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Statistics for Genomic Data Science
Jeff Leek, PhD
Jeff Leek, PhD
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Register for this course and see more details by visiting:
OpenCourser.com/course/vtxp5p/statistics
What's inside
Syllabus
Module 1
This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.
Read more
Syllabus
Module 1
This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.
Read more
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
Module 4
In this week we will cover a lot of the general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces foundational concepts and methods in genomic data science, catering to the growing interest in the field
Led by Dr. Jeff Leek, a respected professor in the field of biostatistics and genomics
Part of the Genomic Big Data Science Specialization from Johns Hopkins University, a renowned institution in genomics research
Emphasizes practical applications, covering pipelines commonly used in analyzing various types of genomic data
Assumes familiarity with basic statistical concepts, making it suitable for learners with some prior knowledge
May require additional resources for learners without significant background in genomics or statistical analysis
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Reviews summary
Valuable genomic data statistics
Learners say Statistics for Genomic Data Science is an engaging course that covers a comprehensive set of topics relevant to genomic data analysis. The well-organized materials and engaging professor make the complex subject matter easy to understand. While the course is a bit short, learners appreciate the additional resources and recommended readings provided. They also value the practical applications and real-world examples used throughout the course.
Engaging and enthusiastic instructor.
"The instructor is one of the best whose teaching and course design appealed to me most effectively..."
"I really appreciate the enthusiasm of the instructor."
Provides valuable and practical materials.
"Useful!"
"Challenging but worth it"
"Great course as a starting point for statistical genomics!"
Some concepts are not explained in sufficient detail.
"His lecture is too fast, lack of detail, unclear explaination, redundant and unorganized handout."
Requires some proficiency in R programming.
"This course covers some statistical techniques in genomics using R and Bioconductor packages."
"It has most of the same problems as the previous courses in this specialization in that the work is at a level for which the student really needs some significant background in the technical aspects in order to complete the course."
Career center
Learners who complete Statistics for Genomic Data Science will develop knowledge and skills
that may be useful to these careers:
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to design studies, analyze data, and interpret results in the field of biology. This course provides a solid foundation in statistical concepts and techniques commonly used in genomic data analysis, which is a rapidly growing field within biology. By taking this course, you will gain the skills necessary to design and conduct your own genomic studies, analyze data, and draw meaningful conclusions from your findings. This course can help you launch a successful career in biostatistics or advance your existing career in the field.
Data Scientist
Data Scientist
A Data Scientist uses data to extract insights and solve problems. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to preprocess data, build models, and test hypotheses. These skills are essential for any Data Scientist who wants to work with genomic data.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to preprocess data, build models, and evaluate their performance. These skills are essential for any Machine Learning Engineer who wants to work with genomic data.
Research Scientist
Research Scientist
A Research Scientist conducts research in a variety of fields, including biology, chemistry, and physics. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills are essential for any Research Scientist who wants to conduct research in genomics.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to preprocess data, build models, and evaluate their performance. These skills are essential for any Software Engineer who wants to work on software systems that use genomic data.
Statistician
Statistician
A Statistician applies statistical methods to collect, analyze, and interpret data. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills are essential for any Statistician who wants to work with genomic data.
Bioinformatician
Bioinformatician
A Bioinformatics Analyst uses computational tools and techniques to analyze biological data. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to preprocess data, build models, and evaluate their performance. These skills are essential for any Bioinformatics Analyst who wants to work with genomic data.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of health and disease in populations. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills are essential for any Epidemiologist who wants to conduct research in genomics.
Clinical Research Associate
Clinical Research Associate
A Clinical Research Associate manages clinical trials and ensures that they are conducted in accordance with ethical and regulatory guidelines. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills are essential for any Clinical Research Associate who wants to work on clinical trials involving genomic data.
Healthcare Data Analyst
Healthcare Data Analyst
A Healthcare Data Analyst uses data to improve the quality, efficiency, and cost-effectiveness of healthcare delivery. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to preprocess data, build models, and evaluate their performance. These skills are essential for any Healthcare Data Analyst who wants to work with genomic data.
Quality Assurance Analyst
Quality Assurance Analyst
A Quality Assurance Analyst ensures that products and services meet quality standards. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills are essential for any Quality Assurance Analyst who wants to work with genomic data.
Regulatory Affairs Specialist
Regulatory Affairs Specialist
A Regulatory Affairs Specialist ensures that products and services comply with government regulations. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills are essential for any Regulatory Affairs Specialist who wants to work with genomic data.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation. This course can help you build the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to write clear and concise documentation that explains complex technical concepts. This skill is essential for any Technical Writer who wants to work on documentation for genomic data analysis software or tools.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. This course may be useful for building the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills may be helpful for managing projects that involve genomic data analysis.
Business Analyst
Business Analyst
A Business Analyst identifies and analyzes business needs and develops solutions to meet those needs. This course may be useful for building the skills necessary to succeed in this role by providing you with a strong foundation in statistical concepts and techniques used in genomic data analysis. You will learn how to design and conduct studies, analyze data, and draw meaningful conclusions from your findings. These skills may be helpful for analyzing business needs and developing solutions that involve genomic data.
For more career information including salaries, visit:
OpenCourser.com/course/vtxp5p/statistics
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Statistics for Genomic Data Science.
This highly influential book provides a comprehensive overview of statistical learning methods, including linear and nonlinear regression, classification, and unsupervised learning, offering a more advanced perspective on the statistical techniques used in genomic data science.
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Covers a wide range of biostatistical methods commonly used in the analysis of biomedical data, including linear and nonlinear regression, analysis of variance, and survival analysis, providing a solid foundation for understanding and applying these methods in genomic data science.
Foundational text in biostatistics, covering the fundamental concepts and methods used in the analysis of biological data. It provides a comprehensive overview of topics such as probability, statistical inference, and regression analysis, which are essential knowledge for students and practitioners in genomic data science.
Focuses on the application of statistical methods to bioinformatics, covering topics such as sequence analysis, gene expression analysis, and population genetics. It provides practical guidance on the use of statistical software and tools, making it a valuable resource for researchers in the field.
This classic textbook provides a comprehensive overview of bioinformatics, covering topics such as sequence analysis, gene expression analysis, and phylogenetics, offering a solid foundation for understanding the computational principles underlying genomic data science.
Focuses on practical data skills and techniques essential for genomic data scientists, covering data management, analysis, and visualization.
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, classification, and clustering. It widely-used textbook in machine learning and data science, and provides a solid foundation for understanding the statistical methods used in genomic data science.
Provides a clear and concise introduction to linear regression, covering topics such as model fitting, variable selection, and diagnostics, offering a good foundation for understanding the linear models used in genomic data science.
Save
Provides a comprehensive guide to next-generation sequencing data analysis, covering topics such as quality control, alignment, variant calling, and data interpretation, offering practical advice for working with genomic data in a real-world setting.
Provides a practical guide to computational genomics using R, covering topics such as data preprocessing, genome alignment, and variant calling, offering hands-on experience with the computational techniques used in genomic data science.
Introduces Bayesian statistical methods and their applications in the social sciences, providing a framework for analyzing genomic data with complex uncertainties.
Provides a detailed overview of microarray technology and its applications in genomic research, covering topics such as experimental design, data analysis, and quality control, offering a good understanding of this important technology in genomic data science.
Save
Provides a practical guide to exploratory data analysis using R, covering topics such as data visualization, data transformation, and statistical tests, offering hands-on experience with the techniques used in genomic data science.
Save
Provides a comprehensive overview of data visualization techniques, covering topics such as choosing the right chart type, presenting data effectively, and avoiding common pitfalls, offering guidance on creating effective visualizations for genomic data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vtxp5p/statistics
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Johns Hopkins University
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Jeff Leek, PhD
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Good to know
Introduces foundational concepts and methods in genomic data science, catering to the growing interest in the field
Led by Dr. Jeff Leek, a respected professor in the field of biostatistics and genomics
Part of the Genomic Big Data Science Specialization from Johns Hopkins University, a renowned institution in genomics research
Emphasizes practical applications, covering pipelines commonly used in analyzing various types of genomic data
Assumes familiarity with basic statistical concepts, making it suitable for learners with some prior knowledge
May require additional resources for learners without significant background in genomics or statistical analysis
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