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- access to all Manning books, including MEAPs!
team
5, 10 or 20 seats+ for your team - learn more
Look inside
In this liveProject, you’ll use the Julia programming language and dimensionality reduction techniques to visualize housing sales data on a scatter plot. This visualization will allow the marketing team to identify links and demand patterns in sales, and is also a useful tool for noise reduction or variance analysis. You’ll use the popular PCA algorithm to visualize the sales dataset with overlaid clustering assignments from k-means and DBSCAN methods, and then expand Julia’s capabilities by calling Python modules using the PyCall.jl package. This extra flexibility will allow you to explore the t-SNE and UMAP algorithms which have excellent results for high-dimensional datasets.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
book resources
When you start your liveProject, you get full access to the following books for 90 days.
prerequisites
This liveProject is for experienced data scientists and data analysts who are interested in building their skills in Julia. To begin this liveProject, you will need to be familiar with:TOOLS
- Basics of Jupyter Notebook
- Basics of Julia and intermediate experience in another high-level programming language such as Python or R
- Intermediate knowledge of scikit-learn and umap-learn Python packages
- Intermediate knowledge of plotting libraries
- Basic usage of MultivariateStats.jl package
- Basics of PyCall.jl and Conda.jl packages
- Basics of Arrow data format
- Intermediate usage of dimensionality reduction algorithms
- Intermediate analysis of PCA dimensionality reduction characteristics
- Basic scatterplots in 2D and 3D
- Basics of calling Python from Julia
you will learn
In this liveProject, you’ll master dimensionality reduction, unsupervised learning algorithms, and put the powerful Julia programming language into practice for real-world data science tasks.- PCA, t-SNE, and UMAP dimensionality reduction techniques
- Validating and analyzing output of PCA algorithm
- Calling Python modules from Julia
- Combining clustering and dimensionality reduction results
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
choose your plan
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$249.99
only $20.83 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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- choose twelve free products per year
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Dimensionality Reduction with PCA, t-SNE and UMAP project for free
Dimensionality Reduction with PCA, t-SNE and UMAP project for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
![]()
Dimensionality Reduction with PCA, t-SNE and UMAP project for free
