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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.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.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.
Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.
Learn how to visualize time series in R, then practice with a stock-picking case study.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.
Master AI for marketing to plan smarter campaigns, create quality content, and build custom AI agents.
Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Azure Security
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn business valuation with real-world applications and case studies using discounted cash flows (DCF).
Learn how to access financial data from local files as well as from internet sources.
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
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 to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.
Learn how to reduce training times for large language models with Accelerator and Trainer for distributed training