Data, AI, and Cloud Courses

• BasicSkill Level
• 4.5+
• 119

Elevate your analysis with this hands-on course using SQL with DataLab workbooks.

• IntermediateSkill Level
• 4.7+
• 118

Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

• BasicSkill Level
• 4.5+
• 113

Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.

• AdvancedSkill Level
• 4.4+
• 113

In this course youll learn how to leverage statistical techniques for working with categorical data.

• BasicSkill Level
• 4.9+
• 110

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

• IntermediateSkill Level
• 4.5+
• 109

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

• IntermediateSkill Level
• 4.6+
• 108

Learn the fundamentals of valuing stocks.

• AdvancedSkill Level
• 4.8+
• 105

Learn to process sensitive information with privacy-preserving techniques.

• AdvancedSkill Level
• 4.2+
• 105

Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

• IntermediateSkill Level
• 4.5+
• 104

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

• IntermediateSkill Level
• 4.7+
• 103

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

• IntermediateSkill Level
• 4.7+
• 103

Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.

• IntermediateSkill Level
• 4.7+
• 98

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

• IntermediateSkill Level
• 4.6+
• 98

Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.

• AdvancedSkill Level
• 4.7+
• 97

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

• IntermediateSkill Level
• 4.3+
• 97

Learn to detect fraud with analytics in R.

• IntermediateSkill Level
• 4.5+
• 96

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

• IntermediateSkill Level
• 4.8+
• 94

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

• IntermediateSkill Level
• 4.4+
• 93

You will use Net Revenue Management techniques in Google Sheets for a Fast Moving Consumer Goods company.

• IntermediateSkill Level
• 4.7+
• 92

Learn to analyze, plot, and model multivariate data.

• IntermediateSkill Level
• 4.3+
• 92

In this course youll learn how to apply machine learning in the HR domain.

• BasicSkill Level
• 4.7+
• 90

Explore GDPR through real-world cases on data rights, breaches, and compliance challenges.

• IntermediateSkill Level
• 4.6+
• 89

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

• IntermediateSkill Level
• 4.7+
• 88

Explore HR data analysis in Tableau with this case study.

• AdvancedSkill Level
• 5.0+
• 83

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

• IntermediateSkill Level
• 4.4+
• 83

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

• IntermediateSkill Level
• 4.5+
• 82

In ecommerce, increasing sales and reducing expenses are top priorities. In this case study, youll investigate data from an online pet supply company.

• BasicSkill Level
• 4.7+
• 80

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

• IntermediateSkill Level
• 4.4+
• 79

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

• BasicSkill Level
• 4.8+
• 78

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