SOCR News MIDAS Biomedical Bootcamp 2021

SOCR News & Events: 2021 MIDAS Data Science for Biomedical Scientists Bootcamp

The Michigan Institute for Data Science (MIDAS) is organizing a week-long Data Science for Biomedical Scientists Bootcamp. This workshop will introduce data science from a biomedical perspective. Bootcamp participants will learn about practical data science applications in biomedical and health case-studies. Modern data science, machine learning, artificial intelligence, and biostatistical methods will be integrated into the training curriculum.

Instructors

Kayvan Najarian

Nambi Nallasamy

Ivo Dinov, University of Michigan, SOCR, MIDAS.

Michael Mathis

Ryan Stidham

Jonathan Gryak

Michael Sjoding

Workshop Logistics

• Dates/Times: Monday through Friday, July 26-20, 2021, 7:00-16:00 US-EDT (daily).
• Registration: Registration Link.
• URL: MIDAS Bootcamp Website.
• Session Format: Two daily sessions (3-hours each).
• Session URL.

Overview

Target Audience: This workshop is open to all biomedical scientists. The curriculum is geared towards junior faculty members who plan to incorporate data science in their scholarly work.

Prerequisite: College level math and statistics.

Main components:

Math and algorithmic foundations for data science

Key concepts of data science

Introduction to Python programming

Machine learning, support vector machine, artificial neural network, deep learning

Example of biomedical research projects with data science

Incorporating data science in biomedical grant proposals

Program Schedule

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

Additional Resources

• Course Flyer
• DSPA Wikipedia.
• DSPA Springer Page & SpringerLink (PDF Download).
• dspa.predictive.space & DSPA MOOC Canvas Site.

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