Genomic Data Science Specialization - Johns Hopkins University (Coursera)

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No.	Completed	Course
I	Completed	Introduction to Genomic Technologies
II	Completed	Python for Genomic Data Science
III	Completed	Algorithms for DNA Sequencing
IV	Completed	Command Line Tools for Genomic Data Science
V	Completed	Bioconductor for Genomic Data Science
VI	Completed	Statistics for Genomic Data Science

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I. Introduction to Genomic Technologies

• Biology overview
• Measurement technology (NGS, sequencing platforms, microarrays)
• Computing technology
• Data science technology (genomic data formats: FASTA, FASTQ, SAM/BAM)

II. Python for Genomic Data Science

• Strings and numbers
• Data structures, ifs, and loops
• Functions and modules
• Reading data (FASTA, FASTQ parsing)
• Biopython

III. Algorithms for DNA Sequencing

• DNA sequencing
• Exact and approximate matching
• Edit distance and alignment (sequence alignment algorithms, Bowtie2 concepts)
• Read assembly (de Bruijn graphs, overlap graphs)

IV. Command Line Tools for Genomic Data Science

• Command line basics (Linux/Unix)
• Working with files and directories
• Text processing tools (grep, awk, sed)
• Managing and analyzing genomic data (SAM/BAM, SAMtools, Bowtie2, STAR, BEDTools)

V. Bioconductor for Genomic Data Science

• R programming
• Bioconductor packages (GenomicRanges, Biostrings, DESeq2, edgeR)
• Genomic data analysis
• Working with biological data

VI. Statistics for Genomic Data Science

• Statistical methods (hypothesis testing, multiple testing correction, FDR)
• Exploratory data analysis (PCA, clustering)
• Statistical modeling (regression models)
• Hypothesis testing
• Regression analysis