Data, AI, and Cloud Courses

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

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

Elevate decision-making skills with Decision Models, analysis methods, risk management, and optimization techniques.

Learn how to reduce training times for large language models with Accelerator and Trainer for distributed training

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

Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.

Enhance your Tableau skills with this case study on inventory analysis. Analyze a dataset, create calculated fields, and create visualizations.

Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.

Learn to use the Bioconductor package limma for differential gene expression analysis.

Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.

Learn to easily summarize and manipulate lists using the purrr package.

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!

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.

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

In this course youll learn how to use data science for several common marketing tasks.

In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

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

Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.

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

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

In this course youll learn how to create static and interactive dashboards using flexdashboard and shiny.

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

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

Learn how to prepare and organize your data for predictive analytics.

Learn to analyze and model customer choice data in R.