# Difference between revisions of "SOCR News ISI WSC DSPA Training 2021"

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− | ! Areas !! Competency !! Expectation | + | ! Areas !! Competency !! Expectation !! Notes |

|- | |- | ||

− | | Algorithms and Applications || Tools || Working knowledge of basic software tools (command-line, GUI based, or web-services) || Familiarity with statistical programming languages, e.g., R or SciKit/Python, and database querying languages, e.g., SQL or NoSQL | + | | rowspan="3"|Algorithms and Applications || Tools || Working knowledge of basic software tools (command-line, GUI based, or web-services) || Familiarity with statistical programming languages, e.g., R or SciKit/Python, and database querying languages, e.g., SQL or NoSQL |

|- | |- | ||

− | + | | Algorithms || Knowledge of core principles of scientific computing, applications programming, API’s, algorithm complexity, and data structures || Best practices for scientific and application programming, efficient implementation of matrix linear algebra and graphics, elementary notions of computational complexity, user-friendly interfaces, string matching | |

|- | |- | ||

− | | | + | | Application Domain || Data analysis experience from at least one application area, either through coursework, internship, research project, etc. || Applied domain examples include: computational social sciences, health sciences, business and marketing, learning sciences, transportation sciences, engineering and physical sciences |

|- | |- | ||

− | | Data Management || Data validation & visualization || Curation, Exploratory Data Analysis (EDA) and visualization || Data provenance, validation, visualization via histograms, Q-Q plots, scatterplots (ggplot, Dashboard, D3.js) | + | | rowspan="3"|Data Management || Data validation & visualization || Curation, Exploratory Data Analysis (EDA) and visualization || Data provenance, validation, visualization via histograms, Q-Q plots, scatterplots (ggplot, Dashboard, D3.js) |

|- | |- | ||

− | + | | Data wrangling || Skills for data normalization, data cleaning, data aggregation, and data harmonization/registration || Data imperfections include missing values, inconsistent string formatting (‘2016-01-01’ vs. ‘01/01/2016’, PC/Mac/Linux time vs. timestamps, structured vs. unstructured data | |

|- | |- | ||

− | | | + | | Data infrastructure || Handling databases, web-services, Hadoop, multi-source data || Data structures, SOAP protocols, ontologies, XML, JSON, streaming |

|- | |- | ||

− | | | + | | rowspan="3"|Analysis Methods || Statistical inference || Basic understanding of bias and variance, principles of (non)parametric statistical inference, and (linear) modeling || Biological variability vs. technological noise, parametric (likelihood) vs non-parametric (rank order statistics) procedures, point vs. interval estimation, hypothesis testing, regression |

|- | |- | ||

− | | | + | | Study design and diagnostics || Design of experiments, power calculations and sample sizing, strength of evidence, p-values, False Discovery Rates || Multistage testing, variance normalizing transforms, histogram equalization, goodness-of-fit tests, model overfitting, model reduction |

|- | |- | ||

− | + | | Machine Learning || Dimensionality reduction, k-nearest neighbors, random forests, AdaBoost, kernelization, SVM, ensemble methods, CNN || Empirical risk minimization. Supervised, semi-supervised, and unsupervised learning. Transfer learning, active learning, reinforcement learning, multiview learning, instance learning | |

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## Revision as of 13:06, 11 March 2021

## Contents

## SOCR News & Events: 2021 ISI/WSC Training and Education Bootcamp on Data Science and Predictive Analytics (DSPA)

## Overview

....

## Organizer

## Session Logistics

**Date/Time**: Wednesday & Thursday, June 16-17, 2021, 14.00-17.00, Central European Summer Time, CEST (UTC+2)**Registration**: TBD.**URL**: TBD.**Conference**: 2021 ISI World Statistical Congress.**Session Format**: Daily 3-hour sessions.- Session URL: https://myumi.ch/erXm2.

## Overview

This course will be based on a Data Science and Predictive Analytics (DSPA) course I teach at the University of Michigan. The training will provide intermediate to advanced learners with a solid data science foundation to address challenges related to collecting, managing, processing, interrogating, analyzing and interpreting complex health and biomedical datasets using R. Participants will gain skills and acquire a tool-chest of methods, software tools, and protocols that can be applied to a broad spectrum of Big Data problems.

Before diving into the mathematical algorithms, statistical computing methods, software tools, and health analytics, we will discuss a number of driving motivational problems. These will ground all the subsequent scientific discussions, data modeling, and computational approaches.

### Vision

Enable active-learning by integrating driving motivational challenges with mathematical foundations, computational statistics, and modern scientific inference

### Values

Effective, reliable, reproducible, and transformative data-driven discovery supporting open-science

### Strategic priorities

Trainees will develop scientific intuition, computational skills, and data-wrangling abilities to tackle Big biomedical and health data problems. Instructors will provide well-documented R-scripts and software recipes implementing atomic data-filters as well as complex end-to-end predictive big data analytics solutions.

### Outcomes

Upon successful completion of this course, participants are expected to have moderate competency in at least two of each of the three competency areas: Algorithms and Applications, Data Management, and Analysis Methods. Specifically, participants will get end-to-end R-protocols, gain ML/AI algorithm knowledge, explore data validation, wrangling, and visualization, experiment with statistical inference and model-free Machine Learning tools.

Areas | Competency | Expectation | Notes |
---|---|---|---|

Algorithms and Applications | Tools | Working knowledge of basic software tools (command-line, GUI based, or web-services) | Familiarity with statistical programming languages, e.g., R or SciKit/Python, and database querying languages, e.g., SQL or NoSQL |

Algorithms | Knowledge of core principles of scientific computing, applications programming, API’s, algorithm complexity, and data structures | Best practices for scientific and application programming, efficient implementation of matrix linear algebra and graphics, elementary notions of computational complexity, user-friendly interfaces, string matching | |

Application Domain | Data analysis experience from at least one application area, either through coursework, internship, research project, etc. | Applied domain examples include: computational social sciences, health sciences, business and marketing, learning sciences, transportation sciences, engineering and physical sciences | |

Data Management | Data validation & visualization | Curation, Exploratory Data Analysis (EDA) and visualization | Data provenance, validation, visualization via histograms, Q-Q plots, scatterplots (ggplot, Dashboard, D3.js) |

Data wrangling | Skills for data normalization, data cleaning, data aggregation, and data harmonization/registration | Data imperfections include missing values, inconsistent string formatting (‘2016-01-01’ vs. ‘01/01/2016’, PC/Mac/Linux time vs. timestamps, structured vs. unstructured data | |

Data infrastructure | Handling databases, web-services, Hadoop, multi-source data | Data structures, SOAP protocols, ontologies, XML, JSON, streaming | |

Analysis Methods | Statistical inference | Basic understanding of bias and variance, principles of (non)parametric statistical inference, and (linear) modeling | Biological variability vs. technological noise, parametric (likelihood) vs non-parametric (rank order statistics) procedures, point vs. interval estimation, hypothesis testing, regression |

Study design and diagnostics | Design of experiments, power calculations and sample sizing, strength of evidence, p-values, False Discovery Rates | Multistage testing, variance normalizing transforms, histogram equalization, goodness-of-fit tests, model overfitting, model reduction | |

Machine Learning | Dimensionality reduction, k-nearest neighbors, random forests, AdaBoost, kernelization, SVM, ensemble methods, CNN | Empirical risk minimization. Supervised, semi-supervised, and unsupervised learning. Transfer learning, active learning, reinforcement learning, multiview learning, instance learning |

## Program

...

## Resources

...

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