Daten-, KI- und Cloud-Kurse

Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.

Learn to create your own Python packages to make your code easier to use and share with others.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

Step right into the dynamic world of data modeling with Snowflake!

Learn to perform linear and logistic regression with multiple explanatory variables.

Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.

Fine-tune Llama for custom tasks using TorchTune, and learn techniques for efficient fine-tuning such as quantization.

Master multi-stage builds, Docker networking tools, and Docker Compose for optimal containerized applications!

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Learn the core techniques necessary to extract meaningful insights from time series data.

In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.

Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

Learn how to build interactive and insight-rich dashboards with Dash and Plotly.

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

Build the foundation you need to think statistically and to speak the language of your data.

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.

Discover how the Pinecone vector database is revolutionizing AI application development!

Take your reporting skills to the next level with Tableau’s built-in statistical functions.

Analyze text data in R using the tidy framework.

Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.