Lo que aprenderás
- How to bridge diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing
- Advanced techniques to analyze genomic data.
- How to structure, annotate, normalize, and interpret genome-scale assays.
- How to analyze data from several experimental protocols, using open-source software, including R and Bioconductor.
Advances in genomics have triggered fundamental changes in medicine and research. Genomic datasets are driving the next generation of discovery and treatment, and this series will enable you to analyze and interpret data generated by modern genomics technology.
Using open-source software, including R and Bioconductor, you will acquire skills to analyze and interpret genomic data. These courses are perfect for those who seek advanced training in high-throughput technology data. Problem sets will require coding in the R language to ensure mastery of key concepts. In the final course, you’ll investigate data analysis for several experimental protocols in genomics.
Enroll now to unlock the wealth of opportunities in modern genomics.
Cursos en este programa
Certificación Profesional en Data Analysis for Genomics de HarvardX
- 2–4 horas por semana durante 4 semanas
The structure, annotation, normalization, and interpretation of genome scale assays.
- 2–4 horas por semana durante 5 semanas
Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.
- 2–4 horas por semana durante 4 semanas
Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.
- R is listed as a required skill in 64% of data science job postings and was Glassdoor’s Best Job in America in 2016 and 2017. (source: Glassdoor)
- Companies are leveraging the power of data analysis to drive innovation. Google data analysts use R to track trends in ad pricing and illuminate patterns in search data. Pfizer created customized packages for R so scientists can manipulate their own data.
- 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. (source: Kaggle, 2017)
- Data Scientists are few in number and high in demand. (source: TechRepublic)
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