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The CODATA-RDA Research Data Science School

Fecha de inicio:

02-12-2019

Hora de finalización:

05:00 pm

Lugar:

Sala MultiUsos CeNAT

Hora de inicio:

08:00 am

Fecha de finalización:

13-12-2019

Organizador:

CeNAT, RedCONARE

#dataSanJose2019

CODATA-RDA Reserch Data Science summer Schools

Curriculum and Programme Specifications 

imgCodata2019

 

Open and Responsible Research – Lou Bezuidenhout – sponsored by CONARE

 
Aims:
To consider the importance of open and responsible research
Learning Outcomes: At the end of this course a student will
  • Understand responsible conduct of research as it pertains to data science
  • Have a broad understanding of the Open Science movement
  • Have reflected on the impact Open Science on their own research and future career.

Course content:

  • Introduction to “open and responsible (data) science citizenship”
    • Responsible conduct of research intro
    • Open Science intro
  • Being open and responsible at home
    • Challenges to open and responsible science group work and discussion
    • Understanding open and responsible research in the “big picture”
    • Introduction to societal impact of data
    • Introduction to “infraethics”

How long: 4.5 hours

Research Data Management – Steve – S. Venkat – – sponsored by CONARE

Aims:

To have an understanding of the principles of research data management (RDM) and the impact of Openness and Sharing in Research

Learning Outcomes:

At the end of this course a student will

  • Understand the data curation lifecycle
  • Appreciate the practical advantages of good RDM and open research/science
  • How to add value and longevity to your data
  • Understand the principles and importance of standardisation
  • How to publish data

Course content:

  • Incentives for curation
  • The data curation life-cycle
  • FAIR principles
  • Open vs FAIR
  • File formats
  •  Metadata
  • Ontologies
  • Licenses
  • Repositories
  • Persistent identifiers (PIDs)
  • Data management plans (DMPs)
  • Concurso de fondos en Centroamérica

How long: 1 day

Software Carpentry – Steve, Joao and Mariana – sponsored by RDA and CeNAT

Aims:

To have an introductory understanding of programming and software engineering skills to manipulate data and analyse data in reproducible fashion.

Learning Outcomes:

At the end of this course a student will

  • have an introductory understanding of the Unix shell,
  • be able to execute simple commands in Python,
  • be able to use Git.

Course content:

  • Introduction to the Unix shell.
  • File concepts in Unix.
  • Combining Unix commands, pipes and filters.
  • Shell scripts.
  • Functions in Python.
  • Conditionals in Python.
  • Command line Python programs.
  • Best practices in Python.
  • Setting up Git.
  • Tracking changes in Git.
  • Collaboration and Open Science with Git.

How long: 2 ½ days

Analysis – José Pablo and Raphael – sponsored by CeNAT and CODATA

Aims:

To have an understanding of the principles necessary to analyse data in terms of being able to make decisions from large amounts of data and applying machine learning techniques.

Learning Outcomes:

At the end of this course a student will

  • understand the basic principles of machine learning,
  • apply pipelines to build recommender systems,
  • understand how to use Artificial Neural Networks, with hands-on experience,
  • understand the principles of Boosted Decision Trees and SUpport Vector Machines.

Course content:

  • Machine learning concepts,
  • Recommender systems,
  • Artificial Neural Networks,
  • Other machine learning methods.

How long: 1 ½ days

 

Visualisation – Hugh – Sponsored by CODATA

Aims:

To have an understanding of the principles of visualising data. Learning Outcomes: At the end of this course a student will

  • understand how to use Python to perform visualisation,
  • be able to perform a critical assessment of effective visualisation techniques.

Course content:

  • Data wrangling.
  • Visualisation packages in R (such as ggplot2).
  • Visualisation in Python.
  • Workshop based approaches to critical assessment of visualisation.

How long: 1 day

Computational Infrastructures – Rob – sponsored by RDA

Aims:

To introduce students to open computational infrastructures available to them when analysis tasks outgrow their local computational resources.

Learning Outcomes:

At the end of this course a student will

  • understand the basic concepts of HTC, HPC and Cloud computing,
  • be able to execute a distributed computing job
  • be able to use more advanced features such as batch schedulers or containers.
  • Be able to interact with Cloud services

Course content:

  • Introduction to cloud computing concepts such as IaaS and PaaS and SaaS and their aspects.
  • Secure authentication mechanisms
  • Deploying scripts.
  • Interacting with mass storage repositories.
  • Use of batch schedulers of containers.
  • Adopt cloud-based environment and services

How long: 1.5 days

Author Carpentry – Marce

Aims:

To have an understanding of authorship in the 21st century.

Learning Outcomes:

At the end of this course a student will have

  • Created an ORCiD for themselves,
  • Understood the concept of reproducible reporting
  • Understood how to use RMarkdown for reproducible reporting Course content:
  • Introduction to ORCiD’s
  • Reproducible reporting using (for example) Rstudio How long: 3 hours

Information Security – Rob and Raphael – sponsored by RDA and CODATA

Aims:

To have an understanding of the importance of Information Security in an Open era .

Learning Outcomes:

At the end of this course a student will have

  • Understood that their online activity, and any systems they create or use for Data Science, will be subject to online attack
  • Understood security design principles that they can apply to their work
  • Understood the basics of cryptography and encryption, and their importance Course content:
  • Introduction to Computer Security
  • Practical Security and Cryptography
  • Evening session; cracking ciphers OR ethical discussion

How long: 3 hours

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