High-Dimensional Data Analysis from edX

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:

Genomics Data Analysis:

This class was supported in part by NIH grant R25GM114818.

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

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

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

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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

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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

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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

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Find the determinant of a matrix.
Invert a matrix.
Solve systems of linear equations.

Follow tutorials on using scikit-learn for machine learning tasks

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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

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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

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Volunteer with a data science organization or project

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Volunteering with a data science organization or project is a great way to gain practical experience and to make connections with other data scientists.

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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

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Creating content on a data science topic will help you to solidify your understanding of the material and to share your knowledge with others.

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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

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Participating in a data science competition or hackathon is a great way to test your skills and to learn from others.

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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

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Data Visualization Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Bioinformatics Analyst

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.

See salaries and explore the career path for Research Scientist

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Health Data 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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Software Engineer