Data-, AI- och molnkurser

• MellanliggandeFärdighetsnivå
• 4.4+
• 116

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

• MellanliggandeFärdighetsnivå
• 4.7+
• 114

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

• GrundläggandeFärdighetsnivå
• 4.5+
• 113

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

• MellanliggandeFärdighetsnivå
• 4.6+
• 111

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

• MellanliggandeFärdighetsnivå
• 4.6+
• 110

Integrate AI/LLM applications with APIs, databases, and filesystems easier than ever before with the Model Context Protocol (MCP).

• MellanliggandeFärdighetsnivå
• 4.5+
• 108

Learn the fundamentals of valuing stocks.

• MellanliggandeFärdighetsnivå
• 4.4+
• 107

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.

• GrundläggandeFärdighetsnivå
• 4.8+
• 102

This course explores how organizations can use custom gen AI agents to help tackle specific business challenges.

• AvanceradFärdighetsnivå
• 4.2+
• 102

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

• GrundläggandeFärdighetsnivå
• 4.7+
• 100

Innovating with Google Cloud AI

• MellanliggandeFärdighetsnivå
• 4.7+
• 100

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

• MellanliggandeFärdighetsnivå
• 4.7+
• 100

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

• MellanliggandeFärdighetsnivå
• 4.6+
• 100

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

• AvanceradFärdighetsnivå
• 4.7+
• 99

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

• MellanliggandeFärdighetsnivå
• 4.3+
• 99

Learn to detect fraud with analytics in R.

• MellanliggandeFärdighetsnivå
• 4.5+
• 98

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

• AvanceradFärdighetsnivå
• 4.7+
• 97

Learn to process sensitive information with privacy-preserving techniques.

• AvanceradFärdighetsnivå
• 4.6+
• 97

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

• MellanliggandeFärdighetsnivå
• 4.6+
• 97

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

• MellanliggandeFärdighetsnivå
• 4.5+
• 97

Learn to analyze, plot, and model multivariate data.

• MellanliggandeFärdighetsnivå
• 4.6+
• 96

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

• MellanliggandeFärdighetsnivå
• 4.6+
• 95

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

• MellanliggandeFärdighetsnivå
• 4.3+
• 94

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

• MellanliggandeFärdighetsnivå
• 4.8+
• 93

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

• MellanliggandeFärdighetsnivå
• 4.3+
• 93

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

• MellanliggandeFärdighetsnivå
• 4.7+
• 92

Explore HR data analysis in Tableau with this case study.

• GrundläggandeFärdighetsnivå
• 4.8+
• 91

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

• MellanliggandeFärdighetsnivå
• 4.4+
• 82

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

• AvanceradFärdighetsnivå
• 5.0+
• 81

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

• GrundläggandeFärdighetsnivå
• 4.8+
• 81

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