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Part I |
Data Science |
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Sept 7 |
L0 |
Introduction and orientation |
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Sept 9 |
L1 |
Information technology for managing science |
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Sept 9 |
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Assignment 1 start |
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Sept 14 |
L2 |
Modern computing paradigms and RStudio Cloud |
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Sept 16 |
L3 |
Visualizing breast cancer transcriptomes |
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Sept 21 |
L4 |
Representing & manipulating data |
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Sept 23 |
L5 |
Transformations of breast cancer transcriptomes |
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Sept 27 |
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Assignment 1 due |
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Sept 28 |
L6 |
Exploratory data analysis of breast cancer transcriptomes |
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Sept 28 |
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Assignment 2 start |
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Part II |
Bioinformatics |
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Sept 30 |
L7 |
The NCBI: a rich resource for the life sciences |
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Oct 5 |
L8 |
How to wrangle with marine microbes |
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Oct 7 |
L9 |
Organzing the Tara Ocean’s data |
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Oct 12 |
L10 |
Exploring microbial diversity across our oceans |
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Oct 14 |
L11 |
Representing genomes in R |
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Oct 19 |
L12 |
Genome annotations |
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Part III |
Computational Biology |
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Oct 21 |
L13 |
Transcription factor binding sites in Baker’s yeast |
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Oct 22 |
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Assignment 2 due |
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Oct 26 |
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Midterm (1.15 hr, open book) |
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Oct 28 |
L14 |
Gene finding with Hidden Markov Models |
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Oct 28 |
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Assignment 3 start |
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Nov 2 |
L15 |
Gene finding continued |
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Nov 4 |
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Project outline due for genomics diploma and grad students (1 page) |
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Nov 4 |
L16 |
Models of sequence evolution |
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Nov 9 |
L17 |
Phylogenies and alignments |
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Nov 11 |
L18 |
Basics of machine learning |
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Nov 13 |
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Assignment 3 due |
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Nov 15 |
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Assignment 4 start |
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Nov 16 |
L19 |
Discovery breast cancer subtypes: unsupervised analysis |
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Nov 18 |
L20 |
Transcriptomics: a deep dive |
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Nov 23 |
L21 |
Predicting breast cancer patient outcome from expression data: supervised analysis |
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Nov 25 |
L22 |
Deep learning CNNs: proliferative index of a cancer cell with microscopy |
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Nov 30 |
L23 |
Computational challenges of single cell profiling I |
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Part IV |
Reproducible Science and Communication |
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Dec 2 |
L24 |
Reproducibility in life science research |
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Dec 2 |
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Assignment 4 due |
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Dec 10 |
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Project due |
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Dec 15, 2pm-4pm |
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Final (HB 130 (our normal classroom), open book) |
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