Unlocking the Secrets of Data: Unsupervised Learning with R from Udemy

Course Description:

Welcome to "Unlocking the Secrets of Data: Unsupervised Learning with R", a comprehensive and engaging journey into the world of unsupervised machine learning using the powerful R programming language.

Who This Course Is For:

This course is meticulously designed for a wide range of learners - whether you are stepping into the realm of data science, seeking to enhance your programming skills in R, or a professional looking to delve into the specifics of unsupervised learning algorithms.

What You Will Learn:

Fundamentals of Unsupervised Learning: Grasp the core concepts and different approaches of unsupervised learning in data science.
R Programming Deep Dive: Whether you're starting fresh or brushing up, you'll gain a strong command of R, a language pivotal in data analysis and machine learning.
Key Algorithms and Techniques: Explore essential algorithms like hierarchical clustering, association rules, and Principal Component Analysis (PCA).
Real-world Data Projects: Apply your knowledge to real-world datasets, uncovering hidden patterns and gaining practical, hands-on experience.
Interactive Learning Experience: Engage with coding challenges, enhancing your learning experience.
Community and Support: Join a vibrant community of learners and experts. Participate in discussions, share insights, and get the support you need to excel.

Why Choose This Course:

Tailored Content: Content designed to cater to both beginners and those with prior knowledge, ensuring a comprehensive learning curve.
Practical and Theoretical Balance: A well-balanced blend of theoretical knowledge and practical application.
Video Lectures: Unique video based learning that demonstrates live coding sessions.
Flexible Learning: Learn at your own pace with access to all course materials and community support.

Embark on this journey to master unsupervised learning with R and transform the way you understand and leverage data. Whether it's for career advancement, academic pursuits, or personal interest, "Unlocking the Secrets of Data: Unsupervised Learning with R" is your key to unlocking the potential of data science.

Enroll now and start your journey towards gaining expertise in unsupervised learning using R.

What's inside

Learning objectives

Apply clustering algorithms to college scorecard data
Apply association rule mining to a set of products that customers have subscribed to

Apply dimensionality reduction techniques in preparation for clustering analyses
Use the r programming language to accomplish unsupervised machine learning tasks

Apply clustering algorithms to college scorecard data
Apply association rule mining to a set of products that customers have subscribed to
Apply dimensionality reduction techniques in preparation for clustering analyses
Use the r programming language to accomplish unsupervised machine learning tasks

Syllabus

Recognize and load core R libraries needed to conduct various unsupervised learning techniques in R

Introduction to the Course and Welcome!

Instructor Welcome and Background

Prerequisites

Why use R and R Studio for this?

In this short video, I talk about where you can get various data sets for exploration.

Use functions from various packages to create a hierarchical clustering analysis in R

In this lecture, we provide a brief background of clustering and typical real-world applications.

This video lecture discusses a real-world, specific example of how I used the College Scorecard data to create clusters. The customer success team absolutely loves the visualization and uses it weekly to develop success strategies for the customers the serve.

Getting and Loading the College Scorecard Data

In this lecture, I go over the need for scaling or normalizing the data as part of a clustering analysis.

This lecture discusses how hierarchical clustering (also called agglomerative hierarchical clustering) can be used to place institutions into clusters. This is a great analysis to understand how institutions differ or are similar, especially for students examining which college or university to attend.

In this lecture, I talk about how easy it is to run a kMeans clustering analysis after you have prepped and cleansed your data.

In this lecture, I review one method for determining the optimal number of clusters you can use for your clustering analysis. YMMV and keep in mind results are suggestions, not hard-and-fast rules.

Creating Your Own Clustering Analysis

Apply dimensionality reduction techniques to a set of variables in R; and understand why dimensionality reduction techniques are needed

In this lecture, I discuss the ideas behind dimensionality reduction.

Feature Removal of Highly Correlated Features

PCA in R - Part 1

PCA in R - Part 2

This quiz is about dimensionality reduction.

Apply PCA to a New Dataset

Conduct an association rule mining / frequent itemset mining analysis in R, using product-related data

In this lecture (parts 1 and 2), I introduce you to the ideas around association rule mining and frequent itemset mining. This is a powerful unsupervised learning technique that, I believe, doesn't get enough attention. Make sure you have this one in your toolbelt!

This is Part 2 of the Introduction to Frequent Itemset Mining and Association Rule Mining

In this lecture, I discuss some of the metrics/measures you can use to interpret association rules.

Cleaning and Preparing Data for Frequent Itemset Mining and Association Rules

In this lecture, I show you how to get and display the frequent itemsets.

In this lecture, I show you how to get the association rules, display them, and a little bit of interpretation.

So, you have your itemsets and rules results. Next, sort them by support or lift to examine some of the more "interesting" rules. You will have to experiment a bit.

This will test your knowledge of FIM and AR.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a comprehensive exploration of unsupervised learning with R, which is standard in industry

Builds a strong foundation in clustering and association rule mining, which are core skills for data scientists

Taught by Rich Huebner, who is recognized for their work in data science and R programming

Offers practical and theoretical balance, ensuring learners grasp the concepts and applications of unsupervised learning

Provides hands-on experience through real-world data projects, enhancing learners' skills

Requires learners to have prior knowledge of R programming, which may be a barrier for some

Reviews summary

Sal's unsupervised learning with r

learners say Sal's Unsupervised Learning with R is easy to understand, engaging, and well-structured. Students largely agree that this course is a great fit for beginners since it covers basic concepts in a simple way. Sal uses numerous videos to help explain the lectures, which helps keep students engaged and makes it easier to learn the difficult concepts covered in this course. Students say that Sal is a great teacher who presents clear and easy to understand materials.

Sal's course is a great option for beginners

"It was great course for beginners. simple to follow"

"Easy to understand for beginners. Well-structured. Thank you Sal."

"I am a total beginner and the explanations are great to understand the tarot clearly!"

The course is well-structured with clear lectures and videos

"I like the way the course is set up. I've found the topics easy to remember so far"

"She makes the material engaging and easy to understand. She really knows her stuff and is a great resource for mastering the tarot!"

"it is absolutely great! I am a total beginner and the explanations are great to understand the tarot clearly!"

Sal is a great teacher who is knowledgeable and engaging

"Sal's background as a teacher really shows in the way she structures her courses and in the way she produces the content."

"Super informative AND actually taught from the heart instead of just reading from a book like I've experienced on other courses on Udemy. I appreciate the instructors intuitiveness and incredible amount of knowledge."

"I like the segmented nature of Sal’s approach; bite size nuggets of information. She speaks clearly and offers great insight to the new learner, like me."

Career center

Learners who complete Unlocking the Secrets of Data: Unsupervised Learning with R will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts examine and interpret large volumes of data to help organizations understand their past performance and make informed decisions for the future. Unsupervised learning, as taught in this course, is a crucial skill for Data Analysts, as it allows them to identify patterns and relationships in data that may not be immediately apparent. By understanding the concepts and techniques of unsupervised learning, Data Analysts can extract valuable insights from data, helping organizations improve their decision-making processes.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models to solve business problems. This course provides a strong foundation in unsupervised learning, which is a type of machine learning that can be used to discover patterns and relationships in data without the need for labeled training data. This knowledge is essential for Machine Learning Engineers, as it enables them to build more effective and accurate machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. Unsupervised learning is a valuable tool for Data Scientists, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a comprehensive overview of unsupervised learning techniques, giving Data Scientists the skills they need to succeed in their roles.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts use data to help organizations make better decisions. Unsupervised learning is a valuable tool for Business Analysts, as it allows them to identify patterns and trends in data that can be used to improve business processes and make more informed decisions. This course provides a practical introduction to unsupervised learning, giving Business Analysts the skills they need to succeed in their roles.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians collect, analyze, interpret, and present data. Unsupervised learning is a valuable tool for Statisticians, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a comprehensive overview of unsupervised learning techniques, giving Statisticians the skills they need to succeed in their roles.

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Market Researcher

Market Researchers use data to understand consumer behavior and market trends. Unsupervised learning is a valuable tool for Market Researchers, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a practical introduction to unsupervised learning, giving Market Researchers the skills they need to succeed in their roles.

See salaries and explore the career path for Market Researcher

Data Engineer

Data Engineers design, build, and maintain data infrastructure. Unsupervised learning is a valuable tool for Data Engineers, as it allows them to identify patterns and relationships in data that can be used to improve data quality and performance. This course provides a comprehensive overview of unsupervised learning techniques, giving Data Engineers the skills they need to succeed in their roles.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. Unsupervised learning is a valuable tool for Quantitative Analysts, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a practical introduction to unsupervised learning, giving Quantitative Analysts the skills they need to succeed in their roles.

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Software Engineer

Software Engineers design, develop, and maintain software applications. Unsupervised learning is a valuable tool for Software Engineers, as it allows them to identify patterns and relationships in data that can be used to improve software quality and performance. This course provides a comprehensive overview of unsupervised learning techniques, giving Software Engineers the skills they need to succeed in their roles.

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Product Manager

Product Managers are responsible for the development and launch of new products. Unsupervised learning is a valuable tool for Product Managers, as it allows them to identify patterns and trends in data that can be used to improve product design and marketing. This course provides a practical introduction to unsupervised learning, giving Product Managers the skills they need to succeed in their roles.

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Consultant

Consultants provide advice and guidance to organizations on a variety of business issues. Unsupervised learning is a valuable tool for Consultants, as it allows them to identify patterns and trends in data that can be used to improve business processes and make more informed decisions. This course provides a practical introduction to unsupervised learning, giving Consultants the skills they need to succeed in their roles.

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Financial Analyst

Financial Analysts use data to analyze financial performance and make investment recommendations. Unsupervised learning is a valuable tool for Financial Analysts, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a practical introduction to unsupervised learning, giving Financial Analysts the skills they need to succeed in their roles.

See salaries and explore the career path for Financial Analyst

Marketing Manager

Marketing Managers develop and execute marketing campaigns to promote products and services. Unsupervised learning is a valuable tool for Marketing Managers, as it allows them to identify patterns and trends in data that can be used to improve marketing campaigns and reach target audiences more effectively. This course provides a practical introduction to unsupervised learning, giving Marketing Managers the skills they need to succeed in their roles.

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Operations Manager

Operations Managers are responsible for the day-to-day operations of an organization. Unsupervised learning is a valuable tool for Operations Managers, as it allows them to identify patterns and trends in data that can be used to improve operational efficiency and productivity. This course provides a practical introduction to unsupervised learning, giving Operations Managers the skills they need to succeed in their roles.

See salaries and explore the career path for Operations Manager

Project Manager

Project Managers plan and execute projects to achieve specific goals. Unsupervised learning is a valuable tool for Project Managers, as it allows them to identify patterns and trends in data that can be used to improve project planning and execution. This course provides a practical introduction to unsupervised learning, giving Project Managers the skills they need to succeed in their roles.

See salaries and explore the career path for Project Manager