Suivez de courtes vidéos animées par des instructeurs experts, puis mettez en pratique ce que vous avez appris avec des exercices interactifs dans votre navigateur.
ou
En continuant, vous acceptez nos Conditions d'utilisation, notre Politique de confidentialité et le fait que vos données sont stockées aux États-Unis.In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.
Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Utilisez des RNN pour classifier le sentiment, générer des phrases et traduire des textes.
Améliorez vos compétences KNIME avec notre cours sur la transformation des données, les opérations en colonnes et loptimisation du flux de travail.
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.
Réduisez les temps d’entraînement des LLM avec Accelerator et Trainer pour l’entraînement distribué.
Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
Create a healthcare AI agent using Haystack, an open-source framework for orchestrating LLMs and external components.
Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Learn how to pull character strings apart, put them back together and use the stringr package.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.
Automate data manipulation with KNIME, mastering merging, aggregation, database workflows, and advanced file handling.
In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.