SOCR News MIDAS Biomedical Bootcamp 2021

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

2021 MIDAS Biomedical 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.


Kayvan Najarian
Nambi Nallasamy
Ivo Dinov, University of Michigan, SOCR, MIDAS.
Michael Mathis
Ryan Stidham
Jonathan Gryak
Michael Sjoding

Workshop Logistics


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

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