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Professional Certificate in
Data Analysis for Genomics
HarvardX

What you will learn

  • 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.

Expert instruction
3 skill-building courses
Self-paced
Progress at your own speed
4 months
2 - 4 hours per week
$347
USD
For the full program experience

Courses in this program

  1. HarvardX's Data Analysis for Genomics Professional Certificate

  2. 2–4 hours per week, for 5 weeks

    The structure, annotation, normalization, and interpretation of genome scale assays.

  3. 2–4 hours per week, for 5 weeks

    Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

  4. 2–4 hours per week, for 5 weeks

    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)

Meet your instructors

from Harvard University (HarvardX)
Peter Kraft
Professor of Epidemiology and Biostatistics
Harvard T.H. Chan School of Public Health
Vincent Carey
Professor, Medicine
Harvard Medical School
X. Shirley Liu
Professor of Computational Biology and Biostatistics
Harvard T.H. Chan School of Public Health
Rafael Irizarry
Professor of Biostatistics
Harvard University
Michael Love
Assistant Professor, Departments of Biostatistics and Genetics
UNC Gillings School of Global Public Health

Experts from HarvardX committed to teaching online learning

Enrolling Now

$347
USD
3 courses in 4 months
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