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

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==Video Recordings==
==Video Recordings==
* [https://attendee.gotowebinar.com/recording/5279492413447473676 Day 1 Video podcast]
* [https://attendee.gotowebinar.com/recording/5279492413447473676 Day 1 Video podcast].
* Day 1 Video podcast (coming up ...)
* [https://attendee.gotowebinar.com/recording/4162355614300329995 Day 2 Video podcast].

Revision as of 09:55, 21 June 2021

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

2021 ISI World Statistical Congress


Ivo Dinov, University of Michigan, SOCR, MIDAS.
Dr. Dinov is a professor of Health Behavior and Biological Sciences and Computational Medicine and Bioinformatics at the University of Michigan. He is a member of the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM) and a core member of the University of Michigan Comprehensive Cancer Center. Dr. Dinov serves as Director of the Statistics Online Computational Resource, Co-Director of the Center for Complexity and Self-management of Chronic Disease (CSCD Center), Co-Director of the multi-institutional Probability Distributome Project, Associate Director of the Michigan Institute for Data Science (MIDAS), and Associate Director of the Michigan Neuroscience Graduate Program (NGP). He is a member of the American Statistical Association (ASA), International Association for Statistical Education (IASE), American Mathematical Society (AMS), American Association for the Advancement of Science (AAAS), and an Elected Member of the Institutional Statistical Institute (ISI).

Session Logistics


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.


Assumed prior knowledge includes: Completed undergraduate study with quantitative STEM exposure, some quantitative training, programming experience, and high-level of energy and motivation to learn. Preinstalled R and RStudio on user local client computer.


This course is based on active-learning and integrates driving motivational challenges with mathematical foundations, computational statistics, and modern scientific inference.


The training aims to provide 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.


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


The Data Science and Predictive Analytics textbook is divided into the following 23 chapters, each progressively building on the previous content.

  1. Motivation
  2. Foundations of R
  3. Managing Data in R
  4. Data Visualization
  5. Linear Algebra & Matrix Computing
  6. Dimensionality Reduction
  7. Lazy Learning: Classification Using Nearest Neighbors
  8. Probabilistic Learning: Classification Using Naive Bayes
  9. Decision Tree Divide and Conquer Classification
  10. Forecasting Numeric Data Using Regression Models
  11. Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines
  12. Apriori Association Rules Learning
  13. k-Means Clustering
  14. Model Performance Assessment
  15. Improving Model Performance
  16. Specialized Machine Learning Topics
  17. Variable/Feature Selection
  18. Regularized Linear Modeling and Controlled Variable Selection
  19. Big Longitudinal Data Analysis
  20. Natural Language Processing/Text Mining
  21. Prediction and Internal Statistical Cross Validation
  22. Function Optimization
  23. Deep Learning, Neural Networks

Program Outline

Program Details

Wednesday, June 16, 2021, 8:00-11:00 AM US-EDT Thursday, June 17, 2021, , 8:00-11:00 AM US-EDT
Welcome Review of Day 1
DSPA Summer Course Overview (ISI/WSC, prereqs, vision, objectives, outcomes, Website) Questions, comments, issues?
Introductions (Instructor: Ivo Dinov; Attendees: please post in Chat/Discussion-Forum: Participant's Name, Affiliation, Title, interests, and one fun fact about you Supervised AI
Course Coverage Model-based
Expectations and optional capstone project (below) Baseball players physique modeling
SOCR Resources: Datasets & Case-studies, Webapps, DSPA, Spacekime/TCIU, GitHub, Prob & Stats EBook, SMHS EBook, Current SOCR Users k-NN prediction of galaxy spin
Open Science It’s online, therefore it exists! Model-free
Download DSPA Textbook (free) Estimate the square root function using NN
Resource Search & Navigation, Language Translations
NN Google Trends and the Stock Market
Motivation - and 7D of Big Data Unsupervised AI
Digitalization of all human experiences Classification and clustering (k-Means, spectral, hierarchical)
Responsible Data Science/Ethical Predictive Analytics Hot-dogs example
R vs. Python vs. SAS vs. SPSS vs. other SW Silhouette plots
Confirm local installations of R & RStudio Pediatric trauma clustering study
RStudio GUI
Rmarkdown Notebook (IDE) End-to-end Pipeline Workflow from raw data … models … visualization … analytics … reporting/pubs
Example Demo (requires knitr package)
Chapter 4 RMD Source, HTML output, SOCR_Header
Math Foundations
5-min Break 5-min Break
Data types: categorical & numeric, structured and unstructured, scalar, vector, matrix, data-frame, tensor, list, object Reticulation (interoperability between R, Python, C/C++ and other languages)
Data manipulation import/export, EM imputation, webpage scraping, sample statistics (moments) Text modeling & NLP (sentiment analysis example)
EDA (visualization)
Compare R EDA vs. HTML/JS: SOCRAT (NI data of AD/MCI/NC), Motion Charts (Housing Prices), BrainViewer (raw MRI, DTI tracks, Brain Atlas)
Probability Distributions: Distributome, TVN Webapp Longitudinal data analysis (Google trends analytics)
Dimensionality reduction
Linear PCA: 2D --> 1D example, PPMI (Parkinson's disease) example
5-min Break 5-min Break
Non-linear: MNIST data OCR: UMAP OCR, t-SNE OCR Role of optimization in AI/ML (Healthcare manufacturer product optimization example)
SOCR/Tensorboard/Projector UKBB Brain Study Deep neural networks (image-classification example)
Capstone project: interactive-learning using monthly US macro-economic data. Use the RMD source, the example HTML output, and the provided data to experiment with some of the DSPA techniques. Think of ways to augment these data (expand the time range and increase the feature richness) DSPA Appendices: Bayesian Simulation, Modeling and Inference; Information-Theoretic Foundation of Statistical Learning; Surface, Shape, and Manifold Representation and Visualization; Power Analysis in Experimental Design; Database SQL/NoSQL Queries & Google BigQuery; Image Convolution, Filtering, & Fourier Transform; Causality, Transfer Entropy, & Mechanistic Effects; Agent-based Reinforcement Learning
Demonstrations of interesting Capstone project results
Open discussion Open discussion


Video Recordings


Partial list of participants:

  • Jennifer Daniels: Adjunct Math and Statistics Instructor at Mid Michigan College, Davenport University, and Alma College. Graduate student at Central Michigan University.
  • Jo Edwards: Australian Bureau of Statistics, Project Manager/Data Scientist.
  • Jannik Schaller: Federal Statistical Office of Germany (DESTATIS), Interest: Data Fusion/ Statistical and Machine Learning.
  • Edviges Coelho: Statistics Portugal and Universidade Lusófona.
  • Kadri Rootalu: Data scientist in Statistics Estonia, but have an education in Sociology
  • Jared Mendoza: University of the Philippines Los Banos, Assistant Professor of Statistics
  • Lynda Aouar: UNCO, PhD student in Applied Statistics, I am interested about nonparametric statistics
  • Ananda Manage: Dept of Math & Stat, Sam Houston State University, Texas, USA
  • Michal Ciszewski, PhD student in Statistics at TU Delft, interests: activity recognition and anomaly detection
  • Joyce Chang; Data scientist at the U of Pittsburgh School of Medicine; interested in risk prediction modeling and identify heterogeneous treatment effects
  • Katherine Zavez: PhD student in the Department of Statistics at the University of Connecticut
  • Ewilly Liew: Lecturer in Econometrics and Business Statistics, Monash University Malaysia. Interest: behavioral research in higher education and healthcare.
  • Jennifer Daniels: Interested in Applied Statistics. Particularly, Data/Text Mining. I have lived and taught in Japan many years ago.
  • Elizabeth Gonzalez: Statistics Department, Colegio de Postgraduados, Mexico, interested in statistical inference in general.
  • Delia Ortega: PhD student in Statistics. Universidad Nacional, Colombia.
  • Li Zhou: PhD student in stat at Auburn University
  • Ilich Lama: Principal Research Scientist - Environmental Data Science (NCASI), Montreal, Canada - Interested among other things in statistical analysis of industrial emissions/releases.
  • Brocha Stern, postdoctoral fellow at Northwestern University, orthopedic health services and outcomes research
  • Annette Kifley, biostatistician in rehabilitation studies, University of Sydney
  • Jo Edwards: I am interested in Coding and Classification techniques as well as Entity extraction
  • Nur Aziha Mansor: Statistician in Department of Statistics Malaysia, Interest in data management
  • Martina Ozoglu: Statistical Office of the Slovak Republic, tourism analyst. I am interested in new forms of Tourism and its data interpretation.
  • Jason Ng, Monash University, Dept of Econometrics and Business Statistics
  • Quratulain Khaliq: PhD Statistics candidate from Pakistan
  • Malcolm Cai: Working in the public service of Singapore. Keen on data science, and sports.
  • Nurhazwani Abdul Halim, an Executive from Data Management and Statistics Department, from Central Bank of Malaysia. I am interested in Data Science and Machine Learning
  • Zsófia Szente: Hungarian Central Statistical Office, statistician. I am interested in data visualization and data science.
  • Luigi Arzedi, PhD student in Statistics at University of Cagliari (Italy)
  • Miguel David Alvarez, PhD student in Economics and I work as a Data Scientist in the National Electoral Institute (Mexico).
  • Felibel Zabala: methodologist from Stats NZ. I am interested in data science & machine learning in official statistics
  • Quratulain Khaliq: PhD Candidate, Allama Iqbal open University, Statistical process Control, Robustness technique, non parametric statistics. I am interested to link SPC techniques to data science.

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