Visualizing Big Data with Trelliscope in R
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
Learn to create animated graphics and linked views entirely in R with plotly.
Use generative AI to tackle data cleaning, fixing duplicates, nulls, and formatting for consistent, accurate datasets.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Use C++ to dramatically boost the performance of your R code.
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.