Performing Dimension Analysis with R from Pluralsight

This course covers virtually all of the important techniques of dimensionality reduction available in R, allowing model builders to optimize model performance by reducing overfitting and saving on model training time and cost.

Dimensionality Reduction is a powerful and versatile unsupervised machine learning technique that can be used to improve the performance of virtually every ML model. Using dimensionality reduction, you can significantly speed up model training and validation, saving both time and money, as well as greatly reducing the risk of overfitting.

In this course, Performing Dimension Analysis with R, you will gain the ability to design and implement an exhaustive array of feature selection and dimensionality reduction techniques in R. First, you will learn the importance of dimensionality reduction and understand the pitfalls of working with data of excessively high-dimensionality, often referred to as the curse of dimensionality. Next, you will discover how to implement simple feature selection techniques to decide which subset of the existing features we might choose to use while losing as little information from the original, full dataset as possible.

You will then learn important techniques for reducing dimensionality in linear data. Such techniques, notably Principal Components Analysis and Linear Discriminant Analysis, seek to re-orient the original data using new, optimized axes. The choice of these axes is driven by numeric procedures such as Eigenvalue and Singular Value Decomposition.

You will then move to dealing with manifold data, which is non-linear and often takes the form of Swiss rolls and S-curves. Such data presents an illusion of complexity but is actually easily simplified by unrolling the manifold.

Finally, you will explore how to implement a wide variety of manifold learning techniques including multi-dimensional scaling (MDS), Isomap, and t-distributed Stochastic Neighbor Embedding (t-SNE). You will round out the course by comparing the results of these manifold unrolling techniques with artificially generated data. When you are finished with this course, you will have the skills and knowledge of Dimensionality Reduction needed to design and implement ways to mitigate the curse of dimensionality in R.

What's inside

Syllabus

Course Overview

Understanding the Importance of Reducing Complexity in Data

Performing Dimensional Analysis for Continuous Data

Performing Dimensional Analysis for Categorical Data

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Develops essential data science skills for working with complex, high-dimensional datasets, helping mitigate model overfitting and reduce training time

Provides comprehensive coverage of feature selection and dimensionality reduction techniques in R, empowering learners to make informed data-driven decisions

Leverages manifold learning techniques to unravel complex non-linear data structures, enabling learners to uncover hidden patterns and insights from data

Taught by Janani Ravi, an experienced instructor with a strong reputation in data science and dimensionality reduction

Requires foundational knowledge in statistics, linear algebra, and R programming, making it suitable for intermediate to advanced learners

May require additional resources for hands-on practice and project implementation

Reviews summary

Comprehensive r dimensionality analysis

According to learners, this course offers a comprehensive and practical deep dive into dimensionality reduction techniques in R. Many found the instructor's explanations exceptionally clear, making complex topics like t-SNE and PCA accessible. The hands-on coding examples are frequently highlighted as highly valuable and directly applicable to real-world data science problems, striking a good balance between theory and practical implementation. While the course is largely seen as a strong asset for data professionals, some feedback suggests a fast pace or potential need for prior R/statistics knowledge. Recent reviews indicate its continued relevance, though a few note minor R package versioning issues, suggesting an ongoing need for updates.

Strikes a balance, but some desired more mathematical depth.

"It strikes a great balance between theory and practical application, providing enough mathematical intuition without getting bogged down in proofs."

"My only constructive feedback would be on the depth of theoretical explanations – sometimes I wanted a little more 'why' behind the 'how'..."

"Overall, it strikes a good balance, but for those craving deep theoretical dives, supplemental material might be beneficial."

Strong emphasis on practical R coding and real-world applicability.

"I particularly valued the hands-on coding examples; they were well-structured and directly applicable to real-world data science problems."

"I found the practical exercises useful... it's a solid course that delivers on its promise."

"The R implementations were straightforward, making it easy to follow along and apply to my own datasets."

Instructor excels at simplifying complex statistical and ML concepts.

"The instructor's explanations of complex topics like t-SNE and PCA were incredibly clear, breaking down dense concepts into digestible parts."

"While the instructor explains things well, I often had to pause and re-watch sections."

"The sections on manifold learning were particularly insightful and demystified techniques I struggled with before."

Covers a wide array of essential dimensionality reduction techniques.

"This course is a phenomenal resource for anyone looking to truly understand and implement dimensionality reduction in R."

"Fantastic course! I've been looking for a comprehensive guide to dimensionality reduction that goes beyond just PCA, and this delivered."

"I appreciated the detailed walkthroughs for each technique. I feel much more confident applying these methods in my work."

A few reviews note slight outdatedness in R package versions.

"I found the practical exercises useful, though some of the R packages used seemed to be slightly older versions."

"It would be great if the course material could be updated regularly to reflect the latest R practices and package versions."

"The R code snippets occasionally had errors or depended on specific package versions that caused issues on my system."

Some learners found the pace fast, suggesting prior R/stats knowledge is beneficial.

"The content is good, but I felt the pace was a bit too fast for someone like me who's relatively new to R and machine learning concepts."

"Some more introductory material or a clearer statement of prerequisites would be helpful."

"The explanations sometimes assumed too much prior knowledge of advanced statistics, making it hard to keep up."

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 Performing Dimension Analysis with R with these activities:

Organize and Review Course Materials

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Improve your understanding and retention of course materials by organizing and reviewing lecture notes, assignments, and other resources.

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Gather and organize all course materials, including lecture notes, assignments, quizzes, and exams.
Review materials regularly, focusing on key concepts and techniques.
Identify areas where further clarification or practice is needed.

Review Linear Algebra Fundamentals

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Refresh your knowledge of vector and matrix operations, linear transformations, and vector spaces to strengthen your foundation for dimensionality reduction techniques.

Browse courses on Linear Algebra

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Review fundamental concepts of linear algebra, such as matrix operations, vectors, and vector spaces.
Practice solving systems of linear equations and matrix computations.
Explore eigenvalues and eigenvectors to gain insights into linear transformations.

Review Linear Algebra and Multivariate Statistics

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Strengthen your foundational knowledge by reviewing linear algebra and multivariate statistics, which are essential for understanding dimensionality reduction techniques.

Browse courses on Linear Algebra

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Review fundamental concepts of linear algebra, such as vectors, matrices, and linear transformations.
Explore concepts of multivariate statistics, including probability distributions, covariance, and correlation.
Practice solving problems and working through examples to reinforce your understanding.

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Solve Dimensionality Reduction Practice Problems

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Strengthen your analytical skills and problem-solving abilities by working through a series of practice problems related to dimensionality reduction.

Browse courses on Dimensionality Reduction

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Find online resources or textbooks that provide practice problems on dimensionality reduction.
Attempt to solve the problems independently, referring to course materials as needed.
Review and analyze your solutions, identifying areas for improvement.

Participate in a Peer Study Group

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Engage in collaborative learning by joining a peer study group to discuss concepts, solve problems, and reinforce your understanding of dimensionality reduction.

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Find or create a study group with peers who share similar interests and goals.
Establish regular meeting times and set clear objectives for each session.
Actively participate in discussions, share insights, and seek feedback from group members.

Follow Tutorials on Dimensionality Reduction Algorithms

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Enhance your understanding of dimensionality reduction algorithms by following online tutorials and implementing them in practice.

Browse courses on Principal Component Analysis

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Identify and select relevant tutorials that cover various dimensionality reduction algorithms.
Follow the tutorials, implement the algorithms in R, and experiment with different datasets.
Compare the results and performance of different algorithms to gain practical insights.

Create a Visual Representation of Dimensionality Reduction

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Demonstrate your understanding of dimensionality reduction by creating a visual representation, such as an infographic or interactive visualization, that explains the concepts and techniques in a clear and engaging way.

Browse courses on Data Visualization

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Choose a suitable dataset and apply dimensionality reduction techniques to reduce its dimensionality.
Select an appropriate visualization method to represent the reduced data, considering factors such as clarity, effectiveness, and audience.
Design and create the visual representation, ensuring it is visually appealing, informative, and easy to understand.

Build a Dimensionality Reduction Model

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Apply your knowledge to build a real-world dimensionality reduction model, demonstrating your understanding of feature selection, data preprocessing, and model evaluation.

Browse courses on Model Building

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Gather a suitable dataset and explore its characteristics.
Apply dimensionality reduction techniques to select relevant features and reduce data complexity.
Build a machine learning model using the reduced dataset and evaluate its performance.
Present your findings and insights in a clear and concise manner.

Career center

Learners who complete Performing Dimension Analysis with R will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. This course, Performing Dimension Analysis with R, is highly relevant to this role as it provides the skills needed to optimize model performance. Dimensionality reduction techniques like those taught in this course help reduce overfitting, improve model training efficiency, and enhance model accuracy.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, interpret, and present data. This course, Performing Dimension Analysis with R, is highly relevant to this role as it provides the skills needed to handle complex datasets and extract meaningful insights. Dimensionality reduction techniques like those taught in this course help Statisticians overcome the challenges of high-dimensional data and improve the accuracy of their analysis.

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Data Scientist

Data Scientists clean and analyze vast quantities of data to extract meaningful insights for companies. This course, Performing Dimension Analysis with R, provides the skills and knowledge needed to reduce the complexity of high-dimensional data, a common challenge faced by Data Scientists. By learning techniques like Principal Components Analysis and t-SNE, you'll be better equipped to handle complex datasets and extract valuable insights.

See salaries and explore the career path for Data Scientist

Research Scientist

Research Scientists conduct scientific research to advance knowledge and develop new technologies. This course, Performing Dimension Analysis with R, provides valuable skills for Research Scientists as it teaches techniques to analyze complex datasets and extract meaningful insights. By understanding the curse of dimensionality and applying dimensionality reduction techniques, Research Scientists can improve the accuracy and efficiency of their research.

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Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. This course, Performing Dimension Analysis with R, provides valuable skills for Data Analysts as it teaches techniques to reduce data complexity and extract meaningful insights. By understanding the curse of dimensionality and applying dimensionality reduction techniques, Data Analysts can improve the accuracy and efficiency of their analysis.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course, Performing Dimension Analysis with R, provides valuable skills for Quantitative Analysts as it teaches techniques to reduce data complexity and extract meaningful insights. By understanding the curse of dimensionality and applying dimensionality reduction techniques, Quantitative Analysts can improve the accuracy and efficiency of their analysis, leading to better investment decisions.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure. This course, Performing Dimension Analysis with R, provides valuable skills for Data Engineers as it teaches techniques to optimize data storage and processing. Dimensionality reduction techniques like those taught in this course help Data Engineers reduce data size, improve data quality, and enhance the performance of data pipelines.

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Business Analyst

Business Analysts use data to identify opportunities and solve problems for businesses. This course, Performing Dimension Analysis with R, provides valuable skills for Business Analysts as it teaches techniques to reduce data complexity and extract meaningful insights. By understanding the curse of dimensionality and applying dimensionality reduction techniques, Business Analysts can improve the accuracy and efficiency of their analysis, leading to better decision-making.

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Data Architect

Data Architects design and manage data systems and infrastructure. This course, Performing Dimension Analysis with R, may be useful for Data Architects who work with large datasets. Dimensionality reduction techniques like those taught in this course can help Data Architects optimize data storage and processing, improve data quality, and enhance the performance of data systems.

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Product Manager

Product Managers define and manage the development of products. This course, Performing Dimension Analysis with R, may be useful for Product Managers who work with data-driven products. Dimensionality reduction techniques like those taught in this course can help Product Managers understand user behavior, identify market trends, and make informed decisions about product development.

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Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. This course, Performing Dimension Analysis with R, may be useful for Financial Analysts who work with large datasets. Dimensionality reduction techniques like those taught in this course can help Financial Analysts identify investment opportunities, manage risk, and make informed investment decisions.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. This course, Performing Dimension Analysis with R, may be useful for Actuaries who work with large datasets. Dimensionality reduction techniques like those taught in this course can help Actuaries identify risks, manage uncertainty, and make informed decisions about insurance products and financial strategies.

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Marketing Analyst

Marketing Analysts analyze marketing data to understand customer behavior and improve marketing campaigns. This course, Performing Dimension Analysis with R, may be useful for Marketing Analysts who work with large datasets. Dimensionality reduction techniques like those taught in this course can help Marketing Analysts identify customer segments, track campaign performance, and make informed decisions about marketing strategies.

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Project Manager

Project Managers plan, execute, and deliver projects. This course, Performing Dimension Analysis with R, may be useful for Project Managers who work with data-intensive projects. Dimensionality reduction techniques like those taught in this course can help Project Managers identify risks, track progress, and make informed decisions throughout the project lifecycle.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course, Performing Dimension Analysis with R, may be useful for Software Engineers who work with data-intensive applications. Dimensionality reduction techniques like those taught in this course can help Software Engineers optimize data storage and processing, leading to improved software performance and efficiency.

See salaries and explore the career path for Software Engineer

Performing Dimension Analysis with R

What's inside

Syllabus

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Reviews summary

Comprehensive r dimensionality analysis

Activities

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