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Rafael Irizarry
and
Michael Love
If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principle component analysis. We will learn about the batch effect, the most challenging data analytical problem in genomics today, and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.
Read more
If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principle component analysis. We will learn about the batch effect, the most challenging data analytical problem in genomics today, and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.
Read more
If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principle component analysis. We will learn about the batch effect, the most challenging data analytical problem in genomics today, and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.
Finally, we give a brief introduction to machine learning and apply it to high-throughput, large-scale data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates and cross-validation.
Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
These courses make up two Professional Certificates and are self-paced:
Data Analysis for Life Sciences:
- PH525.1x: Statistics and R for the Life Sciences
- PH525.2x: Introduction to Linear Models and Matrix Algebra
- PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
- PH525.4x: High-Dimensional Data Analysis
Genomics Data Analysis:
- PH525.5x: Introduction to Bioconductor
- PH525.6x: Case Studies in Functional Genomics
- PH525.7x: Advanced Bioconductor
This class was supported in part by NIH grant R25GM114818.
Register for this course and see more details by visiting:
OpenCourser.com/course/w6qmt0/high
What's inside
Learning objectives
- Mathematical distance
- Dimension reduction
- Singular value decomposition and principal component analysis
- Multiple dimensional scaling plots
- Factor analysis
- Dealing with batch effects
- Clustering
- Heatmaps
- Basic machine learning concepts
- Mathematical distance
- Dimension reduction
- Singular value decomposition and principal component analysis
- Multiple dimensional scaling plots
- Factor analysis
- Dealing with batch effects
- Clustering
- Heatmaps
- Basic machine learning concepts
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces data analysis and interpretation concepts, catering to students interested in these fields
Starts off with mathematical foundations (distance and dimension reduction), providing a solid theoretical understanding
Covers both practical applications (e.g., dimensionality reduction, visualization) and theoretical foundations (e.g., singular value decomposition, principal component analysis)
Tackles a common challenge in genomics (batch effect) and provides techniques to detect and adjust for it
Provides a brief overview of machine learning concepts and their applications in high-throughput data analysis
Consists of seven parts designed to accommodate diverse learner backgrounds, allowing for tailored learning
Part of two professional certificates, indicating industry relevance and potential career advancement opportunities
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Reviews summary
Okay class for high-dimensional analysis
According to students, this high-dimensional data analysis course is okay. The course has a number of assignments and students have commented on the class being engaging. However, the deadlines are difficult, so be sure to plan ahead if you enroll in this course.
Engaging assignments in this course
Difficult deadlines in this course
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in High-Dimensional Data Analysis with these
activities:
Read 'Data Science from Scratch' by Joel Grus
Show steps
This book provides a comprehensive introduction to data science concepts and techniques, and will give you a solid foundation for the topics covered in this course.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
-
Read the first three chapters of the book, which cover the basics of data science, including data types, data structures, and data analysis.
-
Complete the exercises at the end of each chapter to test your understanding of the material.
Solve practice problems on linear algebra and matrix operations
Show steps
Linear algebra is a fundamental tool in data science for manipulating and analyzing data. These practice problems will sharpen your skills in this important area.
Browse courses on
Linear Algebra
Show steps
-
Find the determinant of a matrix.
-
Invert a matrix.
-
Solve systems of linear equations.
Follow tutorials on using scikit-learn for machine learning tasks
Show steps
Scikit-learn is a powerful machine learning library for Python. These tutorials will help you get started with using scikit-learn for common machine learning tasks, such as classification and regression.
Browse courses on
scikit-learn
Show steps
-
Follow the scikit-learn tutorial on supervised learning.
-
Complete the exercises in the tutorial to practice using scikit-learn for classification and regression.
Three other activities
Expand to see all activities and additional details
Show all six activities
Volunteer with a data science organization or project
Show steps
Volunteering with a data science organization or project is a great way to gain practical experience and to make connections with other data scientists.
Show steps
-
Find a data science organization or project that you are interested in.
-
Contact the organization or project and inquire about volunteer opportunities.
-
Volunteer your time and skills to help the organization or project achieve its goals.
Create a blog post or video tutorial on a data science topic
Show steps
Creating content on a data science topic will help you to solidify your understanding of the material and to share your knowledge with others.
Show steps
-
Choose a data science topic that you are interested in and that you have a good understanding of.
-
Research the topic and gather information from reliable sources.
-
Create a blog post or video tutorial that explains the topic in a clear and concise way.
Participate in a data science competition or hackathon
Show steps
Participating in a data science competition or hackathon is a great way to test your skills and to learn from others.
Show steps
-
Find a data science competition or hackathon that is relevant to your interests and skill level.
-
Form a team or work independently on the competition.
-
Use your data science skills to solve the problem and submit your solution.
Read 'Data Science from Scratch' by Joel Grus
Show steps
This book provides a comprehensive introduction to data science concepts and techniques, and will give you a solid foundation for the topics covered in this course.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book, which cover the basics of data science, including data types, data structures, and data analysis.
- Complete the exercises at the end of each chapter to test your understanding of the material.
Solve practice problems on linear algebra and matrix operations
Show steps
Linear algebra is a fundamental tool in data science for manipulating and analyzing data. These practice problems will sharpen your skills in this important area.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Find the determinant of a matrix.
- Invert a matrix.
- Solve systems of linear equations.
Follow tutorials on using scikit-learn for machine learning tasks
Show steps
Scikit-learn is a powerful machine learning library for Python. These tutorials will help you get started with using scikit-learn for common machine learning tasks, such as classification and regression.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Follow the scikit-learn tutorial on supervised learning.
- Complete the exercises in the tutorial to practice using scikit-learn for classification and regression.
Three other activities
Expand to see all activities and additional details
Show all six activities
Volunteer with a data science organization or project
Show steps
Volunteering with a data science organization or project is a great way to gain practical experience and to make connections with other data scientists.
Show steps
Show steps
Show steps
- Find a data science organization or project that you are interested in.
- Contact the organization or project and inquire about volunteer opportunities.
- Volunteer your time and skills to help the organization or project achieve its goals.
Create a blog post or video tutorial on a data science topic
Show steps
Creating content on a data science topic will help you to solidify your understanding of the material and to share your knowledge with others.
Show steps
Show steps
Show steps
- Choose a data science topic that you are interested in and that you have a good understanding of.
- Research the topic and gather information from reliable sources.
- Create a blog post or video tutorial that explains the topic in a clear and concise way.
Participate in a data science competition or hackathon
Show steps
Participating in a data science competition or hackathon is a great way to test your skills and to learn from others.
Show steps
Show steps
Show steps
- Find a data science competition or hackathon that is relevant to your interests and skill level.
- Form a team or work independently on the competition.
- Use your data science skills to solve the problem and submit your solution.
Career center
Learners who complete High-Dimensional Data Analysis will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist analyzes and interprets data to extract meaningful insights and patterns using statistical and machine learning techniques. This course is highly relevant to this role as it covers topics such as dimension reduction, principal component analysis, and machine learning concepts, providing a valuable foundation for success in data science.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and develops machine learning models to solve business problems and automate tasks using data. This course provides a solid foundation in machine learning concepts and techniques, including dimension reduction, principal component analysis, and clustering, which are essential for building and deploying effective machine learning models.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to improve decision-making and optimize systems in various industries. This course provides a solid foundation in statistical and machine learning concepts, including dimension reduction, principal component analysis, and clustering, which are essential for building and evaluating optimization models.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to provide insights and make predictions. This course provides a strong foundation in statistical concepts and techniques, including mathematical distance, dimension reduction, principal component analysis, and factor analysis, which are essential for success in statistical analysis and modeling.
Data Visualization Engineer
Data Visualization Engineer
A Data Visualization Engineer designs and develops data visualizations to communicate insights effectively. This course provides a strong foundation in data visualization techniques, including dimension reduction, principal component analysis, and clustering, which are essential for creating clear and informative data visualizations.
Data Analyst
Data Analyst
A Data Analyst analyzes and interprets data to extract meaningful insights and patterns. This course provides a strong foundation in data analysis techniques, including dimension reduction, principal component analysis, and clustering, which are essential for effective data analysis and visualization.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to assess risk and make investment decisions in the financial industry. This course provides a solid foundation in statistical and machine learning concepts, including dimension reduction, principal component analysis, and clustering, which are essential for building and evaluating financial models.
Bioinformatics Analyst
Bioinformatics Analyst
A Bioinformatics Analyst analyzes large biological datasets using computational tools and techniques to identify patterns and insights. This course is highly relevant to this role as it covers dimension reduction techniques, principal component analysis, and machine learning concepts, which are essential for analyzing and interpreting biological data.
Research Scientist
Research Scientist
A Research Scientist conducts scientific research to expand knowledge in a particular field of study. This course can be useful in providing a foundation in statistical and machine learning techniques that are commonly used in research, helping you develop the skills necessary to design and conduct scientific studies and analyze data effectively.
Actuary
Actuary
An Actuary assesses and manages financial risks in the insurance and finance industries. This course can be useful in providing a foundation in statistical and machine learning techniques that are increasingly being used in actuarial science for tasks such as risk modeling and pricing.
Biostatistician
Biostatistician
A Biostatistician designs and analyzes statistical studies and experiments in the biological sciences, working in collaboration with scientists to develop and test hypotheses and interpret results. This course may be useful in gaining foundational knowledge in mathematical distance, dimension reduction, principal component analysis, and other concepts that can be applied to statistical studies in the biological sciences, helping you build a foundation for success in this role.
Health Data Analyst
Health Data Analyst
A Health Data Analyst analyzes and interprets health-related data to improve patient care and outcomes. This course can be useful in providing a foundation in statistical and machine learning techniques that are commonly used in healthcare data analysis, helping you develop the skills necessary to extract meaningful insights from health data.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and manages risks in various industries. This course can be useful in providing a foundation in statistical and machine learning techniques that are commonly used in risk analysis, helping you develop the skills necessary to quantify and mitigate risks effectively.
Business Analyst
Business Analyst
A Business Analyst identifies and analyzes business needs and processes to improve efficiency and effectiveness. This course can be useful in providing a foundation in statistical and machine learning techniques that are increasingly being used in business analysis for tasks such as data analysis and predictive modeling.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. While this course is not directly related to software engineering, it can be useful in providing a foundation in statistical and machine learning concepts, which are increasingly being used in software development for tasks such as data analysis and model building.
For more career information including salaries, visit:
OpenCourser.com/course/w6qmt0/high
Reading list
We've selected 28 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
High-Dimensional Data Analysis.
Save
A comprehensive reference on deep learning, covering fundamental concepts, architectures, and applications.
A collection of case studies showcasing the use of Bioconductor for various genomics data analysis tasks.
Covers a wide range of statistical learning methods, including supervised and unsupervised learning, regularization, and model selection. A valuable reference for understanding the theoretical foundations of machine learning.
A classic textbook on machine learning, covering a wide range of topics including supervised and unsupervised learning, model evaluation, and regularization.
A textbook on statistical learning, covering a wide range of topics including supervised and unsupervised learning, model evaluation, and regularization.
Provides a solid introduction to the perceptual and cognitive principles behind visualization, and how to apply these insights to the design of effective data visualizations.
A textbook on high-dimensional data analysis, covering topics such as dimensionality reduction, clustering, and classification.
Save
A practical guide to data science using R, covering topics such as data manipulation, visualization, and machine learning.
Covers the use of bioinformatics tools and techniques to analyze genomic data, including sequence analysis, gene expression analysis, and genome-wide association studies. A valuable resource for learning how to use bioinformatics tools in genomics research.
Provides a comprehensive overview of dimension reduction techniques, with a focus on applications in machine learning. It valuable resource for researchers and students in machine learning and related fields.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for researchers and students in machine learning and related fields.
Provides a comprehensive overview of machine learning techniques, with a focus on applications in bioinformatics. It valuable resource for researchers and students in bioinformatics and related fields.
A textbook on statistical methods for bioinformatics, covering topics such as sequence alignment, gene expression analysis, and phylogenetic inference.
A practical guide to machine learning using the R programming language, covering a wide range of techniques and real-world applications. Useful for learning how to implement machine learning algorithms in R.
Provides a comprehensive overview of numerical linear algebra, with a focus on algorithms and applications. It valuable resource for researchers and students in scientific computing and related fields.
A practical guide to implementing machine learning models in Python using popular libraries.
Save
A comprehensive overview of data visualization techniques, including best practices, examples, and common pitfalls.
A comprehensive guide to R programming, covering topics such as data structures, functions, and graphics.
Provides a comprehensive overview of modern multivariate statistical techniques, with a focus on regression, classification, and manifold learning. It valuable resource for researchers and students in data analysis and related fields.
Save
A practical guide to genomics data analysis, covering topics such as data exploration, visualization, and statistical analysis.
Save
本书是中国学者编写的机器学习领域的经典著作，内容涵盖了机器学习、统计学习、人工智能等领域的基本概念和方法。深入浅出，适合中级学习者阅读。
Save
Provides a comprehensive overview of data mining, with a focus on concepts and techniques. It valuable resource for researchers and students in data mining and related fields.
Provides a practical guide to data management and analysis in bioinformatics, covering topics such as data integration, data visualization, and statistical analysis. A valuable resource for learning how to handle and analyze biological data.
Provides an introduction to applied linear algebra, with a focus on applications in engineering and computer science. It valuable resource for researchers and students in these fields.
Provides a practical introduction to machine learning. It covers a variety of topics, including data preprocessing, feature engineering, and model evaluation. It good resource for learners who want to apply machine learning techniques to real-world problems.
Provides a comprehensive overview of data mining techniques. It covers a wide range of topics, including data preprocessing, feature selection, and model evaluation. It valuable resource for learners who want to apply data mining techniques to business problems.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for learners who want to understand the theory and application of pattern recognition and machine learning techniques.
Provides a comprehensive overview of multivariate statistical analysis. It covers a wide range of topics, including multivariate regression, discriminant analysis, and factor analysis. It valuable resource for learners who want to understand the theory and application of multivariate statistical techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w6qmt0/high
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Effort
2 to 4 hours per week for 4 weeks
Level
Advanced
Via
edX
Institution
Harvard University
Instructors
Rafael Irizarry
Michael Love
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
العربية
हिंदी
Türkçe
తెలుగు
Bahasa Indonesia
Kiswahili
This course belongs to a
collection
called
Data Analysis for Life Sciences. It's comprised of four courses:
- Introduction to Linear Models and Matrix Algebra
- Statistical Inference and Modeling for High-throughput Experiments
- High-Dimensional Data Analysis (viewing)
- Statistics and R
Good to know
Introduces data analysis and interpretation concepts, catering to students interested in these fields
Starts off with mathematical foundations (distance and dimension reduction), providing a solid theoretical understanding
Covers both practical applications (e.g., dimensionality reduction, visualization) and theoretical foundations (e.g., singular value decomposition, principal component analysis)
Tackles a common challenge in genomics (batch effect) and provides techniques to detect and adjust for it
Provides a brief overview of machine learning concepts and their applications in high-throughput data analysis
Consists of seven parts designed to accommodate diverse learner backgrounds, allowing for tailored learning
Part of two professional certificates, indicating industry relevance and potential career advancement opportunities
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