instructions here at the PirateBox project site.

To customize your SSID (the name of your wireless device) and Home Page you can follow these instructions.

Workshops hosted on PirateBox

I have used my PirateBox to share workshop slides, articles, and code.  Up to down this has been a small supplement to my normal workshops, but it could have a larger role.

What I would like to do is to create a self-contained training environment that would not depend on the vagaries of local configuration and connectivity issues.  Following a “train the trainer” model, one could build a PirateBox with an entire data literacy course running off of web pages on the PirateBox.  The PirateBox could include all necessary software (a complete R installation, for example) and a collection of supporting documents, datasets, code, and any other information. This material could also be mirrored to/from a regular website, but the portable and self-contained aspect of the PirateBox opens up many possibilities.

So the trainer could walk into a room anywhere in the world (for example a small Mongolian town – сум), with their PirateBox and lead a workshop based on materials that reside in their entirety on the PirateBox.  Then leave the PirateBox behind so that those in the community could continue to work with the materials and any additional modules.  They could adapt, repurpose, and create their own materials too. So the PirateBox can support ongoing learning, far beyond  the limits of one-shot workshops.

These are not especially new ideas, and even as I type people are surely hacking wireless routers and other devices to perform other advanced functions.  Doubtless the technology will continue to develop.  But for now, the PirateBox software allows one to do interesting work with less than $50 in hardware and a couple of hours in setup time.  Who knows? One can dream of hordes of data literacy pirates emerging from this simple technology.

Data Science in Mongolia – Маш их сайн! (very good!)

While it is the subject for another blog post or another blog, Mongol culture has long held my fascination. Thanks to a series of fortunate events, I had the opportunity to bring some of my favorite interests (data, statistics, R, Mongolian) all together to form an unforgettable experience in May 2017.  During one week in Ulaanbaatar, I visited three of the oldest, largest, and most important Mongolian universities, as well as the nerve center of Mongolian data, the National Statistics Office.

4kj4kj8qcjb4tkr5jc1lomr1deNational Statistics Office of Mongolia

The first and most intensive event was my invitation to present two days of workshops on Data Science at the National Statistics Office of Mongolia (Монгол Улсын Үндэсний статистикийн хороо). On May 8 and May 9,  I delivered all-day presentations and interactive training on Data Visualization, Big Data, Reproducible Research, and Data Literacy. The presentation slides in English and accompanying Mongolian translation are available here.

 

This slideshow requires JavaScript.

We covered lots of ground, and I was also able to learn about the data environment in Mongolia and some of NSO’s data dissemination efforts such as the 1212.mn data portal. The facilities at NSO were superb, and the audience of 33, consisting primarily of government data professionals from the NSO and other Mongolian agencies were an outstanding group. It was truly a privilege to be able to work with them.  An article (in Mongolian) about the event is here.

In particular, I would like to thank Ch. Davaasuren (Research and Development Director of the Mongolian Marketing Consulting Group for arranging the event, to L. Myagmarsuren (Director of Information Technology at NSO) for hosting it, and to A. Ariunzaya (Chair of NSO) for the invitation.

These three can be seen at the opening of the event [at the link below], along with me and my poor Mongolian – уучлаарай (sorry!).  I promise it will improve!

NSO Data Science opening remarks

The event was greatly enriched by sponsorship from IASSIST, the International Association for Social Science Information Services and Technology.  IASSIST is developing outreach efforts to areas around the world, and provided translation services and lunch for workshop participants.  We had two days of delicious хуушуур (huushur), сүүтэй цай (milk tea), and other Mongolian specialties at Modern Nomads.  Joining IASSIST is a great way to get in touch with a worldwide network of data professionals!

 

This slideshow requires JavaScript.

National_University_of_Mongolia_Seal National University of Mongolia

On Wednesday, May 9, I spoke on Data Literacy to approximately 70 students of statistics at the National University of Mongolia (Монгол Улсын Их Сургууль). Even though the talk started at 7:40 am, students were attentive and asked probing questions. Clearly, they are the future of data science –very curious about career trends and the nature of the work and skills required.   I am sure they will succeed if they remain as focused as they were that day!  Амжилт хүсьё!

 

This slideshow requires JavaScript.

Thanks to D. Amarjargal for inviting me, and B. Myagmarsuren for translating!

ХААИСMongolian University of Life Sciences

On Thursday, May 10, I traveled to the southern side of Ulaanbaatar to speak at the Mongolian University of Life Sciences (Хөдөө Аж Ахуйн Их Сургууль), giving two presentations on Big Data and Data Visualization to a group of approximately 20 faculty of the School of Economics and Business.

 

 

This slideshow requires JavaScript.

The faculty here were very welcoming and discussed many issues in applying big data and visualization techniques to their work.  Many thanks to P. Munktuhya (Head of the Department of Economics, Statistics, and Mathematical Modeling) for arranging the event, to G. Ganzorig (Senior Lecturer in Agricultural and Applied Economics) for translation, and to B. Baasansukh (Dean of the School of Economics and Business) for the invitation.

I was also able to have a very informative and positive meeting with Ts. Sukhtulga (Chief of Administration and International Affairs) to discuss possibilities for cooperation with Rutgers University.  An article (in Mongolian) about my visit appeared here.  I really regretted not having more time to spend here!

MUST_logo_20130530082631

 

 

Mongolian University of Science and Technology

My final talk on Friday, May 11 was at the Mongolian University of Science and Technology (Шинжлэх Ухаан, Технологийн Их Сургууль), where I spoke on Big Data, Reproducible Research, and Data Visualization, hitting highlights from my earlier presentations during the week.

 

This slideshow requires JavaScript.

Approximately 40 faculty and students from MUST’s School of Business Administration and Humanities attended.  Once again, the audience was attentive and questioning up until the end, even though the talk was held late on Friday afternoon.  I was very impressed by the curiosity and dedication of the Mongolian academic community here, and throughout my trip.

At MUST, I would like to thank J. Oyuntungalag (Professor of Technology Management) for arranging the talk.  I also enjoyed a good meeting with U. Batbaatar and P. Jargaltuya of the Office of International Affairs and Cooperation.

On Friday, I was also able to spend some time at the Mongolian Marketing Consulting Group‘s offices to learn more about how they conduct polling, market research, and other data collection, thanks to the hospitality of Ch. Davaasuren.

It was such a memorable and rewarding experience that I must continue to thank those who made it possible, once again Ch. Davaasuren who helped throughout the week, and especially to M. Bayarmaa who worked tirelessly to organize many aspects of the week’s events and behind the scenes to keep things running smoothly.

I can only hope that this is the start of a long and productive collaboration with the Mongolian data world.

Би цагийг гайхалтай сайхан өнгөрөөсөн! (I had a glorious time!)

More Mongolian Universities

Now I will also be speaking at the Mongolian University of Life Sciences (ХААИС) on Thursday, May 11, and at Mongolian University of Science and Technology (ШУТИС) on Friday, May 12.  That gives me the chance to post their logos here:

I will be posting presentation materials and some reflections later on.

Mongolia GIS

While researching my upcoming Mongolia trip, I was amazed to discover a treasure trove of Mongolian data already at Rutgers.  Christopher Free, a quantitative ecologist, studies Mongolian fisheries, and is an R and GIS expert to boot.  He has compiled a one-stop archive for Mongolian GIS data and R courses that use Mongolian fish data as examples [much better than sports statistics!].

These are exactly the kinds of global connections I am delighted to make!

Traveling with a light digital footprint

Prepping for destination UB, I have decided to travel with a low cost digital devices with a more limited feature set, rather than leaving my electronic life exposed to loss, search, or seizure.  To that end, I am taking with me a low-cost Mobal Phone [sic] and a cheap laptop, albeit reconfigured to run a premium operating system, Debian.

The laptop is suitably tagged with the things that keep me running these days, R, Rutgers, and D Music.  The keyboard has been “Mongolized” as well.

I will report back later on whether this travel gear can get the job done.

20170418_14375120170418_14383720170418_144039

Data Science in Mongolia in May

Flag_of_Mongolia.svg

I am delighted to announce that I have been invited to present on several data science topics at the Mongolian National Statistics Office (Монгол Улсын Үндэсний Статистикийн Хороо) and the National University of Mongolia (Монгол Улсын Их Сургууль) from May 8th to 10th.

I will post more detailed commentary after the visit, but I am excited by the opportunity to meet colleagues and learn about the Mongolian environment, and explore the potential for collaboration on data issues.

National_University_of_Mongolia_Seal4kj4kj8qcjb4tkr5jc1lomr1de