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== [[SOCR_News | SOCR News & Events]]: 2022 ISI Short Course - Data Science and Predictive Analytics (DSPA) == | == [[SOCR_News | SOCR News & Events]]: 2022 ISI Short Course - Data Science and Predictive Analytics (DSPA) == | ||
− | [[Image: | + | [[Image:DSPA_ISI_2022_anime.gif|right| [https://www.isi-web.org/events/courses/ 2022 ISI Short Courses] ]] |
== Instructor == | == Instructor == |
Revision as of 16:21, 20 December 2021
Contents
SOCR News & Events: 2022 ISI Short Course - Data Science and Predictive Analytics (DSPA)
Instructor
- 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
- Date/Time: TBD (March/April 2022); Time TBD Central European Summer Time, CEST (UTC+2); US-EDT.
- Registration: Registration Link, moderate registration fees apply.
- GoToMeeting: TBD.
- URL: TBD Official ISI Course Website.
- Session Format: Two daily sessions (3-hours each).
- Session URL.
Overview
This course will be based on a Data Science and Predictive Analytics (DSPA) course the instructor teaches 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.
Prerequisites
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.
Vision
This course is based on active-learning and integrates driving motivational challenges with mathematical foundations, computational statistics, and modern scientific inference.
Values
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.
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 |
Topics
The Data Science and Predictive Analytics textbook is divided into the following 23 chapters, each progressively building on the previous content.
- Motivation
- Foundations of R
- Managing Data in R
- Data Visualization
- Linear Algebra & Matrix Computing
- Dimensionality Reduction
- Lazy Learning: Classification Using Nearest Neighbors
- Probabilistic Learning: Classification Using Naive Bayes
- Decision Tree Divide and Conquer Classification
- Forecasting Numeric Data Using Regression Models
- Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines
- Apriori Association Rules Learning
- k-Means Clustering
- Model Performance Assessment
- Improving Model Performance
- Specialized Machine Learning Topics
- Variable/Feature Selection
- Regularized Linear Modeling and Controlled Variable Selection
- Big Longitudinal Data Analysis
- Natural Language Processing/Text Mining
- Prediction and Internal Statistical Cross Validation
- Function Optimization
- Deep Learning, Neural Networks
Program Outline
- Welcome and introductions
- Course logistics (please come prepared with access to Internet connected computers having local versions of R (statistical computing environment) and RStudio (graphical user interface and integrated development environment)
- Data manipulation and visualization
- Non-linear dimensionality reduction (UMAP & t-SNE)
- Supervised and Unsupervised, model-based and model-free prediction, regression, classification, and clustering
- Reticulation (Interoperability between R, Python, C/C++ and other languages)
- Role of optimization in AI/ML
- Activities and HTML5 demos.
Program Details
Day 1 (TBD) | Day 2 (TBD) |
---|---|
Welcome | Review of Day 1 |
DSPA Summer Course Overview (ISI, 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) |
Rresponsible 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 |
Resources
- Course Flyer.
- 1-page Course Coverage with dynamic links to content.
- DSPA Wikipedia.
- DSPA Springer Page & SpringerLink (PDF Download).
- dspa.predictive.space & DSPA MOOC Canvas Site.
- (past) 2021 ISI/WSC DSPA Course.
Video Recordings
- to be posted later ...
Participants
Partial list of participants:
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