Large scale visualizations of unstructured mesh data

Panel: The powerful data exploration & web app framework for Python

High-level tools to simplify visualization in Python.

Event for geo-coders to explore open tools and approaches for enhancing geospatial analysis

Schulungsmaterialen für die Cusy Python-Schulungen: https://cusy.io/de/seminare

Explore cryptocurrency market trends with Python using unsupervised learning techniques. Using Jupyter Notebooks to implement K-means clustering and Principal Component Analysis (PCA) to analyze and predict price trends of cryptocurrencies over 24-hour and 7-day periods.

Unsupervised learning model to predict if cypto are affected by price changes

Objective: Utilize data visualization skills to find properties that are viable investments

An analysis of the historical data of the Berlin marathon, highlighting trends in running speeds, changes in average and fastest times over the years, and how age and gender affects performance.

Weather and Perfect Vacationing Spots Worldwide, by using APIs

Phyton API Challenge

Unsupervised Learning to predict cryptocurrencies

In this challenge, I’ll use my knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

Module 6 Challenge

How do crypto-currencies behave? What factors affect them?

A way to both track your spending as well as set savings goals (incorporates data analysis and financial decision making).

In this assignment, I'll use numerical/visual aggregation, interactive visualizations, widgets, and explore the geospatial relationships in the data by using interactive visualizations with hvPlot and GeoViews.

Use scikit-learn and the KMeans clustering algorithm to analyse a Crypto currency dataset