Data, AI, and Cloud Courses

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.

Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.

Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.

Enhance your Tableau skills with this case study on inventory analysis. Analyze a dataset, create calculated fields, and create visualizations.

In this course, youll learn how to implement more advanced Bayesian models using RJAGS.

Take your Julia skills to the next level with our intermediate Julia course. Learn about loops, advanced data structures, timing, and more.

This course will show you how to combine and merge datasets with data.table.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Learn defensive programming in R to make your code more robust.

Learn to create animated graphics and linked views entirely in R with plotly.

Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.

Learn how to visualize big data in R using ggplot2 and trelliscopejs.

Learn how to tune your models hyperparameters to get the best predictive results.

Predict employee turnover and design retention strategies.

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.

Learn to detect fraud with analytics in R.

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

Learn to analyze and model customer choice data in R.

Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Continue learning with purrr to create robust, clean, and easy to maintain iterative code.

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.

Use C++ to dramatically boost the performance of your R code.

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

Learn to build simple models of market response to increase the effectiveness of your marketing plans.

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.