Unsupervised Learning in Python faq

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Unsupervised learning in Python is a great way to discover underlying groups in a dataset. Learn how to cluster companies based on stock market prices and distinguish different species using measurement clustering. Also, explore hierarchical clustering and t-SNE, two unsupervised learning techniques for data visualisation. Finally, discover Principal Component Analysis (PCA), a dimension reduction technique used to improve model performance and generalisation, and Non-negative matrix factorization (NMF), a technique that expresses samples as combinations of interpretable parts.

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Course Overview

❗The content presented here is sourced directly from Datacamp platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [June 30th, 2023]

This course provides an introduction to unsupervised learning in Python. Students will learn how to find the underlying groups (or "clusters") in a dataset. Examples of clustering include companies based on stock market prices and distinguishing different species based on measurement clustering. The course will also cover hierarchical clustering and t-SNE, two unsupervised learning techniques for data visualisation. Additionally, students will learn about "Principal Component Analysis," the most fundamental dimension reduction technique ("PCA"). To improve model performance and generalisation, PCA is frequently used prior to supervised learning. Finally, students will learn about "Non-negative matrix factorization" ("NMF"), a dimension reduction technique that expresses samples as combinations of interpretable parts. By the end of the course, students will have a better understanding of unsupervised learning and how to apply it to their own datasets.

[Applications]
After completing this course, students can apply the knowledge they have gained to a variety of tasks. For example, they can use unsupervised learning techniques to identify underlying groups in a dataset, such as clustering companies based on stock market prices or distinguishing different species based on measurement clustering. They can also use dimension reduction techniques such as Principal Component Analysis and Non-negative Matrix Factorization to improve model performance and generalisation. Finally, they can use data visualisation techniques such as hierarchical clustering and t-SNE to better understand the data.

[Career Path]
One job position path that is recommended for learners of this course is a Data Scientist. Data Scientists are responsible for analyzing large amounts of data and using their findings to inform decisions and strategies. They use a variety of techniques, including unsupervised learning, to uncover patterns and trends in data. They also develop predictive models to forecast future outcomes. Data Scientists must have strong technical skills, including knowledge of programming languages such as Python, as well as an understanding of statistics and machine learning.

The development trend for Data Scientists is very positive. As businesses become increasingly data-driven, the demand for Data Scientists is expected to continue to grow. Companies are looking for Data Scientists who can help them make sense of their data and use it to make better decisions. Data Scientists are also in demand in the fields of healthcare, finance, and retail, as well as in the public sector. As technology advances, Data Scientists will be expected to stay up to date with the latest tools and techniques.

[Education Path]
The recommended educational path for learners interested in unsupervised learning in Python is to pursue a degree in Data Science. Data Science is an interdisciplinary field that combines mathematics, statistics, computer science, and domain knowledge to extract insights from data. It involves the use of algorithms, methods, and tools to analyze and interpret data.

Data Science degrees typically include courses in programming, data analysis, machine learning, and artificial intelligence. Students will learn how to use Python to analyze data, build predictive models, and create visualizations. They will also learn how to use unsupervised learning techniques such as hierarchical clustering, t-SNE, PCA, and NMF to uncover patterns and insights from data.

The development trend of Data Science degrees is to focus on the application of data science in various industries. This includes courses in healthcare, finance, marketing, and other industries. Students will learn how to use data science to solve real-world problems and create value for businesses. Additionally, courses in ethical considerations and data privacy are becoming increasingly important as data science is used more widely.

Course Syllabus

Clustering for dataset exploration

Visualization with hierarchical clustering and t-SNE

Decorrelating your data and dimension reduction

Discovering interpretable features

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