Data Science with Julia

K-means and DBSCAN Clustering you own this product

This project is part of the liveProject series Hands-on Data Science with Julia
prerequisites
basics of Julia • intermediate data preprocessing • intermediate k-means clustering • intermediate DBSCAN clustering
skills learned
basic data preparation for clustering methods • clustering data with k-means and DBSCAN algorithms • evaluating and visualizing results
Łukasz Kraiński and Bogumił Kamiński
1 week · 4-6 hours per week · INTERMEDIATE

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Look inside
In this liveProject, you’ll use the Julia language and clustering algorithms to analyze sales data and determine groups of products with similar demand patterns. Clustering is a well-established unsupervised learning technique that’s commonly used to discover patterns and relations in data. You’ll apply k-means and DBSCAN clustering techniques to housing sales data for a retail startup, leveraging your basic Julia skills into mastery of this machine learning task.
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.

project authors

Bogumil Kaminski
Bogumił Kamiński is Head of the Decision Analysis and Support Unit and Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He also holds a position of adjunct professor at the Data Science Laboratory at Ryerson University and is affiliated with Fields Institute (Computational Methods in Industrial Mathematics Laboratory). In the Julia community, he is the owner of the JuliaData organization and a member of JuliaStats and JuliaLang organizations on GitHub. He also contributes to the community as the top answerer for the [julia] tag on Stack Overflow.
Lukasz Krainski
Łukasz Kraiński is a research assistant at the Decision Analysis and Support Unit at SGH Warsaw School of Economics. He is a certified cloud engineer with expertise in Azure and GCP cloud platforms. You can find him at tech conferences speaking about MLOps and AI (MLinPL 2019, PositivTech 2020, Data Driven Innovation 2020). Łukasz is also an active developer and maintainer of Julia packages (CGE.jl, SmartTransitionSim.jl).

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
TECHNIQUES
  • Intermediate data preprocessing
  • Basic clustering-related visualizations
  • Intermediate knowledge of k-means clustering and DBSCAN clustering
  • Basics of elbow method and silhouettes

you will learn

In this liveProject, you’ll explore the foundations of k-means and DBSCAN clustering algorithms, confidently apply them to data, and evaluate your output.

  • Basic data preparation for clustering methods
  • Clustering data with k-means and DBSCAN algorithms
  • Evaluating and visualizing the results of clustering
  • Analysis of final output to provide business-related insights

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book resources
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