Omicslogic Data Science
The rapid growth of high-throughput data, including -omics technologies, gave rise to a significant demand for data science skills and experience with bioinformatics methods of analysis. To help introduce biologists, clinicians and students to cutting edge bioinformatics methods and commonly used data science concepts, our team designed an online bioinformatics training program called OmicsLogic. This online summer program is designed for Data science beginners students interested in data-driven research questions.
Upcoming Events
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12January
Session 1: Omics: Introduction to data types and properties
Session Topics Overview of commonly used “omics” data NGS, Mass-Spec, phenotypic data (genomics, transcriptomics, metagenomics) Phenotypes: clinical, imaging, metadata (research, clinical, biotech, pharma) The...
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14January
Session 2: Big Data Challenges and Opportunities (conceptual and computational)
Session Topics Availability and variability of data Unprecedented Detail and volume Data heterogeneity, complexity, and noise Need for structure and reproducibility
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19January
Session 3: Cleaning, loading and processing data (Logical steps and a practice)
Session Topics Analysis logic: from raw reads to a table of expression (RNA-seq example) Common sources of unwanted technical variation pre-processing steps, filtering and...
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21January
Session 4: Exploratory data analysis: data summary and effective visualization
Summary statistics (histogram, boxplot, a scatterplot of 2 samples compared to each other, Excel “summary statistics” operation) Visualization of practice data – compare the...
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26January
Session 5: Hands-on: handling large and complex data
Session Topics Learn how to make statistical representations of the data and how to address missing or data errors. How do you compare the...
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28January
Session 6 – Introduction to Machine Learning (ML) and Artificial Intelligence (AI)
Session Topics Hypothesis testing 101: compare conditions and find the p-value Data-driven discovery: discover groups or conditions Process of inference for a machine versus...
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02February
Session 7 – Unsupervised Machine Learning: dimensionality reduction and clustering
Session Topics Finding patterns in the data and methods of data mining. PCA, k-means, h-clustering (run example on T-Bio and then open the script...
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04February
Session 8 – Supervised Machine Learning: classification and feature selection
Session Topics Conceptual Introduction: Known sample data is used to train the computer to use these patterns to correlate to unknown data. Binary decision...
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09February
Session 9 – Model accuracy and validation
Session Topics Technical accuracy (ROC curve) Logical or biological relevance (compare feature selection with PCA by subtype or clinical phenotype) Trained Model validation: Learning...
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11February
Session 10 – ML in production – getting results with ML
Session Topics The interaction between artificial intelligence and human Differences between ML and AI In what ways can AI support human research and decision...
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