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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Learn business valuation with real-world applications and case studies using discounted cash flows (DCF).
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Learn how to visualize time series in R, then practice with a stock-picking case study.
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Learn how to design, automate, and monitor scalable forecasting pipelines in Python.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.
In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Azure Security
Learn how to reduce training times for large language models with Accelerator and Trainer for distributed training
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Learn how to pull character strings apart, put them back together and use the stringr package.
Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Create a healthcare AI agent using Haystack, an open-source framework for orchestrating LLMs and external components.